Model predictive path integral for decentralized multi-agent collision avoidance.

ABSTRACT: Collision avoidance is a crucial component of any decentralized multi-agent navigation system. Currently, most of the existing multi-agent collision-avoidance methods either do not take into account the kinematic constraints of the agents (i.e., they assume that an agent might change the direction of movement instantaneously) or are tailored to specific kinematic motion models (e.g., car-like robots). In this work, we suggest a novel generalized approach to decentralized multi-agent collision-avoidance that can be applied to agents with arbitrary affine kinematic motion models, including but not limited to differential-drive robots, car-like robots, quadrotors, etc. The suggested approach is based on the seminal sampling-based model predictive control algorithm, i.e

SUBMITTER: Dergachev S 

PROVIDER: S-EPMC11639165 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature