Robot Navigation

Ask about this topic

Overview

Robot navigation combines sensing and localization with route planning and motion control, from indoor mobile robots and legged platforms to drones, vehicles, and medical devices. Common approaches include SLAM and geometric planners such as A* and model-predictive control, alongside reinforcement learning, imitation learning, and vision-language policies that use semantic maps, memory, or predicted futures. Methods are tested in simulation and on physical robots, with benchmarks such as Habitat HM3D and MP3D for object navigation and R2R-CE for language-guided routes. A central challenge is making long-horizon decisions that remain safe and efficient when perception is uncertain, environments change, and policies transfer from simulation to real robots.

Latest papers

CardsList

557 papers103 papers in the last four weeks, up 29% on the four weeks before. 0.6% of all new papers.

May 28, 2026

EXACT-MPPI: Exact Signed-Distance Navigation for Arbitrary-Footprint Robots from Point Clouds via Path Integral Control

Ground robots often carry payloads, implements, or other attachments that turn their effective footprint into complex, non-convex shapes. Navigating safely through clutter then requires reasoning about this true geometry, yet most local planners simplify it with convex or inflated proxies and rasterize sensor data into occupancy grids or distance fields. Both choices eliminate feasible motions when clearance is comparable to the footprint geometry. We present EXACT-MPPI, a training-free local navigation framework that maps local point-cloud observations and sparse guidance directly to motion commands, without any intermediate map representation. The framework embeds an analytic, exact signed-distance evaluator into a Model Predictive Path Integral (MPPI) controller. The footprint is represented as a simple polygon for general convex or concave planar shapes, with a rectangle-cover specialization for faster evaluation of rectilinear footprints, enabling footprint-aware collision costs without convex decomposition, inflation, or learned encoders. During each MPPI rollout, observed obstacle points are transformed into the predicted body frame and evaluated against the footprint. All operations are batched in JAX, leveraging GPU parallelism for real-time receding-horizon control. Experiments show that EXACT-MPPI accelerates batched distance evaluation over a learned point-to-robot baseline, preserves feasible motion where convex-footprint planners fail, and remains robust under dense static and moving obstacles. The same framework deploys on differential-drive, Ackermann, omnidirectional, and hybrid-mode platforms by changing only the footprint description and motion model without per-platform training. Pairing exact footprint geometry with sampling-based predictive control thus offers a practical, training-free path to footprint-aware local navigation across diverse robots.

Chen Peng, Zhikang Ge, Wenwu Lu +3

ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China · College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China · Department of Biological and Agricultural Engineering, University of California, Davis, Davis, California, USA

Robot NavigationRobot Motion PlanningSampling-Based Motion Planning
AskPDF
May 27, 2026

Ontology-Guided Reasoning for Affordance-Based Explanations of Robot Navigation

This paper proposes ontology-guided reasoning for affordance-based explanations of robot navigation. In human environments, it is not sufficient for a robot to detect that its route is blocked. It must also reason about what nearby objects afford, which state changes are possible, and which of these changes would allow it to continue safely. We address this problem by representing nearby entities, their affordances, affordance states, and qualitative spatial relations in a local affordance ontology and by evaluating hypothetical object--affordance state changes as candidate explanation factors. This yields explanations that are not only semantically grounded but also actionable. We instantiate the approach in a lightweight benchmark centered on a robot librarian scenario and evaluate it on procedurally generated navigation cases. The results show that ontology-guided reasoning identifies relevant explanation factors more accurately than a semantic-only baseline and remains robust as semantic clutter increases. Overall, the paper argues that affordance ontologies can serve not merely as semantic descriptions of the environment, but as reasoning foundations for explainability and reliable robot autonomy.

Amar Halilovic, Vahidin Hasic, Senka Krivic

Institute of Artificial Intelligence, Ulm University, 89081 Ulm, Germany · Faculty of Electrical Engineering, University of Sarajevo, 71000 Sarajevo, BiH

Ontology-Based ReasoningRobot NavigationSafe Robot Navigation
AskPDF
May 27, 2026

Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments

Self-supervised online traversability estimation enables robots to continuously learn from unlabeled open-world experiences and adapt their navigation behavior toward safe and efficient trajectories. Existing approaches either rely on handcrafted proprioceptive traversability scores, limiting robot-agnosticism, or cluster prior data, preventing online learning. Moreover, many continual learning methods incur substantial memory and computational costs, hindering onboard deployment. We introduce COTRATE, an online learning framework for continuous traversability estimation from multimodal, unlabeled robot experience. Our method first infers robust traversability scores using a robot-agnostic, learning-based online terrain assessment module operating on proprioceptiveand inertial signals. These scores then supervise a visual traversability network through a novel alignment loss that associates visual embeddings with online terrain assessments. To mitigate forgetting during continual learning with minimal overhead, we propose a diversity-aware feature selection strategythat preserves performance using a compact replay memory. We further show that the learned traversability representation supports knowledge transfer across different robot platforms with different locomotion kinematics. We evaluate COTRATE on a dataset of ≈\approx 50,000 images collected with two robotic platforms across 11 outdoor terrains, and benchmark it on navigation tasks in three representative outdoor environments. We make the dataset, code, and trained models publicly available.

Julia Hindel, Simon Bultmann, Houman Masnavi +2

Department of Computer Science, University of Freiburg, Germany.

