Negotiation Framework for Safe Multi-Agent Planning via Spatiotemporal Tubes

This study presents a multi-agent negotiation-based framework to obtain collision-free paths while performing prescribed-time reach-avoid-stay (RAS) tasks for agents with unknown dynamics and bounded disturbance. By employing spatiotemporal tubes to generate time-varying state constraints, we ensure that all agents adhere to RAS specifications using synthesized controllers. To prevent inter-agent collisions, a negotiation mechanism is proposed where successful negotiations result in spatiotemporal tubes for each agent fulfilling desired tasks. This approach results in a completely distributed, approximation-free control law for each agent. The effectiveness of this mechanism was validated through simulations of multi-agent robot navigation and drone navigation tasks involving prescribed-time RAS specifications and collision avoidance.

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Publication Details

Published
2025-06-24
DOI
https://doi.org/10.23919/ecc65951.2025.11187220
Citations
3
Primary Topic
Multi-Agent Systems and Negotiation
Type
article
Field-Weighted Citation Impact
7.44
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article

Negotiation Framework for Safe Multi-Agent Planning via Spatiotemporal Tubes

Ravi Kumar L, Ratnangshu Das, Pushpak Jagtap, Mohd. Faizuddin Faruqui
3 citations
Multi-Agent Systems and Negotiation
7.44
article

Negotiation Framework for Safe Multi-Agent Planning via Spatiotemporal Tubes

Ravi Kumar L, Ratnangshu Das, Pushpak Jagtap, Mohd. Faizuddin Faruqui
article en
3 citations

Abstract

This study presents a multi-agent negotiation-based framework to obtain collision-free paths while performing prescribed-time reach-avoid-stay (RAS) tasks for agents with unknown dynamics and bounded disturbance. By employing spatiotemporal tubes to generate time-varying state constraints, we ensure that all agents adhere to RAS specifications using synthesized controllers. To prevent inter-agent collisions, a negotiation mechanism is proposed where successful negotiations result in spatiotemporal tubes for each agent fulfilling desired tasks. This approach results in a completely distributed, approximation-free control law for each agent. The effectiveness of this mechanism was validated through simulations of multi-agent robot navigation and drone navigation tasks involving prescribed-time RAS specifications and collision avoidance.

Indian Space Research Organisation (IN), Robert Bosch (China) (CN), Texas Instruments (Norway) (NO), Indian Institute of Science Bangalore (IN)
Openalex Percentile: Top 3%
Multi-Agent Systems and Negotiation
7.44
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Negotiation Framework for Safe Multi-Agent Planning via Spatiotemporal Tubes — Ravi Kumar L, Ratnangshu Das, et al. · (2025) | TGRS Research Map | TGRS