Multi-Robot Motion Planning

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Latest papers 135

30 papers in the last four weeks, up 329% on the four weeks before. 0.2% of all new papers.

Feb 25, 2026

Enhancing Cellular-enabled Collaborative Robots Planning through GNSS data for SAR Scenarios

Cellular-enabled collaborative robots are becoming paramount in Search-and-Rescue (SAR) and emergency response. Crucially dependent on resilient mobile network connectivity, they serve as invaluable assets for tasks like rapid victim localization and the exploration of hazardous, otherwise unreachable areas. However, their reliance on battery power and the need for persistent, low-latency communication limit operational time and mobility. To address this, and considering the evolving capabilities of 5G/6G networks, we propose a novel SAR framework that includes Mission Planning and Mission Execution phases and that optimizes robot deployment. By considering parameters such as the exploration area size, terrain elevation, robot fleet size, communication-influenced energy profiles, desired exploration rate, and target response time, our framework determines the minimum number of robots required and their optimal paths to ensure effective coverage and timely data backhaul over mobile networks. Our results demonstrate the trade-offs between number of robots, explored area, and response time for wheeled and quadruped robots. Further, we quantify the impact of terrain elevation data on mission time and energy consumption, showing the benefits of incorporating real-world environmental factors that might also affect mobile signal propagation and connectivity into SAR planning. This framework provides critical insights for leveraging next-generation mobile networks to enhance autonomous SAR operations.

Arnau Romero, Carmen Delgado, Jana Baguer +2

AI-driven Systems, i2CAT Foundation, Barcelona, Spain · Universitat Politècnica de Catalunya, UPC, Barcelona, Spain · Pompeu Fabra University, UPF, Barcelona, Spain +2

Search-and-Rescue Robotics Robot Task Planning Multi-Robot Exploration Multi-Robot Motion Planning

Feb 25, 2026

Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning

Multi-robot task planning requires decomposing natural-language instructions into executable actions for heterogeneous robot teams. Conventional Planning Domain Definition Language (PDDL) planners provide rigorous guarantees but struggle to handle ambiguous or long-horizon missions, while large language models (LLMs) can interpret instructions and propose plans but may hallucinate or produce infeasible actions. We present a hierarchical multi-agent LLM-based planner with prompt optimization: an upper layer decomposes tasks and assigns them to lower-layer agents, which generate PDDL problems solved by a classical planner. When plans fail, the system applies TextGrad-inspired textual-gradient updates to optimize each agent's prompt and thereby improve planning accuracy. In addition, meta-prompts are learned and shared across agents within the same layer, enabling efficient prompt optimization in multi-agent settings. On the MAT-THOR benchmark, our planner achieves success rates of 0.95 on compound tasks, 0.84 on complex tasks, and 0.60 on vague tasks, improving over the previous state-of-the-art LaMMA-P by 2, 7, and 15 percentage points respectively. An ablation study shows that the hierarchical structure, prompt optimization, and meta-prompt sharing contribute roughly +59, +37, and +4 percentage points to the overall success rate.

Tomoya Kawabe, Rin Takano

Data Science Laboratories, NEC Corporation, 1753, Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa, 211-8666, Japan.

Large Language Model-Based Robot Planning Multi-Agent LLM Systems Robotics Prompt Optimization Multi-Robot Motion Planning

Feb 23, 2026

Rendezvous and Docking of Mobile Ground Robots for Efficient Transportation Systems

In-Motion physical coupling of multiple mobile ground robots has the potential to enable new applications like in-motion transfer that improves efficiency in handling and transferring goods, which tackles current challenges in logistics. A key challenge lies in achieving reliable autonomous in-motion physical coupling of two mobile ground robots starting at any initial position. Existing approaches neglect the modeling of the docking interface and the strategy for approaching it, resulting in uncontrolled collisions that make in-motion physical coupling either impossible or inefficient. To address this challenge, we propose a central mpc approach that explicitly models the dynamics and states of two omnidirectional wheeled robots, incorporates constraints related to their docking interface, and implements an approaching strategy for rendezvous and docking. This novel approach enables omnidirectional wheeled robots with a docking interface to physically couple in motion regardless of their initial position. In addition, it makes in-motion transfer possible, which is 19.75% more time- and 21.04% energy-efficient compared to a non-coupling approach in a logistic scenario.

