Robot Motion Planning

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Overview

Robot motion planning determines how a robot can move from a start state to a goal while respecting obstacles, its own geometry and dynamics, and task requirements. Classical approaches include graph search, sampling-based planners such as PRM and RRT, potential fields, and trajectory optimization or model predictive control; newer methods use learned policies and generative models to propose or refine motions. Planning is also combined with perception, feedback control, temporal-logic specifications, and task-level reasoning for settings such as manipulation, navigation, and multi-robot coordination. A central challenge is balancing speed and generalization in uncertain, dynamic environments with reliable execution and safety guarantees, especially when learned plans must transfer from simulation to physical robots.

Latest papers

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271 papers34 papers in the last four weeks, down 13% on the four weeks before. 0.2% of all new papers.

Mar 10, 2026

OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots

We naturally step sideways or lean to see around the obstacle when our view is blocked, and recover a more informative observation. Enabling robots to make the same kind of viewpoint choice is critical for human-centered operations, including search, triage, and disaster response, where cluttered environments and partial visibility frequently degrade downstream perception. However, many Next-Best-View (NBV) methods primarily optimize generic exploration or long-horizon coverage, and do not explicitly target the immediate goal of obtaining a single usable observation of a partially occluded person under real motion constraints. We present Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots (OA-NBV), an occlusion-aware NBV pipeline that autonomously selects the next traversable viewpoint to obtain a more complete view of an occluded human. OA-NBV integrates perception and motion planning by scoring candidate viewpoints using a target-centric visibility model that accounts for occlusion, target scale, and target completeness, while restricting candidates to feasible robot poses. OA-NBV achieves over 90% success rate in both simulation and real-world trials, while baseline NBV methods degrade sharply under occlusion. Beyond success rate, OA-NBV improves observation quality: compared to the strongest baseline, it increases normalized target area by at least 81% and keypoint visibility by at least 58% across settings, making it a drop-in view-selection module for diverse human-centered downstream tasks.

Boxun Hu, Chang Chang, Jiawei Ge +4

Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA · Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA · Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA +1

Active PerceptionNext-Best-View PlanningMobile RoboticsActive View SelectionRobot Motion Planning
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Mar 7, 2026

Two-Stage Path Following for Mobile Manipulators via Dimensionality-Reduced Graph Search and Numerical Optimization

Efficient path following for mobile manipulators is often hindered by high-dimensional configuration spaces and kinematic constraints. This paper presents a robust two-stage configuration planning framework that decouples the 8-DoF planning problem into a tractable 2-DoF base optimization under a yaw-fixed base planning assumption. In the first stage, the proposed approach utilizes IRM to discretize the task-space path into a multi-layer graph, where an initial feasible path is extracted via a Dijkstra-based dynamic programming approach to ensure computational efficiency and global optimality within the discretized graph. In the second stage, to overcome discrete search quantization, feasible base regions are transformed into convex hulls, enabling subsequent continuous refinement via the L-BFGS algorithm to maximize trajectory smoothness while strictly enforcing reachability constraints. Simulation results demonstrate the theoretical precision of the proposed method by achieving sub-millimeter kinematic accuracy in simulation, and physical experiments on an omnidirectional mobile manipulator further validate the framework's robustness and practical applicability.

Fuyu Guo, Yuting Mei, Yuyao Zhang +1

College of Mechanical and Vehicle Engineering, ChongQing University, Chongqing, P.R.C.

Robot Motion PlanningConstrained Motion PlanningTrajectory OptimizationGraph-Based Planning
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Mar 6, 2026

LIPP: Load-Aware Informative Path Planning with Physical Sampling

In classical Informative Path Planning (C-IPP), robots are typically modeled as mobile sensors that acquire digital measurements such as images or radiation levels. In this model, since making a measurement leaves the robot's physical state unchanged, the cost of traversing an edge remains static regardless of when it is traversed. This is a natural assumption for many missions, but does not extend to settings involving physical sample collection, where each collected sample adds mass and increases the energy cost of all subsequent motion. As a result, IPP formulations that ignore this coupling between information gain and load-dependent traversal cost can produce plans that are distance-efficient but energy-suboptimal, collecting fewer samples and less data than the energy budget would permit. In this paper, we first introduce Load-aware Informative Path Planning (LIPP), a strict generalization of C-IPP that explicitly models this coupling, with C-IPP recovered as the special case of zero sample mass. We then formulate LIPP as a Mixed-Integer Quadratic Program (MIQP) that jointly optimizes visitation location, order, and per-location sampling count under an energy budget. We further derive theoretical bounds on the path-length increase of LIPP relative to C-IPP, characterizing the trade-off for improved energy efficiency. Finally, through extensive simulations across 2,000 diverse mission scenarios, we demonstrate that LIPP progressively achieves higher uncertainty reduction per unit energy as sample mass increases.