Continual Robot LearningRobot Traversability EstimationRobot NavigationContinual LearningRobotic Perception
AskPDF
May 27, 2026

STR Robot: Design of an Autonomous Mobile Robot from Simulation to Reality

With the rapid development of simulation tools, the development and validation of autonomous robotic systems have become more efficient before real-world deployment. This paper presents a simulation-to-real implementation of an autonomous mobile robot based on an existing mechanical platform. Instead of focusing on mechanical design, our work concentrates on the development of the onboard control, self-localization, and autonomous navigation system. The proposed robot is equipped with onboard sensing and computation to estimate its pose and navigate autonomously in the environment. The overall framework is first developed and tested in simulation, and then deployed on the real robot for experimental evaluation. The results demonstrate the feasibility of the proposed approach and show that simulation provides an effective foundation for developing reliable autonomous mobile robot systems. The source code will be released at https://ntdathp.github.io/outdoor-robot-web.

Vinh Nguyen, Gia-Uy Le, Tien-Dat Nguyen +2

Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology, VNU-HCM Ho Chi Minh City, Vietnam

Robotic ControlMobile RoboticsRobot Navigation
AskPDF
May 26, 2026

Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation

Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environments it has never seen. The dominant approach has been to scale vision-language-action (VLA) foundation models on ever-larger collections of robot trajectories. This paper argues that, for navigation specifically, generality can be obtained structurally, not only through data scale. The underlying decision structure of navigation reduces to a single Language-Vision-Robot Actions Translation. The language action emits semantic-level directional command and the vision action emits a pixel-level visual target. Both outputs lie inside the natural output manifold of pretrained multimodal large language models (MLLMs), so the task can be reasoned about by an agent rather than learned from robot data. Therefore, we present Uni-LaViRA, a unified agentic architecture that extends the same insight to four task families (VLN-CE, ObjectNav, EQA, and Aerial-VLN) and to four heterogeneous real robots (Wheeled, Quadruped, Humanoid robot, and a self-built UAV) in a zero-shot manner. Two agent-loop mechanisms make this unification practical. TODO List Memory (TDM) rewrites a structured checklist of pending sub-goals at every step, reciting the unfinished items back into the agent's most recent attention window. Second Chance Backtrack (SCB) rolls the robot back to the pre-error state and conditions the agent's next plan on the failed sub-trajectory, turning single-pass navigation into a self-correcting process. With zero training effort, Uni-LaViRA reaches 60.7% SR on VLN-CE R2R, 51.3% on VLN-CE RxR, 77.7% on HM3D-v2, 60.0% on HM3D-OVON, 54.7% on MP3D-EQA, and 40.0% on OpenUAV, matching or even surpassing recent training navigation foundation models that consume millions of samples and thousands of GPU-hours.

Hongyu Ding, Sizhuo Zhang, Ziming Xu +13

Nanjing University, Nanjing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China · Beihang University, Beijing, China +2

Large Language Model-Based Robot PlanningRobot NavigationVision-Language NavigationMultimodal AgentsVisual Robot Navigation
AskPDF
May 26, 2026

Resolving Conflicts Where and When They Arise: Reactive Composition of Multi-Goal Behavior

Multi-goal robotic tasks are commonly delegated to planning, because reactive control is prone to local minima when objectives conflict. We show that many such failures stem from static goal representations, not task complexity: the designer defines the objectives, but where and when they must be traded off follows from the world's interaction structure which changes with the state. To read this structure, we extend Active InterCONnect (AICON), a graph of recursive estimators coupled by differentiable interconnections, with adaptive nullspace projections: lower-priority gradients enter the nullspace of higher-priority ones wherever they meet along the graph, not only in action space, ordered by current gradient magnitudes, not a fixed hierarchy. Where two gradients oppose and no weighting of them makes progress, the system instead explores in the nullspace of the stronger one until the conflict dissolves. We robustly solve 100 non-convex navigation and 100 pushT problems, outperforming static potential fields, a diffusion policy, and the same method without projection. Unmodified but embedded in a larger graph, it absorbs perceptual uncertainty, joint limits, and self-collisions on a real robot, solving 49 of 50 pushing trials across varied objects, with active camera control and disturbance recovery emerging from the same coupling. Much of the behavior attributed to planning may thus be within reach of control, once goal representations compose from the conflicts they encounter.

Vito Mengers, Oliver Brock

Robotics and Biology Laboratory, Technische Universität Berlin · Science of Intelligence (SCIoI), Cluster of Excellence, Berlin, Germany · Robotics Institute Germany

Robotic ControlRobot NavigationRobotic Object Pushing
AskPDF
May 26, 2026

Credibility-Aware Learning and Control for Safe USV Navigation under Perception Uncertainty

Safe navigation for Unmanned Surface Vehicles (USVs) under the International Regulations for Preventing Collisions at Sea (COLREGs) remains challenging in dynamic maritime environments, especially when perception uncertainty is miscalibrated. Errors in state estimation can produce unreliable belief states that mislead value learning, while logic based on discrete traffic rules can cause abrupt action corrections. To address these challenges, we integrate Credibility-Weighted Value Learning (CWVL) with Covariance- and Recovery-Aware Control Barrier Function Quadratic Programming (CoReCBF-QP). CWVL derives a dynamic trust factor from the discrepancy between the covariance estimated by the filter and empirical error statistics. This factor modulates the critic's heteroscedastic loss and limits overfitting to miscalibrated observations. CoReCBF expands the collision geometry according to uncertainty and incorporates terms for braking and turning recovery. The resulting hyperbolic safety boundary preserves feasible avoidance velocities and supplies the QP safety constraint. A continuous COLREGs-aware reference in the objective promotes starboard maneuvers in Rule 14 head-on and Rule 15 give-way crossing encounters. Simulations show improved robustness in collision avoidance and COLREGs event compliance, achieving an 82.0% success rate with ten target ships beyond the training range.

