InfoFusionコントローラ：相互情報量に基づくInformed TRRT*とPure Pursuit・MPC融合による経路計画強化
InfoFusion Controller: Informed TRRT Star with Mutual Information based on Fusion of Pure Pursuit and MPC for Enhanced Path Planning
都市環境での自動運転向けに、大域的計画にInformed-TRRT*、局所的計画にMPCとPure Pursuitを相互情報量で融合する経路計画手法を提案し、安全性・安定性・効率を向上させた。
著者: Seongjun Choi, Youngbum Kim, Nam Woo Kim, Mansun Shin, Byunggi Chae, Sungjin Lee
分類: cs.RO, cs.MS
原文アブストラクト
In this paper, we propose the InfoFusion Controller, an advanced path planning algorithm that integrates both global and local planning strategies to enhance autonomous driving in complex urban environments. The global planner utilizes the informed Theta-Rapidly-exploring Random Tree Star (Informed-TRRT*) algorithm to generate an optimal reference path, while the local planner combines Model Predictive Control (MPC) and Pure Pursuit algorithms. Mutual Information (MI) is employed to fuse the outputs of the MPC and Pure Pursuit controllers, effectively balancing their strengths and compensating for their weaknesses. The proposed method addresses the challenges of navigating in dynamic environments with unpredictable obstacles by reducing uncertainty in local path planning and improving dynamic obstacle avoidance capabilities. Experimental results demonstrate that the InfoFusion Controller outperforms traditional methods in terms of safety, stability, and efficiency across various scenarios, including complex maps generated using SLAM techniques. The code for the InfoFusion Controller is available at https: //github.com/DrawingProcess/InfoFusionController.