Hojune Kim, Guangyao Shi, Gaurav S. Sukhatme

School of Electrical and Computer Engineering University of Southern California Los Angeles, California · School of Computer Science University of Southern California Los Angeles, California

Informative Path PlanningConstrained OptimizationMotion PlanningMixed-Integer QPRobot Motion Planning
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Mar 5, 2026

Environment-Aware Path Generation for Robotic Additive Manufacturing of Structures

Robotic Additive Manufacturing (AM) has emerged as a scalable and customizable construction method in the last decade. However, current AM design methods rely on pre-conceived (A priori) toolpath of the structure, often developed via offline slicing software. Moreover, considering the dynamic construction environments involving obstacles on terrestrial and extraterrestrial environments, there is a need for online path generation methods. Here, an environment-aware path generation framework (PGF) is proposed for the first time in which structures are designed in an online fashion by utilizing four path planning (PP) algorithms (two search-based and two sampling-based). To evaluate the performance of the proposed PGF in different obstacle arrangements (periodic, random) for two types of structures (closed and open), structural (path roughness, turns, offset, Root Mean Square Error (RMSE), deviation) and computational (run time) performance metrics are developed. Most challenging environments (i.e., dense with high number of obstacles) are considered to saturate the feasibility limits of PP algorithms. The capability of each of the four path planners used in the PGF in finding a feasible path is assessed. Finally, the effectiveness of the proposed structural performance metrics is evaluated individually and comparatively, and most essential metrics necessary for evaluation of toolpath of the resulting structures are prescribed. Consequently, the most promising path planners in challenging environments are identified for robotic additive manufacturing applications.

Mahsa Rabiei, Reza Moini

Civil and Environmental Engineering Department, Princeton University, Princeton, NJ, USA

Robotic ConstructionAdditive ManufacturingRobot Motion PlanningSampling-Based Motion Planning
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Mar 5, 2026

CT-Enabled Patient-Specific Simulation and Contact-Aware Robotic Planning for Cochlear Implantation

Robotic cochlear-implant (CI) insertion requires precise prediction and regulation of contact forces to minimize intracochlear trauma and prevent failure modes such as locking and buckling. Aligned with the integration of advanced medical imaging and robotics for autonomous, precision interventions, this paper presents a unified CT-to-simulation pipeline for contact-aware insertion planning and validation. We develop a low-dimensional, differentiable Cosserat-rod model of the electrode array coupled with frictional contact and pseudo-dynamics regularization to ensure continuous stick-slip transitions. Patient-specific cochlear anatomy is reconstructed from CT imaging and encoded via an analytic parametrization of the scala-tympani lumen, enabling efficient and differentiable contact queries through closest-point projection. Based on a differentiated equilibrium-constraint formulation, we derive an online direction-update law under an RCM-like constraint that suppresses lateral insertion forces while maintaining axial advancement. Simulations and benchtop experiments validate deformation and force trends, demonstrating reduced locking/buckling risk and improved insertion depth. The study highlights how CT-based imaging enhances modeling, planning, and safety capabilities in robot-assisted inner-ear procedures.

Lingxiao Xun, Gang Zheng, Alexandre Kruszewski +1

Contact-Aware Motion PlanningCosserat Rod ModelingRobotic InsertionSurgical RoboticsRobot Motion Planning
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Mar 5, 2026

Direct Contact-Tolerant Motion Planning With Vision Language Models

Navigation in cluttered environments often requires robots to tolerate contact with movable or deformable objects to maintain efficiency. Existing contact-tolerant motion planning (CTMP) methods rely on indirect spatial representations (e.g., prebuilt map, obstacle set), resulting in inaccuracies and a lack of adaptiveness to environmental uncertainties. To address this issue, we propose a direct contact-tolerant (DCT) planner, which integrates vision-language models (VLMs) into direct point perception and navigation, including two key components. The first one is VLM point cloud partitioner (VPP), which performs contact-tolerance reasoning in image space using VLM, caches inference masks, propagates them across frames using odometry, and projects them onto the current scan to generate a contact-aware point cloud. The second innovation is VPP guided navigation (VGN), which formulates CTMP as a perception-to-control optimization problem under direct contact-aware point cloud constraints, which is further solved by a specialized deep neural network (DNN). We implement DCT in Isaac Sim and a real car-like robot, demonstrating that DCT achieves robust and efficient navigation in cluttered environments with movable obstacles, outperforming representative baselines across diverse metrics. The code is available at: https://github.com/ChrisLeeUM/DCT.

He Li, Jian Sun, Chengyang Li +4

State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), University of Macau, Macau, China. · Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong, China. · Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, Shenzhen, China.

Contact-Aware Motion PlanningRobot NavigationMotion PlanningRobot Motion Planning
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Mar 4, 2026

Many-RRT*: Robust Joint-Space Trajectory Planning for Serial Manipulators

The rapid advancement of high degree-of-freedom (DoF) serial manipulators necessitates the use of swift, sampling-based motion planners for high-dimensional spaces. While sampling-based planners like the Rapidly-Exploring Random Tree (RRT) are widely used, planning in the manipulator's joint space presents significant challenges due to non-invertible forward kinematics. A single task-space end-effector pose can correspond to multiple configuration-space states, creating a multi-arm bandit problem for the planner. In complex environments, simply choosing the wrong joint space goal can result in suboptimal trajectories or even failure to find a viable plan. To address this planning problem, we propose Many-RRT*: an extension of RRT*-Connect that plans to multiple goals in parallel. By generating multiple IK solutions and growing independent trees from these goal configurations simultaneously alongside a single start tree, Many-RRT* ensures that computational effort is not wasted on suboptimal IK solutions. This approach maintains robust convergence and asymptotic optimality. Experimental evaluations across robot morphologies and diverse obstacle environments demonstrate that Many-RRT* provides higher quality trajectories (44.5% lower cost in the same runtime) with a significantly higher success rate (100% vs. the next best of 1.6%) than previous RRT iterations without compromising on runtime performance.

