Simultaneous Path Planning and Task Allocation in Dynamic Environments

This paper addresses the challenge of coordinating task allocation and generating collision-free trajectories for a fleet of mobile robots in dynamic environments. Our approach introduces an integrated framework comprising a centralized task allocation system and a distributed trajectory planner. The centralized task allocation system, employing a heuristic approach, aims to minimize the maximum spatial cost among the slowest robots. Tasks and trajectories are continuously refined using a distributed version of CHOMP (Covariant Hamiltonian Optimization for Motion Planning), tailored for multiple-wheeled mobile robots where the spatial costs are derived from a high-level global path planner. By employing this combined methodology, we are able to achieve near-optimal solutions and collision-free trajectories with computational performance for up to 50 robots within seconds.

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Publication Details

Journal
Robotics
Published
2025-02-01
DOI
https://doi.org/10.3390/robotics14020017
Citations
2
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
2.55
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article

Simultaneous Path Planning and Task Allocation in Dynamic Environments

Jennifer David, Rafael Valencia
2 citations
Robotics
Robotic Path Planning Algorithms
2.55
article

Simultaneous Path Planning and Task Allocation in Dynamic Environments

Jennifer David, Rafael Valencia
article en
2 citations

Abstract

This paper addresses the challenge of coordinating task allocation and generating collision-free trajectories for a fleet of mobile robots in dynamic environments. Our approach introduces an integrated framework comprising a centralized task allocation system and a distributed trajectory planner. The centralized task allocation system, employing a heuristic approach, aims to minimize the maximum spatial cost among the slowest robots. Tasks and trajectories are continuously refined using a distributed version of CHOMP (Covariant Hamiltonian Optimization for Motion Planning), tailored for multiple-wheeled mobile robots where the spatial costs are derived from a high-level global path planner. By employing this combined methodology, we are able to achieve near-optimal solutions and collision-free trajectories with computational performance for up to 50 robots within seconds.

RoboticsVol. 14(2)
Halmstad University (SE)
Openalex Percentile: Top 13%
Robotic Path Planning Algorithms
2.55
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