2025· Proceedings of the 3rd International Conference on Data Analysis and Machine Learning· pp. 386-390· 0 citations· 12 references
TL;DR
This paper provides a thorough survey and integrative presentation of cooperative path planning for multi-robot systems operating in dynamic, cluttered, and partially observable environments and proposes research directions including learning-augmented heuristics, unified safety-aware planning, adaptive MPC – CBF filters, and more informative benchmarks to drive reproducible progress.
Abstract
: This paper provides a thorough survey and integrative presentation of cooperative path planning for multi-robot systems operating in dynamic, cluttered, and partially observable environments. People synthesise algorithmic foundations ranging from heuristic graph search to sampling-based motion planners, including A*, D* Lite, and Safe Interval Path Planning for discrete/time-augmented spaces, as well as RRT, RRT*, and Informed RRT* for continuous configuration spaces. Multi-agent coordination techniques are reviewed, covering reciprocal collision avoidance (ORCA) and centralised Multi-Agent Path Finding (MAPF) solvers such as Conflict-Based Search (CBS) and bounded-suboptimal variants (ECBS). The paper also examine control and safety layers like Model Predictive Control and Control Barrier Functions that translate plans into dynamically feasible commands with safety guarantees. Recent progress in cooperative multi-agent reinforcement learning (MAPPO, QMIX, MADDPG) is evaluated for adaptability under partial observability and nonstationary environments. Applications in warehousing, intelligent transportation, and disaster response are used to illustrate practical trade-offs and integration patterns, referencing real-world systems such as Kiva-style warehouse fleets and autonomous driving pipelines. The paper concludes with a focused discussion on open challenges — scalability with guarantees, safety under uncertainty, sim-to-real transfer, and planning – control interface fragility — and proposes research directions including learning-augmented heuristics, unified safety-aware planning, adaptive MPC – CBF filters, and more informative benchmarks to drive reproducible progress.
This work presents a prioritized Safe Interval Path Planning algorithm (SIPP-PP) with a novel limited goal reservation strategy to prevent goal-blocking conflicts while allowing shared goal regions, and demonstrates a multi-robot planner capable of real-time operation in dense scenarios, satisfying the stringent requirements of industrial applications such as drive units in fulfillment centers.
Rajat Kumar, Kristin Predeck, Ken Meszaros et al.· Proceedings of the Thirty-Fi...· 0 citations
This work introduces a unified RL formulation that jointly optimizes agent and environment policies, where the environment policy learns graph edge costs to provide global movement guidance via backward Dijkstra search and achieves significant improvements over the strong search-based planner, Causal-PIBT, across multiple high-density maps.
He Jiang, Jingtian Yan, Yulun Zhang et al.· 0 citations
Sampling-based motion planning algorithms have been extensively adopted for the global path planning of mobile robots and industrial manipulators in complex static environments, owing to their probabilistic completeness and computational scalability in high-dimensional configuration space. However, most existing variants predominantly rely on single optimization strategy and multi-strategy fusion approaches often suffer from insufficient collaborative design among modules. These limitations hinder the ability to simultaneously balance exploration, convergence speed, and path quality, particularly in cluttered scenes with narrow passages. To address these challenges, this study proposes a novel multi-strategy integrated RRT* (M-RRT*) path planning framework that enables coordinated optimization across all modules. First, a three-layer hybrid sampling strategy is designed, combining goal-biased sampling, obstacle Gaussian sampling, and uniform global sampling to adaptively balance global exploration and local convergence efficiency. Second, an adaptive bidirectional tree expansion mechanism can dynamically adjust the two trees expansion state. Third, a three-point local shortcut optimization serves as the post-processing module to further refine the path quality. Extensive comparative experiments are conducted across three typical 2D environments: cluttered obstacles, maze, and narrow passages environments. The results demonstrate that, compared to RRT*, the M-RRT* reduces average path length by 24.1%, sampling nodes by 84.2%, average planning time by 83.0%, and maintains a 100% success rate across all test environments—notably enhancing planning reliability in complex constrained environments. When benchmarked against Informed-RRT* and B-RRT*, M-RRT* achieves a superior equilibrium between efficiency, success rate, and path quality, exhibiting more pronounced comprehensive advantages in complex maze and narrow passage scenarios. These quantitative results validate that the M-RRT* surpasses existing baselines in overall performance and holds application potential for AGVs, inspection robots, and industrial logistics equipment.
Jian Liu, Bo Tao, Du Jiang et al.· Engineering Research Express· 0 citations
Robotic manipulators operating in cluttered environments require collision-free trajectories that remain executable under kinematic and dynamic constraints. This paper proposes a reinforcement learning (RL)-guided multi-objective trajectory planning framework, termed RL-MOP-HNE, for a 6-DOF UR5 manipulator. The planning model simultaneously minimizes path length, energy consumption, and execution time while satisfying collision-avoidance, kinematic, and dynamic constraints. A tabular SARSA agent is embedded into the evolutionary search to adaptively select search behaviours according to the current optimization state. To improve the balance between exploration and exploitation, a Gaussian-perturbation adaptive hybrid crossover operator is integrated with a hierarchical neighborhood evolution (HNE) strategy, enabling progressive population refinement throughout the search process. The proposed method is evaluated in three representative environments with increasing planning complexity, including single-obstacle, narrow three-obstacle, and irregular five-obstacle scenarios, and is compared with MOEA/D, MOPSO, MSCLPSO, NSGA-II, and RL-NSGA-II. Experimental results show that RL-MOP-HNE generates feasible trajectories in all test cases and achieves the lowest dynamic-stability-prioritized composite scores among the compared algorithms. The planned trajectories exhibit smoother joint motion and lower velocity fluctuations, although these improvements are generally accompanied by longer execution times. Complementary analyses, including time scaling, manipulability, clearance evaluation, statistical significance tests, and ablation studies, further explain the performance characteristics of the proposed framework and quantify the contribution of its key components. The proposed framework is therefore well suited to robotic applications where motion stability and dynamic executability are of greater importance than minimum-time operation.
Zhen-Long Zhao, Shu-Tao Hao, Bi-Hao Jin et al.· Scientific Reports· 0 citations
It is argued that the future of robot path planning will be dominated by hybrid systems that combine global planning, local replanning, optimization, and learning-based prediction, enabling robots to operate more safely, intelligently, and adaptively in complex real-world environments.
Chanyu Wang· Theoretical and Natural Scie...· 0 citations
A softmin-based adaptive blending mechanism that automatically selects the most suitable path from the champion solutions on the Pareto front according to regional environmental conditions, thereby eliminating the need for manual user intervention is introduced.
Osman Emre Turan, Oğuz Mısır, Mustafa Özden· Measurement science and tech...· 0 citations
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