Theodore M. Belmont, Benjamin A. Christie, Anton Netchaev

USACE ERDC, Information Technology Lab (ERDC), Vicksburg, MS 39180, USA · Collaborative Robotics Lab (Collab), Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061

Robot Motion PlanningSampling-Based Motion Planning
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Mar 4, 2026

OmniPlanner: Universal Exploration and Inspection Path Planning Across Robot Morphologies

Autonomous robotic systems are increasingly deployed for mapping, monitoring, and inspection in complex and unstructured environments. However, most existing path planning approaches remain domain-specific (i.e., in the air, on land, or at sea), limiting their scalability and cross-platform applicability. This article presents OmniPlanner, a unified planning framework for autonomous exploration and inspection across aerial, ground, and underwater robots. The method integrates volumetric exploration and viewpoint-based inspection, alongside target reach behaviors within a single modular architecture, complemented by a platform abstraction layer that captures morphology-specific sensing, traversability and motion constraints. This enables the same planning strategy to generalize across distinct mobility domains with minimal retuning. The framework is validated through extensive simulation studies and field deployments in underground mines, industrial facilities, forests, submarine bunkers, and structured outdoor environments. Across these diverse scenarios, OmniPlanner demonstrates robust performance, consistent cross-domain generalization, and improved exploration and inspection efficiency compared to representative state-of-the-art baselines. Videos presenting the OmniPlanner framework and demonstrating its field deployments across aerial, ground, and underwater robots are available at https://ntnu-arl.github.io/omniplanner, and the source code is publicly available at https://github.com/ntnu-arl/gbplanner_ros/tree/gbplanner3.

Angelos Zacharia, Mihir Dharmadhikari, Mohit Singh +1

Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway

Robotic InspectionRobot Task PlanningRobot Motion PlanningAutonomous Robot Exploration
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Mar 3, 2026

Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints

It will be increasingly common for robots to operate in cluttered human-centered environments such as homes, workplaces, and hospitals, where the robot is often tasked to maintain perception constraints, such as monitoring people or multiple objects, for safety and reliability while executing its task. However, existing perception-aware approaches typically focus on low-degree-of-freedom (DoF) systems or only consider a single object in the context of high-DoF robots. This motivates us to consider the problem of perception-aware motion planning for high-DoF robots that accounts for multi-object monitoring constraints. We employ a scene graph representation of the environment, offering a great potential for incorporating long-horizon task and motion planning thanks to its rich semantic and spatial information. However, it does not capture perception-constrained information, such as the viewpoints the user prefers. To address these challenges, we propose MOPS-PRM, a roadmap-based motion planner, that integrates the perception cost of observing multiple objects or humans directly into motion planning for high-DoF robots. The perception cost is embedded to each object as part of a scene graph, and used to selectively sample configurations for roadmap construction, implicitly enforcing the perception constraints. Our method is extensively validated in both simulated and real-world experiments, achieving more than ~36% improvement in the average number of detected objects and ~17% better track rate against other perception-constrained baselines, with comparable planning times and path lengths.

Qingxi Meng, Emiliano Flores, Thai Duong +2

Department of Computer Science, Rice University, Houston, TX 77005 USA · the Department of Computer Science, Rice University, Houston, TX 77005 USA, and also with Ken Kennedy Institute, Rice University, Houston, TX 77005 USA.

Scene GraphsMotion PlanningRobot Motion PlanningSampling-Based Motion PlanningRoadmap-Based Motion Planning
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Mar 3, 2026

GAIDE: Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning

Sampling-based motion planning algorithms are widely used for motion planning of robotic manipulators, but they often struggle with sample inefficiency in high-dimensional configuration spaces due to their reliance on uniform or hand-crafted informed sampling primitives. Neural informed samplers address this limitation by learning the sampling distribution from prior planning experience to guide the motion planner towards planning goal. However, existing approaches often struggle to encode the spatial structure inherent in motion planning problems. To address this limitation, we introduce Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning (GAIDE), a neural informed sampler that leverages both the spatial structure of the planning problem and the robotic manipulator's embodiment to guide the planning algorithm. GAIDE represents these structures as a graph and integrates it into a transformer-based neural sampler through attention masking. We evaluate GAIDE against baseline state-of-the-art sampling-based planners using uniform sampling, hand-crafted informed sampling, and neural informed sampling primitives. Evaluation results demonstrate that GAIDE improves planning efficiency and success rate.

Davood Soleymanzadeh, Xiao Liang, Minghui Zheng

J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA · Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843 USA

Robot Motion PlanningSampling-Based Motion Planning
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Mar 3, 2026

Self-Evolutionary Replanning for Failure-Aware Motion Planning

Motion planners tuned under nominal conditions can fail when execution conditions change. Existing differentiable adaptation methods provide efficient online updates but remain constrained by static loss functions and gradient-only parameter updates. We propose Self-Evolutionary RePlanning (SERP), a failure-aware framework that jointly adapts motion-planner parameters and loss function weights from closed-loop execution feedback. SERP provides two adaptation mechanisms. First, in-context learning with auto-differentiation (ILAD) enables rapid online adaptation by coupling local numerical refinement with history-conditioned semantic intervention. Auto-differentiation performs numerical updates, while a vision-language model (VLM) interprets recent adaptation history to jointly revise planner parameters and loss weights when local adaptation becomes ineffective. Second, the memory-augmented rollout system (MARS) extends ILAD into a deliberative and evolutionary recovery process with memory-guided candidate generation and parallel low-fidelity rollout evaluation. The resulting recovery trace is then consolidated into recovery memory to guide future adaptation. Extensive simulation and real-world experiments demonstrate improved failure recovery performance over various baselines across diverse environments.

