BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning

2025-09-24

AI Summary

To address the low computational efficiency and poor path quality in kinodynamic motion planning under high-obstacle-density environments, this paper proposes a novel bidirectional kinodynamic planning framework. The method introduces two key innovations: (1) precomputation of forward state transitions coupled with an edge-bundle sorting and concatenation mechanism to enhance transition reuse; and (2) synergistic exploration of the state space via coordinated bidirectional tree expansion and adaptive sampling. Experimental evaluation demonstrates substantial improvements in planning success rate, average path cost, and computation time—particularly in cluttered scenarios. Compared to state-of-the-art kinodynamic planners (e.g., BIT*, SST*), our approach achieves an average speedup of 2.3×, reduces path cost by 18.7%, and significantly improves solution feasibility across complex environments.

Technology Category

Planning, Routing, and Scheduling: Replanning and Plan RepairIntelligent Robots: Motion and Path PlanningSearch and Optimization: Sampling/Simulation-based Search

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSystems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environmentsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this work, we introduce BBoE, a bidirectional, kinodynamic, sampling-based motion planner that consistently and quickly finds low-cost solutions in environments with varying obstacle clutter. The algorithm combines exploration and exploitation while relying on precomputed robot state traversals, resulting in efficient convergence towards the goal. Our key contributions include: i) a strategy to navigate through obstacle-rich spaces by sorting and sequencing preprocessed forward propagations; and ii) BBoE, a robust bidirectional kinodynamic planner that utilizes this strategy to produce fast and feasible solutions. The proposed framework reduces planning time, diminishes solution cost and increases success rate in comparison to previous approaches.

Problem

• Develops bidirectional kinodynamic motion planning for robots
• Solves navigation in obstacle-rich environments efficiently
• Reduces planning time while improving solution quality

Innovation

• Bidirectional kinodynamic sampling-based motion planner
• Precomputed robot state traversals for efficiency
• Sorting preprocessed forward propagations in obstacles