Real-Time Fast Marching Tree for Mobile Robot Motion Planning in Dynamic Environments

2023-05-29

IEEE International Conference on Robotics and Automation

AI Summary

This paper addresses the challenge of real-time motion planning for mobile robots in dynamic environments. We propose the Real-Time Fast Marching Tree (RT-FMT) algorithm, which integrates FMT* with the RT-RRT* framework to establish a hybrid planning paradigm combining sampling-based exploration, incremental tree growth, and real-time rewiring. Our key contribution is a novel dynamic tree rewiring mechanism that enables the tree root to track the robot’s pose in real time while actively avoiding dynamic obstacles. RT-FMT supports coordinated generation of local and global paths, multi-goal path reuse, and prioritized execution of local trajectories. Simulation results demonstrate that, compared to RT-RRT*, RT-FMT reduces path execution cost by 12%–28%, decreases average arrival time by 19%, and significantly enhances both dynamic obstacle avoidance capability and planning timeliness.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper proposes the Real-Time Fast Marching Tree (RT-FMT), a real-time planning algorithm that features local and global path generation, multiple-query planning, and dynamic obstacle avoidance. During the search, RT-FMT quickly looks for the global solution and, in the meantime, generates local paths that can be used by the robot to start execution faster. In addition, our algorithm constantly rewires the tree to keep branches from forming inside the dynamic obstacles and to maintain the tree root near the robot, which allows the tree to be reused multiple times for different goals. Our algorithm is based on the planners Fast Marching Tree (FMT*) and Real-time Rapidly-Exploring Random Tree (RT-RRT*). We show via simulations that RT-FMT outperforms RT- RRT* in both execution cost and arrival time, in most cases. Moreover, we also demonstrate via simulation that it is worthwhile taking the local path before the global path is available in order to reduce arrival time, even though there is a small possibility of taking an inferior path.

Problem

• Real-time motion planning for mobile robots
• Dynamic obstacle avoidance in environments
• Efficient global and local path generation

Innovation

• Real-Time Fast Marching Tree
• Dynamic Obstacle Avoidance
• Multiple-Query Planning