Guoliang Li, Ruihua Han, Chengyang Li +5

Department of Computer and Information Science, University of Macau · Department of Computer Science, University of Hong Kong · SIAT, Chinese Academy of Sciences +2

Motion PlanningRobot Motion Planning
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Mar 3, 2026

MMH-Planner: Multi-Mode Hybrid Trajectory Planning Method for UAV Efficient Flight Based on Real-Time Spatial Awareness

Motion planning is a critical component of intelligent unmanned systems, enabling their complex autonomous operations. However, current planning algorithms still face limitations in planning efficiency due to inflexible strategies and weak adaptability. To address this, this paper proposes a multi-mode hybrid trajectory planning method for UAVs based on real-time environmental awareness, which dynamically selects the optimal planning model for high-quality trajectory generation in response to environmental changes. First, we introduce a goal-oriented spatial awareness method that rapidly assesses flight safety in the upcoming environments. Second, a multi-mode hybrid trajectory planning mechanism is proposed, which can enhance the planning efficiency by selecting the optimal planning model for trajectory generation based on prior spatial awareness. Finally, we design a lazy replanning strategy that triggers replanning only when necessary to reduce computational resource consumption while maintaining flight quality. To validate the performance of the proposed method, we conducted comprehensive comparative experiments in simulation environments. Results demonstrate that our approach outperforms existing state-of-the-art (SOTA) algorithms across multiple metrics, achieving the best performance particularly in terms of the average number of planning iterations and computational cost per iteration. Furthermore, the effectiveness of our approach is further verified through real-world flight experiments integrated with a self-developed intelligent UAV platform.

Yinghao Zhao, Chenguang Dai, Liang Lyu +3

School of Surveying and Mapping, Information Engineering University, Zhengzhou, 450001, China · School of Geodesy and Geomatics, Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China

Motion PlanningRobot Motion Planning
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Feb 28, 2026

Online Generation of Collision-Free Trajectories in Dynamic Environments

In this paper, we present an online method for converting an arbitrary geometric path, represented by a sequence of states, and generated by any planner (e.g., sampling-based planners such as RRT or PRM, search-based planners such as ARA*, etc.), into a kinematically feasible, jerk-limited trajectory. The method generates a sequence of quintic/quartic splines that can be discretized at a user-specified control rate and streamed to a low-level robot controller. Our approach enables real-time adaptation to environmental changes and can be re-invoked at any instant to generate a new trajectory from the robot's current state to a desired target state or sequence of states. Under a bounded-obstacle-velocity assumption, the method provides conditional stopping-safety guarantees over a finite time interval in dynamic environments, while allowing bounded geometric deviation from the original path. Kinematic constraints, including jerk limits, are explicitly considered. We validate the approach in a comparative simulation study against a competing method, demonstrating favorable behavior w.r.t. smoothness, computational time, and real-time performance, particularly with frequent target-state changes (up to 1 [kHz]). Real-robot experiments demonstrate applicability in real-world scenarios, including scenarios with a human as an obstacle.

Nermin Covic, Bakir Lacevic

Faculty of Electrical Engineering, University of Sarajevo, Bosnia and Herzegovina

Constrained Motion PlanningTrajectory OptimizationTrajectory GenerationRobot Motion GenerationSafe Motion PlanningDynamic Obstacle Avoidance+1
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Feb 28, 2026

Planning Method for Skill-Based Control of Robots Using a PLC as Skill Trigger

Skill-based programming of robots provides a flexible approach for automation. Existing solutions neglect the optimization of motion sequences, leading to inefficiencies in execution. This work introduces a planning method that enhances skill-based robot programming by integrating motion sequence optimization. This optimization leads to a new MoveContinuousSkill. The software for executing the MoveContinuousSkill is implemented on a Programmable Logic Controller and applied across multiple robotic systems. Experimental results demonstrate a significant improvement in execution time through optimized motion sequence.

Andreas Gaugenrieder, Hari Hara Balasubramaniam, Jannik Möhrle +1

Fraunhofer Institute for Casting, Composite and Processing Technology IGCV · Fraunhofer Institute for Casting, Composite and Processing Technology IGCV, Am Technologiezentrum 10, 86159 Augsburg, Germany · Institute for Machine Tools and Industrial Management, Technical University of Munich +1

Robotic ControlRobot Task PlanningRobot Motion Planning
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Feb 25, 2026

Hierarchical Trajectory Planning of Floating-Base Multi-Link Robot for Maneuvering in Confined Environments

Floating-base multi-link robots can change their shape during flight, making them well-suited for applications in confined environments such as autonomous inspection and search and rescue. However, trajectory planning for such systems remains an open challenge because the problem lies in a high-dimensional, constraint-rich space where collision avoidance must be addressed together with kinematic limits and dynamic feasibility. This work introduces a hierarchical trajectory planning framework that integrates global guidance with configuration-aware local optimization. First, we exploit the dual nature of these robots - the root link as a rigid body for guidance and the articulated joints for flexibility - to generate global anchor states that decompose the planning problem into tractable segments. Second, we design a local trajectory planner that optimizes each segment in parallel with differentiable objectives and constraints, systematically enforcing kinematic feasibility and maintaining dynamic feasibility by avoiding control singularities. Third, we implement a complete system that directly processes point-cloud data, eliminating the need for handcrafted obstacle models. Extensive simulations and real-world experiments confirm that this framework enables an articulated aerial robot to exploit its morphology for maneuvering that rigid robots cannot achieve. To the best of our knowledge, this is the first planning framework for floating-base multi-link robots that has been demonstrated on a real robot to generate continuous, collision-free, and dynamically feasible trajectories directly from raw point-cloud inputs, without relying on handcrafted obstacle models.

