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Multi-robot Task Allocation and Path Planning with Maximum Range Constraints

Gang Xu, Yuchen Wu, Sheng Tao, Yifan Yang, Tao Liu, Tao Huang, Huifeng Wu, Yong Liu

TL;DR

This work tackles multi-robot task allocation and path planning in large-scale environments while enforcing robots’ maximum endurance constraints. It introduces RangeTAP, a pipeline that couples obstacle-aware path planning with auction-based task allocation, enabled by a fast continuous-space global path planner called Global-GOS and a lazy auction strategy to accelerate convergence. The Global-GOS planner generates obstacle-avoidance guidance points from inflated polygonal obstacles, achieving near-shortest paths without grid maps and with complexity roughly $O(k\times n)$. By integrating path planning into the auction phase and recasting bids with obstacle-avoidance path lengths, RangeTAP ensures feasibility under endurance limits and improves computation time, demonstrated through extensive simulations and real-world experiments. Overall, the method provides a practical, scalable solution for large robotic fleets operating under range constraints, with strong performance gains over prior approaches.

Abstract

This letter presents a novel multi-robot task allocation and path planning method that considers robots' maximum range constraints in large-sized workspaces, enabling robots to complete the assigned tasks within their range limits. Firstly, we developed a fast path planner to solve global paths efficiently. Subsequently, we propose an innovative auction-based approach that integrates our path planner into the auction phase for reward computation while considering the robots' range limits. This method accounts for extra obstacle-avoiding travel distances rather than ideal straight-line distances, resolving the coupling between task allocation and path planning. Additionally, to avoid redundant computations during iterations, we implemented a lazy auction strategy to speed up the convergence of the task allocation. Finally, we validated the proposed method's effectiveness and application potential through extensive simulation and real-world experiments. The implementation code for our method will be available at https://github.com/wuuya1/RangeTAP.

Multi-robot Task Allocation and Path Planning with Maximum Range Constraints

TL;DR

This work tackles multi-robot task allocation and path planning in large-scale environments while enforcing robots’ maximum endurance constraints. It introduces RangeTAP, a pipeline that couples obstacle-aware path planning with auction-based task allocation, enabled by a fast continuous-space global path planner called Global-GOS and a lazy auction strategy to accelerate convergence. The Global-GOS planner generates obstacle-avoidance guidance points from inflated polygonal obstacles, achieving near-shortest paths without grid maps and with complexity roughly . By integrating path planning into the auction phase and recasting bids with obstacle-avoidance path lengths, RangeTAP ensures feasibility under endurance limits and improves computation time, demonstrated through extensive simulations and real-world experiments. Overall, the method provides a practical, scalable solution for large robotic fleets operating under range constraints, with strong performance gains over prior approaches.

Abstract

This letter presents a novel multi-robot task allocation and path planning method that considers robots' maximum range constraints in large-sized workspaces, enabling robots to complete the assigned tasks within their range limits. Firstly, we developed a fast path planner to solve global paths efficiently. Subsequently, we propose an innovative auction-based approach that integrates our path planner into the auction phase for reward computation while considering the robots' range limits. This method accounts for extra obstacle-avoiding travel distances rather than ideal straight-line distances, resolving the coupling between task allocation and path planning. Additionally, to avoid redundant computations during iterations, we implemented a lazy auction strategy to speed up the convergence of the task allocation. Finally, we validated the proposed method's effectiveness and application potential through extensive simulation and real-world experiments. The implementation code for our method will be available at https://github.com/wuuya1/RangeTAP.
Paper Structure (11 sections, 8 equations, 5 figures, 1 table, 2 algorithms)

This paper contains 11 sections, 8 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. RELATED WORK
  3. Problem Formulation
  4. METHODOLOGY
  5. Fast Path Planning
  6. Task Allocation Incorporated Path Planning
  7. Experiments
  8. Ablation Studies
  9. Simulation and Real-world Experiments
  10. Evaluations in Large-scale Scenarios
  11. Conclusion

Figures (5)

  • Figure 1: The geometric illustration of the proposed path planner, where $\mathbf{p}_{g^*}$ represents the optimal global guidance point obtained in the *th iteration.
  • Figure 2: The results of comparisons in path planning. (a) The small-siezd map. (b) The medium-siezd map. (c) The large-siezd map.
  • Figure 3: Experimental results for task allocation and path planning considering robots' maximum range limits: (a) Trajectories of all robots with the proposed RangeTAP. (b) The maximum remaining range for each robot under the proposed RangeTAP. (c) Trajectories of all robots with the LRGO. (d) The maximum remaining range for each robot under the LRGO.
  • Figure 4: The results of real-world experiment with our proposed RangeTAP: (a) The trajectories of 7 robots visiting 18 task areas. (b) Each robot's maximum remaining range after completing tasks and returning to their respective starting positions. More details can be found in the attached video at https://youtu.be/RY3WLkE3kZs.
  • Figure 5: The evaluation results in the large-sized scenario: (a) The average computation time of task allocation for all robot. (b) The average total travel distance required for robots to complete all tasks.