Yuhang Zhang, Shuqi Chai, Yukang Zhang +5

School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China · Shenzhen Research Institute of Big Data, Shenzhen 518172, China · Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China +5

Collision AvoidanceRobot NavigationControl Barrier FunctionsSafe RLRobust RLRobot Collision Avoidance
AskPDF
May 26, 2026

Look Further: Socially-Compliant Navigation System in Residential Buildings

The distance at which a mobile robot reacts to a person strongly impacts various qualities of the human-robot interaction. In this paper, we focus on the navigation of a mobile delivery robot platform in a residential indoor hallway environment. Social navigation methods typically focus on avoiding uncomfortable human-robot interactions, such as when a robot encroaches on someone's personal space. Since personal space has been shown to be in the range of just a few meters, social navigation methods typically focus on deconflicting and resolving these short-range interactions. In this work, however, we demonstrate that by extending the reaction distance to over eight meters, far beyond the typical interaction distance, we can improve the human's perception of the robot's motion. We introduce the Proactive Lane-Changing (PLC) motion pattern and a navigation system that leverages it to react to people at an increased distance. This pattern consists of changing the robot's lateral position as it navigates down the hallway from the center to the side at an eight-meter distance from an oncoming person. We conducted a user study with 42 participants to assess their impressions of the delivery robot based on three service objectives: safety, smoothness, and politeness. In the straight hallway scenario (Frontal Approach), results showed significant improvement in each of these three objectives compared to typical motion patterns found in the literature: slowing down, stopping, and reactive collision avoidance in the proximity of a person. In contrast, in the intersection (Blind Corner) scenarios, none of the approaches performed significantly better than any other, with participants having a diverse range of preferences among robot motion patterns.

Akira Shiba, Marina Obata, Nathan Kau +4

Woven by Toyota, Inc., Japan

Robot NavigationSocial Robot NavigationSocial NavigationHuman-Robot InteractionIndoor Robot Navigation
AskPDF
May 25, 2026

NightSight: Passive Computation for Navigation in Dark Using Events

Small aerial robots are particularly well-suited for search and rescue in confined and hazardous environments due to their agility, low cost, and ability to traverse through cluttered spaces that are inaccessible to larger platforms. However, enabling autonomous navigation in complete darkness remains a significant challenge, because small aerial robots cannot easily accommodate perception systems that demand substantial payload, power, or computation. In this work, we present a lightweight perception approach that combines a monocular event camera, a coded aperture lens, and an infrared dot projector to enable navigation in such conditions. The projected pattern, when imaged through the coded aperture, produces depth dependent blur signatures that implicitly encode scene geometry. We train a convolutional neural network to decode these signatures into dense depth maps using only synthetic data generated from a simple planar wall setup. Despite this minimal training regime, the model generalizes zero-shot to complex real-world scenes. Our system operates in real time at 20 Hz on a NVIDIA Jetson Orin Nano, demonstrating suitability for resource-constrained platforms. We further analyze the impact of different coded aperture designs on depth estimation performance. Our approach gives high accuracy (l1 error 7.0cm) upto 2.5m range (2.80% error). These results highlight the potential of combining structured illumination, coded optics, and event-based sensing for enabling robust perception and navigation in complete darkness.

Deepak Singh, Brijan Vaghasiya, Shreyas Khobragade +1

Perception and Autonomous Robotics (PeAR) Group Worcester Polytechnic Institute Worcester, USA

Event-Based VisionDepth EstimationRobot NavigationUAV Navigation
AskPDF
May 25, 2026

Collaborative Navigation and Exploration with ββ-Sparse Gaussian Processes

Collaborative navigation of heterogeneous robots in unknown environments poses significant challenges due to sensing, communication, and computational limitations. In this work, a lead robot navigates toward a target while a mobile sensor robot (e.g., a drone) assists by transmitting information about its locally observed map under bandwidth constraints. We propose a framework that enables the sensor to jointly select its transmitted map points and navigation actions online, while also predicting unexplored regions of the environment. To this end, we present ββ-Sparse Gaussian Processes, a robust variational sparse Gaussian Process model for task-aware inducing point selection under cardinality constraints. Furthermore, we develop an action-selection strategy that balances task relevance with exploration. Simulations on Mars and Earth maps show that the framework can reduce path cost by 18% relative to no communication and decrease transmitted information by 76% compared to raw-data transmission baselines.

Evangelos Psomiadis, Dipankar Maity, Panagiotis Tsiotras

D. Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA, USA · Department of Electrical and Computer Engineering, UNC Charlotte, Charlotte, NC, USA

Multi-Robot SystemsRobot NavigationInformative Path PlanningMulti-Robot ExplorationGaussian ProcessesMulti-Robot Coordination+2
AskPDF
May 25, 2026

G-DRAGON: Geospatial Reasoning and Dynamic Planning for Retrieval-Augmented Outdoor Navigation

Autonomous ground robots operating in large-scale outdoor environments require both robust long-range navigation and fine-grained ''last-mile'' exploration. Current advances in visual-language navigation (VLN) work well at short-range tasks, lacking geospatial grounding for long-distance missions. Some OpenStreetMap (OSM)-based methods relying on cloud-based Large Language Models (LLMs) are prone to factual hallucination and cannot conduct ''last-mile'' exploration based on human instruction. To address these challenges, we present G-DRAGON, a retrieval-augmented framework for outdoor, open-world navigation. This framework maps natural-language commands to versioned, local OSM entities via generative retrieval based on lightweight LLM, yielding accurate coordinates for global route planning. A high-level planning module bridges global topological routes with the SLAM system, projecting geospatial waypoints into the robot's navigable frame. For the ''last mile," the framework transitions to frontier-based exploration and open-set semantic voxel mapping to localize open-vocabulary targets. Experimental results in simulation demonstrate our framework outperforms state-of-the-art baselines. Furthermore, we validate the system in unseen real-world urban environments on an Unmanned Ground Vehicle (UGV), successfully completing person-search missions with trajectories of up to 500m.