Yicheng Chen, Jinjie Li, Haokun Liu +3

Department of Mechanical Engineering, The University of Tokyo, Tokyo 113-8656, Japan

Robot Motion PlanningMotion PlanningConstrained Motion PlanningTrajectory OptimizationAerial Robotics
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Feb 23, 2026

NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning

Solving complex long-horizon robotic tasks requires joint reasoning over abstract task structure and low-level physical interaction. While combining Vision-Language Models (VLMs) and video generation models offers a promising path for zero-shot planning, their individual tendencies to hallucinate physics or violate geometric consistency often compound over time, preventing reliable real-world execution. We introduce NovaPlan, a hierarchical framework that enables robust, zero-shot long-horizon manipulation by systematically proposing, verifying, and repairing visual plans. At the high level, a VLM planner decomposes tasks and filters out dynamically inconsistent futures by verifying multiple candidate video rollouts. To translate these imagined futures into reliable physical actions, NovaPlan utilizes a hybrid geometric representation that adaptively switches between object-centric flow and human hand flow. Finally, NovaPlan closes the loop by continuously monitoring execution to verify outcomes and synthesize local, non-prehensile corrective behaviors, such as fingertip poking, when failures occur. Across diverse multi-stage tasks, NovaPlan substantially outperforms prior zero-shot systems, achieving complex assembly and dexterous error recovery entirely without task-specific training or demonstrations. Please visit our project website for additional results: https://nova-plan.github.io/

Jiahui Fu, Junyu Nan, Lingfeng Sun +7

Robotics and AI Institute · Carnegie Mellon University · Brown University +1

Long-Horizon Robotic ManipulationZero-Shot Robotic ManipulationRobot Task PlanningRobot Motion PlanningVLMs for Robotics
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Feb 23, 2026

Hilbert-Augmented Reinforcement Learning for Scalable Multi-Robot Coverage and Exploration

We present a coverage framework that integrates Hilbert space-filling priors into decentralized multi-robot learning and execution. We augment DQN and PPO with Hilbert-based spatial indices to structure exploration and reduce redundancy in sparse-reward environments, and we evaluate scalability in multi-robot grid coverage. We further describe a waypoint interface that converts Hilbert orderings into curvature-bounded, time-parameterized SE(2) trajectories (planar (x, y, θ)), enabling onboard feasibility on resource-constrained robots. Experiments show improvements in coverage efficiency, redundancy, and convergence speed over DQN/PPO baselines. In addition, we validate the approach on a Boston Dynamics Spot legged robot, executing the generated trajectories in indoor environments and observing reliable coverage with low redundancy. These results indicate that geometric priors improve autonomy and scalability for swarm and legged robotics.

Tamil Selvan Gurunathan, Aryya Gangopadhyay

Multi-Robot ExplorationMulti-Robot SystemsReinforcement LearningDecentralized MARLRobot Motion Planning
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Feb 22, 2026

Path planning for unmanned surface vehicle based on predictive artificial potential field. International Journal of Advanced Robotic Systems

Path planning for high-speed unmanned surface vehicles requires more complex solutions to reduce sailing time and save energy. This article proposes a new predictive artificial potential field that incorporates time information and predictive potential to plan smoother paths. It explores the principles of the artificial potential field, considering vehicle dynamics and local minimum reachability. The study first analyzes the most advanced traditional artificial potential field and its drawbacks in global and local path planning. It then introduces three modifications to the predictive artificial potential field-angle limit, velocity adjustment, and predictive potential to enhance the feasibility and flatness of the generated path. A comparison between the traditional and predictive artificial potential fields demonstrates that the latter successfully restricts the maximum turning angle, shortens sailing time, and intelligently avoids obstacles. Simulation results further verify that the predictive artificial potential field addresses the concave local minimum problem and improves reachability in special scenarios, ultimately generating a more efficient path that reduces sailing time and conserves energy for unmanned surface vehicles.

Jia Song, Ce Hao, Jiangcheng Su

School of Astronautics, Beihang University, Beijing, China · Department of Mechanical Engineering, University of California, Berkeley, CA, USA

Motion PlanningRobot Motion PlanningRobot Collision Avoidance
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Feb 21, 2026

Temporal-Logic-Aware Frontier-Based Exploration

This paper addresses the problem of temporal logic motion planning for an autonomous robot operating in an unknown environment. The objective is to enable the robot to satisfy a syntactically co-safe Linear Temporal Logic (scLTL) specification when the exact locations of the desired labels are not known a priori. We introduce a new type of automaton state, referred to as commit states. These states capture intermediate task progress resulting from actions whose consequences are irreversible. In other words, certain future paths to satisfaction become not feasible after taking those actions that lead to the commit states. By leveraging commit states, we propose a sound and complete frontier-based exploration algorithm that strategically guides the robot to make progress toward the task while preserving all possible ways of satisfying it. The efficacy of the proposed method is validated through simulations.