Lars Fischer, Daniel Flögel, Sören Hohmann

FZI Research Center for Information Technology, Karlsruhe, Germany · Institute of Control Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany

Robotics Multi-Robot Coordination Multi-Robot Motion Planning

Feb 20, 2026

Role-Adaptive Collaborative Formation Planning for Team of Quadruped Robots in Cluttered Environments

This paper presents a role-adaptive Leader-Follower-based formation planning and control framework for teams of quadruped robots operating in cluttered environments. Unlike conventional methods with fixed leaders or rigid formation roles, the proposed approach integrates dynamic role assignment and partial goal planning, enabling flexible, collision-free navigation in complex scenarios. Formation stability and inter-robot safety are ensured through a virtual spring-damper system coupled with a novel obstacle avoidance layer that adaptively adjusts each agent's velocity. A dynamic look-ahead reference generator further enhances flexibility, allowing temporary formation deformation to maneuver around obstacles while maintaining goal-directed motion. The Fast Marching Square (FM2) algorithm provides the global path for the leader and local paths for the followers as the planning backbone. The framework is validated through extensive simulations and real-world experiments with teams of quadruped robots. Results demonstrate smooth coordination, adaptive role switching, and robust formation maintenance in complex, unstructured environments. A video featuring the simulation and physical experiments along with their associated visualizations can be found at https://youtu.be/scq37Tua9W4.

Magnus Norén, Marios-Nektarios Stamatopoulos, Avijit Banerjee +1

Robotics and Artificial Intelligence Group, Department of Computer, Electrical and Space Engineering, Lulea University of Technology, 971 87 Lule˚a, Sweden

Multi-Robot Formation Control Multi-Robot Motion Planning Robot Collision Avoidance

Feb 19, 2026

Multi-Agent Temporal Logic Planning via Penalty Functions and Block-Coordinate Optimization

Multi-agent planning under Signal Temporal Logic (STL) is often hindered by collaborative tasks that lead to computational challenges due to the inherent high dimensionality of the problem, preventing scalable synthesis with satisfaction guarantees. To address this, we formulate STL planning as an optimization program under multi-agent STL constraints and introduce a penalty-based unconstrained relaxation that can be efficiently solved via a Block-Coordinate Gradient Descent (BCGD) method, where each block corresponds to a single agent's decision variables, thereby mitigating complexity. By utilizing a quadratic penalty function defined via smooth STL semantics, we show that BCGD iterations converge to a stationary point of the penalized problem under standard regularity assumptions. To enforce feasibility, the BCGD solver is embedded within a two-layer optimization scheme: inner BCGD updates are performed for a fixed penalty parameter, which is then increased in an outer loop to progressively improve multi-agent STL robustness. The proposed framework enables scalable computations and is validated through various complex multi-robot planning scenarios.

Eleftherios E. Vlahakis, Arash Bahari Kordabad, Lars Lindemann +3

the Division of Computer Engineering and Computer Science, University West, 46132, Trollhättan, Sweden · the Max Planck Institute for Software Systems, Kaiserslautern, Germany · the Automatic Control Laboratory, ETH Zürich, 8092, Zürich, Switzerland +2

Multi-Agent Planning Multi-Robot Motion Planning Constrained Optimization

Feb 16, 2026

Scalable Multi-Robot Path Planning via Quadratic Unconstrained Binary Optimization

Multi-Agent Path Finding (MAPF) remains a fundamental challenge in robotics, where classical centralized approaches exhibit exponential growth in joint-state complexity as the number of agents increases. This paper investigates Quadratic Unconstrained Binary Optimization (QUBO) as a structurally scalable alternative for simultaneous multi-robot path planning. This approach is a robotics-oriented QUBO formulation incorporating BFS-based logical pre-processing (achieving over 95% variable reduction), adaptive penalty design for collision and constraint enforcement, and a time-windowed decomposition strategy that enables execution within current hardware limitations. An experimental evaluation in grid environments with up to four robots demonstrated near-optimal solutions in dense scenarios and favorable scaling behavior compared to sequential classical planning. These results establish a practical and reproducible baseline for future quantum and quantum-inspired multi-robot coordinations.

