Spatiotemporal Co-Design Enabling Prioritized Multi-Agent Motion Planning

This paper introduces an innovative planner for prioritized multi-agent motion planning, employing a sequential integration of spatial and temporal designs. The planner initiates a smooth trajectory in space for each agent, ignoring the presence of other agents. Subsequently, by treating spatial collisions as 2D obstacles from a temporal perspective, the planner dynamically fine-tunes the trajectory-tracking speed of agents to avoid collisions, ensuring optimal time consumption for the last agent to reach the target as well. Additionally, the proposed approach systematically coordinates priority for each agent. The efficacy of the approach is validated through both simulations and comparative experiments with a recent planner.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10801559
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.25
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article

Spatiotemporal Co-Design Enabling Prioritized Multi-Agent Motion Planning

Yiannis Kantaros, Yunshen Huang, Wenbo He, Shen Zeng
1 citations
Robotic Path Planning Algorithms
0.25
article

Spatiotemporal Co-Design Enabling Prioritized Multi-Agent Motion Planning

Yiannis Kantaros, Yunshen Huang, Wenbo He, Shen Zeng
article en
1 citations

Abstract

This paper introduces an innovative planner for prioritized multi-agent motion planning, employing a sequential integration of spatial and temporal designs. The planner initiates a smooth trajectory in space for each agent, ignoring the presence of other agents. Subsequently, by treating spatial collisions as 2D obstacles from a temporal perspective, the planner dynamically fine-tunes the trajectory-tracking speed of agents to avoid collisions, ensuring optimal time consumption for the last agent to reach the target as well. Additionally, the proposed approach systematically coordinates priority for each agent. The efficacy of the approach is validated through both simulations and comparative experiments with a recent planner.

Openalex Percentile: Top 43%
Robotic Path Planning Algorithms
0.25
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