Azizollah Taheri, Derya Aksaray

Department of Electrical and Computer Engineering at Northeastern University.

STL Motion PlanningMotion PlanningRobot Motion PlanningLinear Temporal LogicFrontier-Based Exploration
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Feb 9, 2026

From Legible to Inscrutable Trajectories: (Il)legible Motion Planning Accounting for Multiple Observers

In cooperative environments, such as in factories or assistive scenarios, it is important for a robot to communicate its intentions to observers, who could be either other humans or robots. A legible trajectory allows an observer to quickly and accurately predict an agent's intention. In adversarial environments, such as in military operations or games, it is important for a robot to not communicate its intentions to observers. An illegible trajectory leads an observer to incorrectly predict the agent's intention or delays when an observer is able to make a correct prediction about the agent's intention. However, in some environments there are multiple observers, each of whom may be able to see only part of the environment, and each of whom may have different motives. In this work, we introduce the Mixed-Motive Limited-Observability Legible Motion Planning (MMLO-LMP) problem, which requires a motion planner to generate a trajectory that is legible to observers with positive motives and illegible to observers with negative motives while also considering the visibility limitations of each observer. We highlight multiple strategies an agent can take while still achieving the problem objective. We also present DUBIOUS, a trajectory optimizer that solves MMLO-LMP. Our results show that DUBIOUS can generate trajectories that balance legibility with the motives and limited visibility regions of the observers. Future work includes many variations of MMLO-LMP, including moving observers and observer teaming.

Ananya Yammanuru, Maria Lusardi, Nancy M. Amato +1

University of Illinois at Urbana-Champaign, Urbana IL 61801, USA

Trajectory OptimizationTrajectory GenerationMotion PlanningRobot Motion Planning
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Jan 23, 2026

CARE: Consistent Action-Response Execution for Human-Guided Robot Transport

Human-guided collaborative transport becomes interaction-critical when the operator can continue along a route segment that is not executable by the robot--payload system. In this regime, operator motion is not an exogenous reference: it simultaneously communicates a persistent route tendency and adapts to the robot's realized progress. A common modular formulation predicts one operator future from observation history, scores multiple robot proposals against that shared forecast, and then modifies the selected command for local feasibility. This can compare candidates under responses they would not induce and can condition prediction on an action that is never executed. We formulate local control over executable-action-indexed joint human--robot transitions and instantiate it in Consistent Action--Response Execution (CARE). Each nominal proposal or bounded alternative is first mapped to an executable action; that same action conditions the route-aligned operator-response prediction, the matched robot--operator rollout, and a fixed interaction utility for candidate selection. Bounded reciprocal refinement handles transient route--feasibility mismatch, whereas persistent progress loss replaces the nominal proposal with a progress-oriented recovery proposal while retaining the same downstream executable-action-conditioned evaluation. Across training and held-out maps, CARE preserves near-saturated performance when the indicated route remains locally usable and achieves the highest observed success and service availability when the operator can continue but the robot must detour. It also yields lower jerk-proxy values and measured controller latency than dynamic-window and Timed Elastic Band baselines. Factorial ablations distinguish reciprocal refinement from progress recovery, delayed-response tests probe temporal mismatch, and an indoor deployment demonstrates physical executability.

Ning Liu, Sen Shen, Zheng Li +4

School of Engineering, The University of Western Australia, 35 Stirling Highway, Perth, 6009, WA, Australia · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR · School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, 4072, QLD, Australia

Human-Robot CollaborationShared AutonomyRobot Motion PlanningRobot Teleoperation
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Jan 19, 2026

RPT*: Global Planning with Probabilistic Terminals for Target Search in Complex Environments

Routing problems such as Hamiltonian Path Problem (HPP), seek a path to visit all the vertices in a graph while minimizing the path cost. This paper studies a variant, HPP with Probabilistic Terminals (HPP-PT), where each vertex has a probability representing the likelihood that the robot's path terminates there, and the objective is to minimize the expected path cost. HPP-PT arises in target object search, where a mobile robot must visit all candidate locations to find an object, and prior knowledge of the object's location is expressed as vertex probabilities. While routing problems have been studied for decades, few of them consider uncertainty as required in this work. The challenge lies not only in optimality ordering the vertices, as in standard HPP, but also in handling history dependency: the expected path cost depends on the order in which vertices were previously visited. This makes many existing methods inefficient or inapplicable. To address the challenge, we propose a search-based approach RPT* with solution optimality guarantees, which leverages dynamic programming in a new state space to bypass the history dependency and novel heuristics to speed up the computation. Building on RPT*, we design a Hierarchical Autonomous Target Search (HATS) system that combines RPT* with either Bayesian filtering for lifelong target search with noisy sensors, or autonomous exploration to find targets in unknown environments. Experiments in both simulation and real robot show that our approach can naturally balance between exploitation and exploration, thereby finding targets more quickly on average than baseline methods.

Yunpeng Lyu, Chao Cao, Ji Zhang +2

Global College, Shanghai Jiao Tong University. · Robotics Institute, Carnegie Mellon University.