Javier González Villasmil

Multi-Robot Motion Planning Multi-Agent Path Finding Quadratic Unconstrained Binary Optimization

Dec 9, 2025

RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight

This paper presents an approach to mutual collision avoidance based on Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velocity Constraints (RVCs). Unlike most existing methods, the proposed approach relies solely on observable information about other robots, eliminating the need for excessive communication. The computationally efficient algorithm for computing RVCs, together with the direct integration of these constraints into the NMPC problem formulation at the controller level, allows the whole pipeline to run at 100 Hz. This high processing rate, combined with modeled nonlinear dynamics of the controlled Uncrewed Aerial Vehicles (UAVs), is a key feature that facilitates the use of the proposed approach for agile UAV flight. The proposed approach was evaluated through extensive simulations emulating real-world conditions in scenarios involving up to 10 UAVs and velocities of up to 25 m/s, and in real-world experiments with accelerations up to 30 m/s

^2

. Comparison with the state of the art shows 31% improvement in terms of flight time reduction in challenging scenarios, while maintaining a collision-free navigation in all trials.

Vit Kratky, Robert Penicka, Parakh M. Gupta +2

Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic

Multi-Robot Motion Planning Nonlinear MPC UAV Navigation Model Predictive Control Safe Motion Planning Robot Collision Avoidance

Nov 26, 2025

From Discrete Plans to Real-World Execution: A World-Model-Driven Framework for Execution-Aware Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) studies how to coordinate multiple agents to reach their goals without collisions and underpins a range of large-scale robotic systems, including automated warehousing and manufacturing. Recent advances enable MAPF solvers to compute high-quality plans for hundreds of agents. However, these plans are generated using simplified robot models with discretized time and action spaces. When they are deployed in physical systems, heterogeneous robot dynamics, asynchronous interactions, communication delays, and other real-world factors can lead to substantial deviations from planned performance. We bridge the gap between discrete planning and real-world execution through ExecTimeNet, a learned world model of MAPF execution that predicts how a discrete MAPF solution will unfold on physical robots, mapping each discrete action to its realized execution state, including its wall-clock completion time and the kinodynamic state in which it ends. Building on this capability, we first propose REMAP, an execution-aware MAPF framework that integrates execution-time estimation into planning, guiding the search toward MAPF solutions with improved execution performance. We also introduce ESADG, a post-planning optimization procedure that optimizes the execution schedule of a given MAPF solution while preserving path feasibility. We evaluate proposed frameworks in high-fidelity simulation with up to 300 agents and on physical robots. In simulation, ExecTimeNet predicts the execution state accurately and transfers to unseen maps and agent counts. Across simulation benchmarks spanning diverse map topologies, REMAP reduces delays by up to 21% over baselines, while ESADG achieves up to 40% normalized improvement. On physical hardware, the full pipeline reduces total execution time by up to 15.3%, demonstrating effective transfer from simulation to real-world deployment.

Jingtian Yan, Shuai Zhou, He Jiang +2

This paper was produced by the IEEE Publication Technology Group. They are in Piscataway, NJ.

Multi-Robot Motion Planning World Models for Robotics World Model-Based Planning Multi-Agent Path Finding

Nov 10, 2025

Continuous-Space Roadmap Generation for Mobile Robot Fleets with Distance Constraints and Geometry-Aware Discretization