Dongzhihan Wang, Yi Du, Jianan Sun +5

School of Future Technology, Shanghai University, Shanghai 200444, China

Off-Road Robot NavigationLarge Language Model-Based Robot PlanningRobot NavigationOpen-Vocabulary Object NavigationFrontier-Based Exploration
AskPDF
May 24, 2026

GreenSeg: Ground Segmentation Algorithm for Agricultural Robots in Mediterranean Greenhouses using RGB-D Point Clouds

Greenhouse agriculture in the Mediterranean region faces significant automation challenges due to its unique structural and environmental constraints. These environments are characterized by extremely narrow aisles, heterogeneous terrains ranging from concrete to tilled soil and severe optical interference caused by polyethylene covers, which induce specular reflections and "ghost points" in depth sensors. While autonomous navigation is essential for digitizing agricultural tasks, traditional solutions often rely on expensive 3D LiDAR systems that are economically unscalable for most facilities. To address this, this paper presents GreenSeg, a robust perception framework for autonomous navigation using RGB-D sensing. The proposed method introduces a dual-layer validation strategy: a robust global plane fitting combined with a surface curvature filter for terrain adaptability, and a seed-point-based Region Growing constraint to ensure the spatial continuity of the navigable plane. Experimental validation was conducted using the AGRICOBIOT I platform across four diurnal scenarios with varying solar elevations. The results show that GreenSeg consistently outperforms benchmark segmentation methods, achieving peak improvements of 11.58% in mean Recall and 19.24% in mIoU during critical rotational maneuvers at the end of corridors. These findings confirm that the proposed algorithm enables stable and safe autonomous navigation in unstructured, dynamic agricultural environments that are subject to budget constraints and sensitive to lighting conditions.

Fernando Cañadas-Aránega, José C. Moreno, José L. Blanco-Claraco

Department of Informatics, CIESOL, ceiA3, Universidad de Almería, Ctra. Sacramento s/n, Almería, 04120, Spain · Department of Engineering, CIESOL, ceiA3, Universidad de Almería, Ctra. Sacramento s/n, Almería, 04120, Spain

3D Point Cloud SegmentationRobot NavigationAgricultural RoboticsRobotic PerceptionSemantic Segmentation
AskPDF
May 24, 2026

Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation

Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models. Olfactory navigation is a highly dynamic and non-stationary task that benefits from real-time continual learning. We introduce an adaptive framework called Grow-Prune-Freeze (GPF) networks that enable an agent to continually learn through growing, pruning, and freezing early layers of its policy in response to world complexity. Grounding GPFs in non-linear random matrix theory, we show that the work of Pennington & Worth (2017) can be extended from single hidden layers to n-layer continual-learning models, and that eigenvalue composition of network weights is preserved as successive layers are added. We show that GPFs based on Expected SARSA achieve a 94% success rate on turbulent plume navigation - a partially observable, non-stationary task representative of the "big world" challenges that motivate adaptive learning in robotics - and provide supporting methodology for applying GPFs in other world models. Further experiments amount evidence that GPFs may generalize well to other machine learning tasks such as reinforcement learning in Atari, image classification, and autoregressive language models. We open source all code and data to encourage improvements on and more research in olfactory robotics.

Kordel K. France, Ovidiu Daescu

Department of Computer Science, University of Texas at Dallas, USA

Non-Stationary RLPartially Observable RLRobot NavigationContinual LearningAdaptive LearningNeural Network Pruning
AskPDF
May 24, 2026

Performance Comparison of Classical and Neural Sampling Algorithms for Robotic Navigation

Integrating artificial intelligence (AI) into sampling-based motion planning provides new possibilities for improving autonomous navigation efficiency. In this paper, three algorithms, namely RRT*, Neural RRT*, and Neural Informed RRT*, are implemented and evaluated on environments containing convex and concave obstacles with different obstacle densities. The obtained results indicate that neural-guided planners improve path quality, producing up to 14% shorter paths and 55--75% smoother trajectories compared with the conventional RRT* algorithm. Among the evaluated methods, Neural Informed RRT* achieves the best overall performance in terms of path length and trajectory smoothness. These results demonstrate the effectiveness of AI-guided sampling strategies for improving reliability and trajectory efficiency in robotic and UAV navigation, despite a slight increase in computation time. Overall, the study highlights the growing importance of artificial intelligence in real-time robotic path planning applications.

Hichem Cheriet, Badra Khellat Kihel, Samira Chouraqui

dept. of Computer Science Universit´e d’USTO Mohamed Boudiaf Oran, Algeria · dept. of Economics Oran2 Mohamed BenAhmed University Oran, Algeria

Robot NavigationSampling-Based Motion Planning
AskPDF
May 24, 2026

Convex-Neural RRT*: Fast and Reliable Learning-Guided Sampling for High-Quality Robot Path Planning

Sampling-based algorithms for robot path planning offer probabilistic completeness and strong empirical convergence properties across environments with diverse obstacle configurations. However, in practice, these methods often require many iterations to obtain high-quality solutions. This paper proposes Convex-Neural RRT*, an enhanced RRT* variant that incorporates neural guidance to predict informative waypoint regions near high-quality paths. Convex candidate regions are extracted from these predictions, enabling the planner to concentrate exploration on geometrically relevant areas while preserving global exploration. The proposed algorithm is evaluated against Neural RRT*, Neural Informed RRT*, classical RRT*, and LTA* across three environment types and 18 benchmark maps. Experimental results show that Convex-Neural RRT* reduces computation time by 30-75% compared to neural-guided variants and up to 88-98% relative to LTA*, while achieving an average path length reduction of approximately 5% compared to classical RRT*, with larger improvements observed in complex environments. The method also maintains an overall success rate above 99% across varying obstacle densities. These findings indicate that convex-guided neural sampling provides an effective balance between computational efficiency and solution quality, supporting its applicability to time-sensitive robotic navigation tasks.

