Hybrid path planning for mobile robots in complex environments: Fusing improved BI-RRT and enhanced DWA

Guogang Wang, Hongwei Sun and Zichao Feng

PLOS ONE, 2026, vol. 21, issue 9, 1-50

Abstract: Mobile robot navigation in complex environments requires efficient global planning and reactive local obstacle avoidance. This paper proposes a hybrid path planning algorithm that integrates an improved Bidirectional Rapidly-exploring Random Tree (BI-RRT) for global guidance with an enhanced Dynamic Window Approach (DWA) for local execution. The BI-RRT algorithm incorporates a multi-sampling point strategy, dynamic step-size adjustment, and an Artificial Potential Field (APF)-inspired node selection mechanism to reduce sampling redundancy and improve path quality. The DWA evaluation function is augmented with historical trajectory information and dynamic obstacle prediction to enable smoother avoidance of moving obstacles. The hierarchical architecture establishes a continuous transition from global path optimization to local obstacle avoidance, with the global BI-RRT path providing waypoints for the local DWA controller. Experimental validation was conducted through MATLAB simulations across three environmental scales (50×50, 100×100, and 200×200) with obstacle densities ranging from 15% to 25%, ROS-based tests, and physical experiments on a Jetauto robot platform. Compared to baseline methods including RRT*, BI-RRT, APF-RRT*, and Informed-RRT*, the proposed algorithm improves search efficiency, achieving an 88–93% reduction in sampling nodes and 89–97% reduction in computation time compared to standard RRT*, while maintaining 100% success rate across tested environments. Physical robot experiments demonstrate 8.17% reduction in path length, 13.85% reduction in execution time, and 40% reduction in turning maneuvers compared to conventional BI-RRT. The algorithm maintains highly reliable navigation performance across the specifically evaluated test environments, including dynamic obstacle scenarios, demonstrating its viability under the tested conditions.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0357770

DOI: 10.1371/journal.pone.0357770

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