Efficient routing of mobile robot fleets requires roadmaps with high redundancy, short path lengths, and sufficient node and edge clearance for conflict-free operation. Existing grid-based methods sacrifice geometric fidelity and impose Manhattan-distance path length constraints, whereas existing continuous-space methods neglect minimum distance constraints and transport demand. This paper proposes a continuous-space roadmap generation method that addresses this gap by placing nodes at convex corner points of the free space and at station interaction points, discretizing free space via local grid expansion, enforcing minimum inter-node and node-edge distance constraints derived from robot dimensions, and applying transport demand-driven K-shortest path pruning. The method is evaluated across three intralogistics environments using two multi-agent pickup and delivery (MAPD) solvers against three baselines: a reaction-diffusion sampling method (GSRM), an 8-connected grid, and random sampling. Under Priority Inheritance with Backtracking (PIBT), the proposed method outperforms GSRM by 1.2-23.4 % at maximum fleet size, the grid by at least 9.1 %, and random sampling by more than 10.4 % across all environments, with a space-time A* solver confirming these results. It further attains near-optimal normalized path lengths of 1.03-1.05 and the highest inter-station connectivity at comparable roadmap complexity.

Marvin Rüdt, Constantin Enke, Kai Furmans

Institute for Material Handling and Logistics, Karlsruhe Institute of Technology, Karlsruhe, Germany.

Multi-Robot Motion Planning Constrained Motion Planning Roadmap-Based Motion Planning Motion Planning Multi-Agent Path Finding

Nov 5, 2025

Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning

Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots to navigate dynamic environments while adhering to manifold constraints imposed by tasks. For example, service robots must carry cups upright while avoiding collisions with humans or other robots. Despite recent advances in decentralized MAMP for high-dimensional systems, incorporating manifold constraints remains difficult. To address this, we propose a manifold-constrained Hamilton-Jacobi reachability (HJR) learning framework for decentralized MAMP. Our method solves HJR problems under manifold constraints to capture task-aware safety conditions, which are then integrated into a decentralized trajectory optimization planner. This enables robots to generate motion plans that are both safe and task-feasible without requiring assumptions about other agents' policies. Our approach generalizes across diverse manifold-constrained tasks and scales effectively to high-dimensional multi-agent manipulation problems. Experiments show that our method outperforms existing constrained motion planners and operates at speeds suitable for real-world applications. Video demonstrations are available at https://youtu.be/RYcEHMnPTH8 .

Qingyi Chen, Ruiqi Ni, Junyoung Kim +1

Department of Computer Science at Purdue University, West Lafayette, IN 47907, USA.

Multi-Robot Motion Planning Constrained Motion Planning Reachability Analysis Safe Motion Planning Motion Planning

Oct 10, 2025

Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization

Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother trajectories, especially while planning in tight spaces. Unfortunately, it is often computationally intractable beyond a very small swarm size. In this paper, we propose Flow-Opt, a learning-based approach towards improving the computational tractability of centralized multi-robot trajectory optimization. Specifically, we reduce the problem to first learning a generative model to sample different candidate trajectories and then using a learned Safety-Filter(SF) to ensure fast inference-time constraint satisfaction. We propose a flow-matching model with a diffusion transformer (DiT) augmented with permutation invariant robot position and map encoders as the generative model. We develop a custom solver for our SF and equip it with a neural network that predicts context-specific initialization. The initialization network is trained in a self-supervised manner, taking advantage of the differentiability of the SF solver. We advance the state-of-the-art in the following respects. First, we show that we can generate trajectories of tens of robots in cluttered environments in a few tens of milliseconds. This is several times faster than existing centralized optimization approaches. Moreover, our approach also generates smoother trajectories orders of magnitude faster than competing baselines based on diffusion models. Second, each component of our approach can be batched, allowing us to solve a few tens of problem instances in a fraction of a second. We believe this is a first such result; no existing approach provides such capabilities. Finally, our approach can generate a diverse set of trajectories between a given set of start and goal locations, which can capture different collision-avoidance behaviors.

Simon Idoko, Prajyot Jadhav, Arun Kumar Singh

All authors are with the University of Tartu, Estonia.