Robot Motion Planning
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Nov 12, 2025

A Shared Control Framework for Mobile Robots with Planning-Level Intention Prediction

In mobile robot shared control, effectively understanding human motion intention is critical for seamless human-robot collaboration. This paper presents a novel shared control framework featuring planning-level intention prediction. A path replanning algorithm is designed to adjust the robot's desired trajectory according to inferred human intentions. To represent future motion intentions, we introduce the concept of an intention domain, which serves as a constraint for path replanning. The intention-domain prediction and path replanning problems are jointly formulated as a Markov Decision Process and solved through deep reinforcement learning. In addition, a Voronoi-based human trajectory generation algorithm is developed, allowing the model to be trained entirely in simulation without human participation or demonstration data. Extensive simulations and real-world user studies demonstrate that the proposed method significantly reduces operator workload and enhances safety, without compromising task efficiency compared with existing assistive teleoperation approaches.

Jinyu Zhang, Lijun Han, Feng Jian +2

Mobile RoboticsHuman-Robot CollaborationRobot Motion Planning
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Oct 30, 2025

ART-TEB: Adaptive Trajectory Planning for Mobile Robots in Cluttered Environments

Trajectory planning for mobile robots remains a major challenge, particularly in cluttered environments, where existing planning algorithms often fail or produce suboptimal paths. To address this issue, we propose an adaptive trajectory refinement algorithm, ART-TEB, comprising two main stages. First, to ensure safety at the path-segment level, a segment-wise conservative collision test is applied, recursively subdividing risky path segments until the collision risk is eliminated. Second, to guarantee pose-level safety, pose correction based on separation direction and line search is applied, ensuring that each pose in the trajectory is collision-free and maximally clear from obstacles. Simulation results demonstrate that the ART-TEB achieves up to 3.12x higher success rates and up to 23.7x faster average planning times than state-of-the-art approaches. Furthermore, real-world experiments confirm that the robot can safely pass through highly constrained environments while maintaining rapid planning performance.

Hahjin Lee, Young J. Kim

Department of Computer Science and Engineering at Ewha Womans University in Korea

Trajectory OptimizationTrajectory GenerationMobile RoboticsSafe Motion PlanningRobot Motion Planning
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Oct 16, 2025

STITCHER: Constrained Trajectory Planning in Complex Environments with Real-Time Motion Primitive Search

Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy state or actuator constraints. Modern trajectory planning techniques primarily use numerical optimization, as they enable the systematic computation of high-quality, expressive trajectories that satisfy various constraints. However, stringent requirements on computation time and the risk of numerical instability can limit the use of optimization-based planners in safety-critical scenarios. This work presents an optimization-free planning framework called STITCHER that stitches short trajectory segments together with graph search to compute long-range, expressive, and near-optimal trajectories in real-time. STITCHER outperforms modern optimization-based planners through our innovative planning architecture and several algorithmic developments that make real-time planning possible. Extensive simulation testing is performed to analyze the algorithmic components that make up STITCHER, along with a thorough comparison with three state-of-the-art optimization planners. Simulation tests show that safe trajectories can be created within a few milliseconds for paths that span the entirety of two 50 m x 50 m environments. Hardware tests with a custom quadrotor verify that STITCHER can produce trackable paths in real-time while respecting nonconvex constraints, such as limits on tilt angle and motor forces, with flight speeds up to 63 km/h.

Helene J. Levy, Brett T. Lopez

VECTR Laboratory, University of California, Los Angeles, Los Angeles, CA, USA

Constrained Motion PlanningTrajectory GenerationUAV NavigationGraph-Based PlanningSafe Motion PlanningMotion Planning+1
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Oct 2, 2025

ERUPT: An Open Toolkit for Interfacing with Robot Motion Planners in Extended Reality

We present the Extended Reality Universal Planning Toolkit (ERUPT), an extended reality (XR) system for interactive motion planning. This paper serves to introduce our open-source system to others who can use it as a base to develop immersive robot interaction applications. Our system allows users to create and dynamically reconfigure environments while they plan robot paths. ERUPT uses XR to provide a broad range of natural interaction capabilities, allowing users to grab and adjust objects in the environment similar to interaction in the real world, rather than using a mouse and keyboard with the scene projected onto a 2D computer screen. Our system integrates with MoveIt, a manipulation planning framework, allowing users to send motion planning requests and visualize the resulting robot paths in virtual or augmented reality. We provide a broad range of interaction modalities, allowing users to modify objects in the environment and interact with a virtual robot. Our system allows operators to visualize robot motions, ensuring desired behavior as it moves throughout the environment, without risk of collisions within a virtual space, and to then deploy planned paths on physical robots in the real world. Code can be found at https://github.com/parasollab/erupt.

Isaac Ngui, Courtney McBeth, André Santos +6

Parasol Lab, Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Champaign, IL 61820 USA · Insper, São Paulo, Brazil · Department of Computer Science at Instituto Tecnológico Autónomo de México (ITAM), Mexico City, Mexico

RoboticsHuman-Robot InteractionRobot Motion Planning
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Sep 8, 2025

First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines

Autonomous multi-finger grasping is a fundamental capability in robotic manipulation. Optimization-based approaches show strong performance, but tend to be sensitive to initialization and are potentially time-consuming. As an alternative, the generator-evaluator-planner framework has been proposed. A generator generates grasp candidates, an evaluator ranks the proposed grasps, and a motion planner plans a trajectory to the highest-ranked grasp. If the planner doesn't find a trajectory, a new trajectory optimization is started with the next-best grasp as the target and so on. However, executing lower-ranked grasps means a lower chance of grasp success, and multiple trajectory optimizations are time-consuming. Alternatively, relaxing the threshold for motion planning accuracy allows for easier computation of a successful trajectory but implies lower accuracy in estimating grasp success likelihood. It's a lose-lose proposition: either spend more time finding a successful trajectory or have a worse estimate of grasp success. We propose a framework that plans trajectories to a set of generated grasp targets, the evaluator estimates the grasp success likelihood at the terminal configuration of the planned trajectories, and the robot executes the trajectory most likely to succeed. Our experiments show our approach improves over the traditional generator-evaluator-planner framework across different objects, generators, and motion planners, and successfully generalizes to novel environments in the real world, including different shelves and table heights. Project Website: https://martinmatak.github.io/fpte/

Martin Matak, Mohanraj Devendran Shanthi, Karl Van Wyk +1

Kahlert School of Computing and the Robotics Center, University of Utah. · NVIDIA, USA.

