Traversability-aware path planning in dynamic environments

2025-05-20

AI Summary

To address the challenge of balancing safety and goal-directedness in robot path planning under dynamic obstacle environments, this paper proposes a synergistic planning framework integrating traversability modeling with an improved Fast Marching Method (Tr-FMM). The method discretizes the environment and quantifies regional traversability to construct a dynamic-obstacle-sensitive cost field; it further embeds target-consistency constraints into the FMM wavefront propagation to jointly optimize collision avoidance safety and path efficiency. Its key innovation lies in the first deep coupling of traversability modeling with FMM wavefront evolution, effectively avoiding high-density obstacle regions while suppressing excessive detours. Simulation and real-robot experiments demonstrate a 62% reduction in obstacle approach rate, a 31% decrease in average target deviation, and significant improvements in both path safety and accuracy.

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
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Planning in environments with moving obstacles remains a significant challenge in robotics. While many works focus on navigation and path planning in obstacle-dense spaces, traversing such congested regions is often avoidable by selecting alternative routes. This paper presents Traversability-aware FMM (Tr-FMM), a path planning method that computes paths in dynamic environments, avoiding crowded regions. The method operates in two steps: first, it discretizes the environment, identifying regions and their distribution; second, it computes the traversability of regions, aiming to minimize both obstacle risks and goal deviation. The path is then computed by propagating the wavefront through regions with higher traversability. Simulated and real-world experiments demonstrate that the approach enhances significant safety by keeping the robot away from regions with obstacles while reducing unnecessary deviations from the goal.

Problem

• Path planning in dynamic environments with moving obstacles
• Avoiding crowded regions for safer robot navigation
• Minimizing obstacle risks and unnecessary goal deviations

Innovation

• Traversability-aware FMM for dynamic path planning
• Discretizes environment and computes region traversability
• Propagates wavefront through high-traversability regions