Multi-Robot Motion Planning Constrained Motion Planning Multi-Agent System Optimization Trajectory Generation Safe Motion Planning

Sep 17, 2025

TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems

Modular Aerial Robot Systems (MARS) consist of multiple drone modules that are physically bound together to form a single structure for flight. Exploiting structural redundancy, MARS can be reconfigured into different formations to mitigate unit or rotor failures and maintain stable flight. Prior work on MARS self-reconfiguration has solely focused on maximizing controllability margins to tolerate a single rotor or unit fault for rectangular-shaped MARS. We propose TransforMARS, a general fault-tolerant reconfiguration framework that transforms arbitrarily shaped MARS under multiple rotor and unit faults while ensuring continuous in-air stability. Specifically, we develop algorithms to first identify and construct minimum controllable assemblies containing faulty units. We then plan feasible disassembly-assembly sequences to transport MARS units or subassemblies to form target configuration. Our approach enables more flexible and practical feasible reconfiguration. We validate TransforMARS in challenging arbitrarily shaped MARS configurations, demonstrating substantial improvements over prior works in both the capacity of handling diverse configurations and the number of faults tolerated. The videos and source code of this work are available at https://github.com/RuiHuangNUS/TransforMARS

Rui Huang, Zhiyu Gao, Siyu Tang +4

Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583 · Department of Mechanical Engineering, National University of Singapore, Singapore 117583

Multi-Robot Motion Planning Aerial Robotics Robot Failure Recovery Robotics Fault-Tolerant Control

Jul 31, 2025

Simulation-based planning of Motion Sequences for Automated Procedure Optimization in Multi-Robot Assembly Cells

Reconfigurable multi-robot cells offer a promising approach to meet fluctuating assembly demands. However, the recurrent planning of their configurations introduces new challenges, particularly in generating optimized, coordinated multi-robot motion sequences that minimize the assembly duration. This work presents a simulation-based method for generating such optimized sequences. The approach separates assembly steps into task-related core operations and connecting traverse operations. While core operations are constrained and predetermined, traverse operations offer substantial optimization potential. Scheduling the core operations is formulated as an optimization problem, requiring feasible traverse operations to be integrated using a decomposition-based motion planning strategy. Several solution techniques are explored, including a sampling heuristic, tree-based search and gradient-free optimization. For motion planning, a decomposition method is proposed that identifies specific areas in the schedule, which can be solved independently with modified centralized path planning algorithms. The proposed method generates efficient and collision-free multi-robot assembly procedures that outperform a baseline relying on decentralized, robot-individual motion planning. Its effectiveness is demonstrated through simulation experiments.

Loris Schneider, Marc Ungen, Elias Huber +1

Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology, 76131, Germany · Bosch Corporate Research, Robert Bosch GmbH, 71272 Renningen, Germany

Multi-Robot Motion Planning Multi-Robot Coordination Robot Task Planning

Jul 18, 2025

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning

Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized planners are capable of coordinating all arms but often face scalability limitations, restricting applicability in real-time settings. On the other hand, decentralized methods are scalable and recent deep learning-based approaches have shown promising results. However, these depend on accurate behavior prediction or coordination protocols and may fail when other arms act unpredictably. To address these challenges, we introduce a neural Hamilton-Jacobi Reachability (HJR) learning-based approach to approximate a safety value function that captures worst-case inter-arm safety constraints. We further develop a decentralized trajectory optimization framework that uses the learned HJR representation for real-time planning. The proposed method is scalable and data-efficient, generalizes across multi-manipulator systems, and outperforms state-of-the-art baselines on challenging multi-arm motion planning tasks.

Qingyi Chen, Zachary Kingston, Ahmed H. Qureshi

Department of Computer Science at Purdue University, West Lafayette, IN, USA

Multi-Robot Motion Planning Constrained Motion Planning Trajectory Optimization Reachability Analysis Safe Motion Planning

Mar 15, 2025

D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors. Code and video: https://github.com/proroklab/d4orm

Yuhao Zhang, Keisuke Okumura, Heedo Woo +2

University of Cambridge · UK · National Institute of Advanced Industrial Science and Technology (AIST) +1

Multi-Robot Motion Planning Constrained Motion Planning Trajectory Optimization Kinodynamic Motion Planning Trajectory Generation Diffusion-Based Planning+2