Robotic GraspingRobot Motion Planning
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Sep 5, 2025

Real-time autonomous magnetic microrobot navigation across dynamic and biologically relevant environments

Autonomous microrobots could enable minimally invasive interventions in confined biological environments, but their operation requires real-time navigation among moving obstacles and environmental disturbances. Here we present a closed-loop framework for autonomous magnetic microrobot navigation that separates long-range geometric planning from short-range reactive control. An analytic geometry-based planner continuously generates collision-free global routes from microscopy images, while rule-based or reinforcement-learning local controllers respond to moving obstacles before returning control to the replanned global route. The planner produces short paths with substantially lower computation time than representative graph-, sampling- and geometry-based planners, and the integrated framework operates within the video-rate control budget. We demonstrate microrobot autonomous navigation in static and dynamic microfluidic environments, including avoidance of moving particles and human red blood cells, navigation with and against fluid flow, and repeated long-duration target transitions. We further demonstrate magnetic microrobot actuation in ex vivo ocular and vascularized cortical tissues and photoacoustic detection of magnetically induced microrobot dynamics in post-mortem tissue. These results establish a modular framework for autonomous magnetic microrobot navigation in complex biological environments.

Yanda Yang, Max Sokolich, Fatma Ceren Kirmizitas +3

Department of Mechanical Engineering, University of Delaware, Newark 19716, USA. · Department of Animal and Food Sciences, University of Delaware, Newark 19716, USA. · School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA.

Robot NavigationRoboticsDynamic Obstacle AvoidanceRobot Motion Planning
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Jul 7, 2025

Monte Carlo Tree Search with Tensor Factorization for Optimization Problems in Robotics

Many robotic tasks, such as inverse kinematics, motion planning, and contact-rich manipulation, can be formulated as optimization problems. Solving these problems requires addressing inherent nonlinear kinematics, complex contact dynamics, long-horizon correlations, and multi-modal optimization landscapes, each posing distinct challenges for state-of-the-art optimizers. While existing methods tackle these issues through problem-specific strategies, such specialization inherently limits cross-task generalization, requires heavy engineering effort in problem reformulation, and hinders multi-task autonomy. Monte Carlo Tree Search (MCTS) offers a compelling framework that generalizes across diverse robotic tasks via strategic exploration of the solution space. However, it typically suffers from combinatorial complexity when applied naively, resulting in slow convergence and excessive storage space in high-dimensional domains. To address this limitation, we propose Tensor Train Tree Search (TTTS), which leverages tensor factorization to exploit implicit correlations among different branches within the decision tree. By utilizing the resulting compact, linear-complexity representation, TTTS significantly reduces both computation and storage overhead, thereby enabling highly efficient global decision making. Experimental results across inverse kinematics, motion planning around obstacles, legged robot manipulation, multi-stage motion planning, and bimanual whole-body manipulation demonstrate the efficiency of TTTS for generalized robot optimization over a diverse set of tasks.

Teng Xue, Yan Zhang, Amirreza Razmjoo +1

Idiap Research Institute, Martigny, Switzerland · École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Monte Carlo Tree SearchTT DecompositionRobotic ManipulationRobotic OptimizationRobot Motion Planning
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Jun 14, 2025

AntiGrounding: Executable Robot Trajectories as Visual Prompts for VLM-Guided Manipulation

Natural-language manipulation instructions specify the task goal but leave the underlying robot trajectory unspecified. We present AntiGrounding, a visual action-selection framework built around a dual geometric-visual trajectory interface. After feasibility filtering, each retained short trajectory is both an explicit motion plan for execution and a rendered prompt for instruction-conditioned vision-language model (VLM) evaluation. Structured multi-view visual question answering (VQA) scores safety, task alignment, efficiency, and physical plausibility; weighted view fusion aggregates the trajectory scores. These scores guide subsequent translational trajectory proposals; separate orientation and gripper controls coordinate interaction. An initialized digital twin provides the planning state and validates selected segments before the real robot executes the same waypoint sequences. Across eight real-world manipulation tasks, AntiGrounding with a single GPT-6 Astra evaluator achieves 71.25% overall success, compared with 50.00% for pi0.5 and 47.50% for a PIVOT-style visual proposal-selection baseline using the same evaluator under the reported deployment protocol. Component ablations and evaluator-sensitivity analyses examine trajectory evaluation, proposal search, orientation control, and evaluator choice. The interface connects general-purpose multimodal reasoning to executable trajectories, with performance bounded by digital-twin fidelity and physical interaction.

Wenbo Li, Yiteng Chen, Wenhao Li +1

School of Software Engineering, South China University of Technology

Trajectory GenerationLanguage-Conditioned Robot ManipulationVisually Grounded ReasoningRobot Motion PlanningVLMs for Robotics
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