Interactive Multi-Modal Motion Planning With Branch Model Predictive Control

نویسندگان

چکیده

Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors must be considered. Since usually demonstrate multimodal behavior, motion planner needs to solve continuous under these behaviors, which contains discrete element. We propose branch Model Predictive Control (MPC) framework that plans over feedback policies leverage behavior agent. In particular, scenario tree is constructed from finite set agent, MPC solves policy form trajectory tree, shares same topology tree. Moreover, coherent risk measures such Conditional Value at Risk (CVaR) are used tuning knob adjust tradeoff between performance robustness. The proposed tested on an vehicle simulation, quadruped robot alongside experiments. result demonstrates interesting human-like achieving balance safety performance.

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ژورنال

عنوان ژورنال: IEEE robotics and automation letters

سال: 2022

ISSN: ['2377-3766']

DOI: https://doi.org/10.1109/lra.2022.3156648