Hichem Cheriet, Badra Khellat Kihel, Samira Chouraqui +1

Robot NavigationMotion PlanningExplainable Motion PlanningSampling-Based Motion Planning
AskPDF
May 23, 2026

Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion

This paper presents Elevator-LIO, a LiDAR-inertial odometry framework designed to achieve continuous robot localization during elevator travel, thereby supporting cross-floor robotic tasks. To address the state-estimation problem in non-inertial frames, Elevator-LIO establishes a decoupled state-estimation model that separately models the robot motion relative to the elevator and the elevator motion itself, and embeds it into a mode-dependent iterated error-state Kalman filter framework. This framework degenerates to conventional LIO estimation in ordinary indoor environments, while enabling the propagation and constrained update of elevator-related states in elevator non-inertial environments, thereby achieving continuous and stable localization. An elevator mode manager detects elevator entry and exit events using LiDAR ranging statistics and estimated states, and introduces event-triggered zero-velocity and zero-acceleration updates when the elevator stops to suppress accumulated vertical drift. In addition, this paper adopts an adaptive voxel downsampling strategy to maintain a stable number of effective points under significant environmental scale changes. We conduct extensive experiments on 20 real-world sequences containing 79 elevator rides, including practical challenges such as large-scale spaces, long vertical travel, dynamic pedestrian interference, and mirror reflections. The results show that Elevator-LIO maintains continuous localization accuracy in all sequences, with terminal height error below 1 cm in 17 sequences. In contrast, existing representative localization systems perform poorly on these elevator sequences. Tests on the Hilti 2022/2023 datasets further show that the proposed method remains competitive in standard indoor scenarios. The project page is available at https://xiaofan4122.github.io/Elevator_LIO_Page/.

Yifan Zhang, Yudong Huang, Yuchong Zhang +4

Shanghai Jiao Tong University, Shanghai, China

Kalman FilteringInertial OdometryRobot NavigationLiDAR OdometryRobot Localization
AskPDF
May 22, 2026

Balancing Accuracy and Efficiency: Adaptive Dynamics Orchestration for Model Predictive Control

Model Predictive Control (MPC) for autonomous navigation faces a fundamental trade-off between model accuracy and real-time efficiency. High-fidelity dynamics models can accurately predict complex vehicle-terrain interactions during trajectory rollouts, but incur significant computational cost, increasing inference latency and reducing control frequency. Conversely, lightweight models enable fast updates and dense sampling, yet may produce erroneous predictions under safety-critical conditions, potentially leading to catastrophic failures such as vehicle rollover. To address this trade-off, we propose Adaptive Dynamics Orchestration (ADO), a framework that dynamically selects the most appropriate dynamics model for the current navigation context. ADO maintains a library of models spanning diverse accuracy-efficiency profiles and continuously refines terrain-conditioned performance estimates using residual errors from online counterfactual rollouts, where executed control actions are replayed across the model library to assess predictive discrepancy. These estimates guide model selection in real time, balancing computational efficiency and predictive accuracy. Real-world experiments on an off-road ground robot demonstrate that ADO significantly reduces modeling error compared to a fixed low-latency baseline, while approaching the accuracy of the highest-fidelity model without incurring its computational cost, resulting in more reliable and effective navigation in challenging terrain.

Francesco Cancelliere, Aniket Datar, Giovanni Muscato +1

Department of Electrical, Electronic and Computer Engineering (DIEEI), University of Catania, Italy · Department of Computer Science, George Mason University, USA

Off-Road Robot NavigationAdaptive Model RoutingRobot NavigationModel Predictive ControlAdaptive Control
AskPDF
May 22, 2026

MASt3R-Nav: WayPixel Navigation in Relative 3D Maps

Visual navigation ability is strongly tied to its underlying representation of the world. Unlike classical 3D maps that require globally-consistent geometry, image- or object-relative topological graphs almost entirely do away with geometric understanding. But, this comes at the cost of navigation capability, often limiting it to merely teach-and-repeat. In this work, we propose a novel map representation in the form of pixel-relative connectivity, which is geometrically accurate but does not require global geometric consistency. Inspired by recent progress in 3D grounded image matching, we construct a map from an image sequence through inter-image connectivity based on pixel correspondences in the relative 3D coordinate systems of individual image pairs. We then use this pixel-level graph to perform global path planning by approximating and sparsifying intra-image pixel connectivity. Through this, we derive a ''WayPixel Costmap'' representation and train a controller conditioned on it to predict a trajectory rollout. We show that this dense pixel-level costmap based on relative geometry is a more accurate conditioning variable for control prediction than its image- and object-level counterparts. This enables a highly capable navigation system, as validated on four types of navigation tasks in the simulator and through real world demonstrations.

Vansh Garg, Rohit Jayanti, Krish Pandya +5

Robotics Research Center, IIIT-Hyderabad, India · University of Heidelberg · MBZUAI

Robot NavigationRobot Motion PlanningVision-Based Robot Control3D Mapping
AskPDF
May 22, 2026

Droneulator: A Portable UAV Simulator for Agricultural Workflows with RotorPy and Godot 4

Agricultural UAV research requires simulators that integrate realistic 3D scenes, high-fidelity vehicle dynamics, and robotics middleware, while remaining practical to deploy across heterogeneous development machines. We present Droneulator, a portable UAV simulator architecture that combines RotorPy for multirotor dynamics with Godot 4 for rendering and sensor generation. Droneulator exposes both PX4-based control and a lightweight WebSocket command path, and publishes synchronised visual and state streams through a Zenoh-based ROS2-compatible pipeline. This integration enables a single stack to support inspection-oriented data capture, ROS2/PX4 local planning, and reinforcement learning experiments without modifying the simulator infrastructure. We present quantified validation of the current system across three agricultural UAV workflows: tree-scale image collection for 3D reconstruction with COLMAP, local planning around canopy obstacles using EGO-Planner, and closed-loop reinforcement learning through a custom Gymnasium environment. In the reported setup, the results show that the simulator can sustain low-latency sensing, support reconstruction-oriented data collection under varying capture density, execute collision-free local planning around canopy obstacles, and support stable depth-sensing-based policy training for obstacle-aware navigation. Together, these results show the potential of Droneulator for agricultural UAV inspection, planning, and learning within one deployable stack.

Jacob Swindell, Michael Lowen, Marija Popovic +1

Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, Lincoln, UK. · Faculty of Aerospace Engineering, MAVLab, Delft University of Technology (TU Delft), Delft, Netherlands.

Robotics SimulationRobot NavigationAgricultural Robotics
AskPDF
May 22, 2026

Multi-Floor Exploration for Ground Robots via an Incremental Reachable Graph and Structural Priors

Autonomous exploration of multi-floor buildings remains challenging for ground robots because conventional 2D and 2.5D maps cannot represent overlapping traversable surfaces such as stairs, ramps, and multiple reachable elevations. This letter presents a multi-floor exploration framework based on an incremental reachable graph. Built as a sparse graph over reachable support surfaces, the graph preserves potentially valid connectivity through tentative graph elements under sparse observations and enables stable, physically reachable frontier detection. To guide exploration beyond the currently mapped floor, we project task-zone priors from an explored floor to initialize a hypothetical graph on the target floor and reconcile it incrementally with incoming observations. A hierarchical planner then jointly reasons over confirmed and hypothetical structures for global guidance. In simulation, the proposed method demonstrates improved exploration efficiency and mapping completeness compared to evaluated baselines. Furthermore, onboard real-world experiments validate its practical feasibility and real-time performance.

Zhiwen Zhu, Jiaqi Chen, Xiangyi Huang +2

Simultaneous Localization and MappingRobot NavigationGraph-Based Planning
AskPDF
May 21, 2026

Scout-Assisted Planning for Heterogeneous Robot Teams under Partially Known Environments

Autonomous robot teams navigating partially known environments face costly backtracking when ground robots encounter blocked roads that are only revealed upon physical traversal. We address this with Scout-Assisted Planning, a heterogeneous planning framework in which scouting Unmanned Aerial Vehicles proactively gather environmental information to improve Unmanned Ground Vehicle navigation. To focus scouting on the most consequential edges, we propose Information Gain-based Action Pruning, which scores candidate scouting actions by their expected impact on ground robot behavior. Since exact Information Gain-based Action Pruning computation is prohibitively expensive, we develop a Graph Neural Network based model that predicts information gain values directly from graph structure and belief state, reducing planning time to real-time levels without sacrificing solution quality. Experiments across three environment types show that SAP with Information Gain Action Pruning reduces ground robot travel cost by 31.9--37.7% over the Canadian Traveler Problem baseline, and outperforms proximity-based scouting guidance by an additional 8--14%, confirming that principled information-gain-guided scouting is both more effective and computationally feasible for real-world deployment

Hoang-Dung Bui, Abhish Khanal, Raihan Islam Arnob +1

George Mason University

Robot NavigationInformative Path PlanningMulti-Robot CoordinationMotion Planning
AskPDF
May 20, 2026

Flying Together: Human-Guided Immersive Shared Control for Aerial Robot Teams in Unknown Environments

While autonomous multi-robots can achieve safe and coordinated navigation, they often struggle to adapt to unforeseen conditions and to capture operator-driven objectives in unstructured environments. We present a Virtual Reality (VR)-based shared control framework for teams of drones operating in constrained and unknown environments, enabling real-time, user-guided exploration. At the core of our approach is a novel, user-guided motion-primitive-based planner that computes continuous, collision-free trajectories while continuously integrating operator input. This planner is coupled with an admittance controller, allowing the operator to flexibly influence team behavior and guide drones toward regions of interest that autonomous planners may overlook. The system supports mixed-reality operations with both physical and simulated drones, and implements a bilateral VR-based interface, allowing the operator to guide the robot team via migration points while receiving immediate visual feedback of the team state. Experimental results show that shared control improves obstacle avoidance, maintains inter-agent spacing, and reduces operator effort, demonstrating the feasibility and advantages of immersive, human-in-the-loop multi-robot navigation.

