Don't Get Stuck: A Deadlock Recovery Approach

When multiple agents share space, interactions can lead to deadlocks, where no agent can advance towards its goal. This paper addresses this challenge with a deadlock recovery strategy. In particular, the proposed algorithm integrates hybrid-A$^\star$, STL, and MPPI frameworks. Specifically, hybrid-...

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Bibliographic Details
Main Authors Baldini, Francesca, Tariq, Faizan M, Bae, Sangjae, Isele, David
Format Journal Article
LanguageEnglish
Published 19.08.2024
Subjects
Computer Science - Robotics
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Summary:When multiple agents share space, interactions can lead to deadlocks, where no agent can advance towards its goal. This paper addresses this challenge with a deadlock recovery strategy. In particular, the proposed algorithm integrates hybrid-A$^\star$, STL, and MPPI frameworks. Specifically, hybrid-A$^\star$ generates a reference path, STL defines a goal (deadlock avoidance) and associated constraints (w.r.t. traffic rules), and MPPI refines the path and speed accordingly. This STL-MPPI framework ensures system compliance to specifications and dynamics while ensuring the safety of the resulting maneuvers, indicating a strong potential for application to complex traffic scenarios (and rules) in practice. Validation studies are conducted in simulations and on scaled cars, respectively, to demonstrate the effectiveness of the proposed algorithm.
DOI:10.48550/arxiv.2408.10167