ソーシャルゾノトープネットワークとモデル予測制御による社会的に受容される二足歩行ロボットナビゲーション
Socially Acceptable Bipedal Robot Navigation via Social Zonotope Network Model Predictive Control
歩行者との相互影響を考慮し、歩行者の将来到達可能領域とロボット自身の社会的に受容される到達可能領域をゾノトープとして予測するニューラルネットワークSZNを提案し、MPCと統合して二足歩行ロボットDigitの足取り計画を行うフレームワークを実現した。
著者: Abdulaziz Shamsah, Krishanu Agarwal, Nigam Katta, Abirath Raju, Shreyas Kousik, Ye Zhao
分類: cs.RO
原文アブストラクト
This study addresses the challenge of social bipedal navigation in a dynamic, human-crowded environment, a research area largely underexplored in legged robot navigation. We present a zonotope-based framework that couples prediction and motion planning for a bipedal ego-agent to account for bidirectional influence with the surrounding pedestrians. This framework incorporates a Social Zonotope Network (SZN), a neural network that predicts future pedestrian reachable sets and plans future socially acceptable reachable set for the ego-agent. SZN generates the reachable sets as zonotopes for efficient reachability-based planning, collision checking, and online uncertainty parameterization. Locomotion-specific losses are added to the SZN training process to adhere to the dynamic limits of the bipedal robot that are not explicitly present in the human crowds data set. These loss functions enable the SZN to generate locomotion paths that are more dynamically feasible for improved tracking. SZN is integrated with a Model Predictive Controller (SZN-MPC) for footstep planning for our bipedal robot Digit. SZN-MPC solves for collision-free trajectory by optimizing through SZN's gradients. and Our results demonstrate the framework's effectiveness in producing a socially acceptable path, with consistent locomotion velocity, and optimality. The SZN-MPC framework is validated with extensive simulations and hardware experiments.