Lou De Bel-Air, Luca Morando, Ruitao Chen +7

New York University, NY 10012, USA · Ecole Polytechnique Federale de Lausanne, Station 9 CH-1015 Lausanne, Switzerland · University of California Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA 94720, USA

Multi-Robot Motion PlanningAerial RoboticsRobot NavigationRobot TeleoperationMulti-Robot ExplorationHuman-Robot Collaboration+1
AskPDF
May 20, 2026

A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning

Kinodynamic planning for car-like vehicles on uneven terrain requires simultaneously optimizing competing objectives such as path efficiency and pose stability. This work presents an adaptive epsilon-constraint method integrated into a Model Predictive Control (MPC) framework, where the epsilon bounds are dynamically adjusted based on terrain descriptors to explore the Pareto front in real time. To capture vehicle-terrain dynamics, we develop a semi-parametric model combining analytical vehicle dynamics with a Sparse Gaussian Process (SGP) trained on the same terrain descriptors. The proposed epsilon-MPC is evaluated against MPPI and GAKD baselines, achieving a 94% navigation success rate while reducing maximum orientation deviation by 24% and improving multi-objective trade-off quality by 23%.

Otobong Jerome, Geesara Kalathunga, Tiago Nascimento

Laborat´orio de Engenharia de Sistemas e Rob´otica, Universidade Federal da Para´ıba (UFPB), Jo˜ao Pessoa, Para´ıba, 58055-000, Brazil · School of Computer Science, University of Lincoln, Lincoln, United Kingdom

Constrained Motion PlanningKinodynamic Motion PlanningRobot NavigationModel Predictive ControlVehicle DynamicsMotion Planning+2
AskPDF
May 20, 2026

Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation

Adaptive robot navigation in dynamic environments requires policies that can reach the target reliably while producing efficient and stable trajectories. This paper presents Q-SpiRL, a quantum spiking reinforcement learning framework for obstacle-aware robot navigation. The framework develops and evaluates five agent families: tabular Q-learning, classical MLP, classical SNN, quantum-enhanced MLP (QMLP), and quantum-enhanced spiking neural network (QSNN). While all models are implemented under a unified training and evaluation pipeline, the QSNN is the central architecture of interest, as it combines spike-based temporal processing with variational quantum feature transformation. Experiments are conducted across three grid-world environments of increasing size, namely 20x20, 30x30, and 40x40, with both static and dynamic obstacles. Performance is assessed using success rate, success-weighted path length, path length, and turn rate under deterministic inference. Results show that QSNN achieves the strongest overall trade-off between task completion, trajectory efficiency, and motion smoothness, reaching up to 99% success rate while maintaining high path efficiency in the most challenging setting. Execution on IBM quantum hardware further demonstrates the feasibility of deploying the proposed hybrid policy under real-device conditions.

Mohamed Khair Altrabulsi, Nouhaila Innan, Alberto Marchisio +2

1eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE

Robot NavigationRobotic RLSpiking Neural NetworksQuantum RLQuantum Neural Networks
AskPDF
May 19, 2026

TravExplorer: Cross-Floor Embodied Exploration via Traversability-Aware 3-D Planning

Zero-shot Object Navigation (ZSON) has shown promise for open-vocabulary target search in unseen environments, yet most existing systems remain tied to planar representations and single-floor assumptions. These assumptions become inadequate in real buildings, where navigation involves floors, stairs, landings, and vertically overlapping spaces. This article presents TravExplorer, a cross-floor embodied exploration framework that couples zero-shot semantic guidance with traversability-aware 3-D planning. TravExplorer maintains a unified volumetric map that distinguishes occupied structures from robot-reachable support surfaces and extracts traversable frontiers from connected support surfaces, including floors, stairs, and landings. A FOV-aware active perception strategy further resolves incomplete observations during cross-floor traversal. To reduce semantic-reasoning latency, a lightweight guidance module aligns a probabilistic instance map from online open-vocabulary segmentation with a spatial value map from fast image-to-text matching. Based on these geometric and semantic memories, a hierarchical planner performs target-aware frontier touring over object hypotheses, traversable frontiers, and stair landmarks, and generates executable cross-floor motions through foothold-guided 3-D search and vertically constrained local trajectory optimization. Experiments over 4,195 simulated episodes on HM3D and MP3D demonstrate consistent advantages over representative ObjectNav baselines. Fifty real-world trials on a Unitree Go2 further validate open-vocabulary target search across single-floor and cross-floor indoor environments without prior maps or human intervention. The code will be released at https://github.com/wuyi2121/TravExplorer.

Han Zheng, Zhe Chen, Yudong Huang +4

Shanghai Jiao Tong University, Shanghai, China.

Embodied NavigationRobot NavigationFrontier-Based ExplorationMotion PlanningObject-Goal NavigationRobot Traversability Estimation
AskPDF
May 19, 2026

D-CLING: Prior-Preserving Depth-Conditioned Fine-Tuning for Navigation Foundation Models

Navigation Foundation Models (NFMs) trained on large cross-embodied datasets have demonstrated powerful generalizability in various scenarios. Adopting in-domain fine-tuning for an NFM efficiently calibrates the visuomotor policy, promising further improvement even in a novel scenario. However, the fine-tuned models still suffer from poor obstacle avoidance or fail to properly reach the provided goals. Furthermore, model updates using a small subset of data typically erode the pre-trained prior, compromising the pre-training generalization. Consequently, fine-tuning deteriorates the capability of the model for robust and accurate navigation. In this work, we present a novel fine-tuning method that leverages large-scale pre-training while efficiently learning in novel setups, such as environments or camera configurations. In particular, inspired by ControlNet, we fine-tune an NFM by attaching a trainable copy of the pre-trained backbone using zero-initialized residual pathways, thereby learning geometric cues. This design enables the model to efficiently acquire in-domain geometry while preserving pre-trained knowledge across various behaviors. Despite its simplicity, our comprehensive evaluation of real-world navigation suggests that our proposal effectively enables robust long-horizon navigation with minimal collisions and human intervention. Additionally, our offline analysis shows that the proposed method maintains or further improves action prediction capabilities beyond the fine-tuned dataset, providing a key insight into continual learning for general navigation. The project page: https://toyotafrc.github.io/DCLING-Proj/

Shintaro Nakaoka, Takayuki Kanai, Kazuhito Tanaka

The authors are with the Frontier Research Center, Toyota Motor Corporation, Toyota, Aichi, Japan.

Visual NavigationFine-TuningRobot NavigationRobot Policy Generalization
AskPDF
May 17, 2026

Beyond Geometry: Efficient Topologically-Grounded Navigation in Complex 3D Environments

Ground robot navigation in complex 3D environments is often hindered by geometric ambiguity, where non-traversable structures such as furniture share local geometric properties with navigable ground. Furthermore, the computational cost of searching massive voxel spaces remains a significant challenge. To address these issues, we present a surface extraction framework that constructs a reduced state space of physically reachable standing positions by enforcing ground support, overhead clearance, and seed-based connectivity constraints. Evaluation across five Matterport3D indoor scenes and three PCT benchmark scenes demonstrates over 80% state space reduction and sub-millisecond A* search on the Matterport3D scenes, with 100% planning success across all 300 tested queries.

Yifan Du, Chengwei Zhang, Siyu Liao +1

School of Integrated Circuits Sun Yat-sen University Shenzhen, China

Robot NavigationRobot Motion Planning
AskPDF
May 16, 2026

BAT-Nav: Belief-Based Arbitration and Termination via Remaining Discoverability in Multi-Goal Semantic Navigation

Multi-goal semantic navigation couples searches through a shrinking horizon: effort spent on one request can make another effectively undiscoverable. Discoverability therefore depends on both goal evidence and the budget that other requests consume. We present BAT-Nav, Belief-Based Arbitration and Termination, which estimates remaining discoverability: the probability that a frozen executor can complete a goal within an additional budget. A calibrated local hazard converts navigation telemetry into a budget-conditioned completion curve. Its marginal return governs reversible; conservative belief and local evidence govern. On intervention-independent replays, the model obtains Brier .132 and ECE .034 without transfer refitting; 50-action completion rises from .047 to .321 across marginal-return sextiles. On HM3D / ApexNav, BAT-Nav raises CR from .372 to .414 and obtains MGSR .173. Its gain widens from 3.4 to 5.8 CR points as competing goals increase from two to five, with the same ordering on MP3D and under infeasible requests.

Xi Lin, Kangyi Wu, Jiayi Li +3

1LCSR, Johns Hopkins University · 2Xi’an Jiaotong University · 3JD Explore Academy, Beijing, China

Robot NavigationRobot Task Planning
AskPDF
May 16, 2026

Emergence of a Flow-Assisted Casting Strategy for Olfactory Navigation via Memory-Augmented Reinforcement Learning

In dynamic flow fields, various animals exhibit remarkable odor search capabilities despite relying on stochastic detections. Interestingly, there exists an optimal time window for integrating these detections that maximizes search efficiency. To understand the underlying mechanism, we investigate the navigation performance of Reinforcement Learning (RL) agents in unsteady flows under varying memory lengths and flow conditions. Without any predefined models, the agents develop a flow-assisted casting strategy and adaptively adjust both the geometry of their search trajectories and the concentration threshold for initiating casting to maximize the success rate. The agent's average speed toward the odor source exhibits a non-monotonic dependence on memory length, which can be explained by the "sector-search" model.

Changxu Zhao, Dongxiao Zhao, Xin Bian +1

State Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China · State Key Laboratory of Fluid Power and Mechatronic Systems, Department of Engineering Mechanics, Zhejiang University, Hangzhou 310027, People’s Republic of China

Reinforcement LearningRobot NavigationMemory-Augmented Agents
AskPDF
May 15, 2026

Beyond Collision Avoidance: Multi-Robot Yielding and Spatial Affordance in Emergency Evacuations

As mobile service robots increasingly coexist with pedestrians, ensuring passively safe behaviour during confined emergency evacuations is critical. Existing multi-robot yielding strategies often focus solely on collision avoidance and macroscopic flow optimisation, overlooking environmental affordances and human spatial expectations. To bridge the gap between macroscopic theory and micro-level perception, we conducted a game-based virtual evacuation experiment (N=56). We investigated individual psychological responses to four multi-robot yielding strategies (Hide, LineEscape, Freeze, ShortestPath) across confined corridors with and without refuge niches. Our results establish a robust preference hierarchy (Hide > LineEscape > Freeze > ShortestPath), demonstrating that proactive space-yielding significantly outperforms freezing and efficiency-first approaches. Crucially, we found that environmental affordances heavily shape cognitive expectations. Actively utilising available niches amplifies the psychological comfort of proactive yielding (Hide). Conversely, failing to use an obvious niche (e.g., executing LineEscape) may trigger Expectation Violation. This is reflected in a drastically increased perceived cognitive delay, despite objectively unimpeded trajectories. Furthermore, prior robot interaction experience helps users decode complex social intents. Ultimately, this research demonstrates that safe human-robot interaction during emergencies must evolve from pure trajectory optimisation to semantically aware navigation. Future work will extend this framework to investigate complex interactions between robot swarms and pedestrian crowds.

Ning Zhou, Edmund R. Hunt, Nikolai W. F. Bode

School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK

Robot NavigationSocial Robot NavigationHuman-Robot InteractionRobot Collision Avoidance
AskPDF