2026-10-14T06:47:10Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33816805 2026-09-15T18:21:03Z category_146 category_734 category_931 category_106 portal_5 item_type_3 month_year_09_2026

<p>Physical robot parameters of Jetauto platform.</p> Guogang Wang (9258169) Hongwei Sun (422270) Zichao Feng (5539031) Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy obstacle densities ranging jetauto robot platform improved bidirectional rapidly historical trajectory information hierarchical architecture establishes exploring random tree enable smoother avoidance artificial potential field 89 – 97 88 – 93 25 %, ros improve path quality hybrid path planning sampling point strategy reduce sampling redundancy local obstacle avoidance mobile robot navigation dynamic obstacle prediction turning maneuvers compared dwa evaluation function global path optimization local dwa controller div >< p computation time compared fusing improved bi rrt algorithm incorporates standard rrt *, path length sampling nodes local execution mobile robots execution time dynamic step global guidance global bi rrt *, size adjustment physical experiments paper proposes moving obstacles maintaining 100 experimental validation continuous transition complex environments based tests <div><p>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 (5050, 100100, and 200200) 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.</p></div> 2026-09-15T18:20:25Z Dataset Dataset 10.1371/journal.pone.0357770.t010 https://figshare.com/articles/dataset/_p_Physical_robot_parameters_of_Jetauto_platform_p_/33816805 CC BY 4.0