An enhanced RRT-Connect algorithm has been developed to improve path planning for robotic arms by optimizing obstacle avoidance and path efficiency.
The study presents several improvements to the RRT-Connect algorithm aimed at optimizing obstacle avoidance for robotic arms, including target biasing, elliptic space sampling, and the incorporation of segmented Bézier curves for smoother trajectories.
The study was conducted by Miaolong Cao, Huawei Mao, Xiaohui Tang, Yuzhou Sun, and Tiandong Cheng, affiliated with relevant institutions.
The article was published recently as indicated by the citation (2025).
The research has been conducted within the domain of robotic arm path planning, with experiments carried out using MATLAB and various simulated environments.
The enhancements aim to address limitations seen with the original RRT-Connect algorithm, improving both the efficiency and safety of obstacle avoidance mechanisms.
Key techniques used include target bias sampling, adaptation of cost functions to incorporate obstacle interaction, variable step-size expansion using artificial potential fields, and final smoothing of paths using Bézier curves.
The enhanced RRT-Connect algorithm showed a 19.39% reduction in average run time and 5% decrease in average path length compared to the existing algorithm.
“The enhanced algorithm meets the requirement for optimal obstacle avoidance path planning by consistently finding the shortest path.”
“Segments of the path can be adjusted more finely, allowing for smoother and more controlled paths.”
The article will begin by discussing the challenges of path planning for robotic arms and the importance of effective collision avoidance. It will introduce the enhanced RRT-Connect algorithm and its significance, emphasizing the study's objective.
This section will provide insight on the original RRT-Connect algorithm and previous optimizations, highlighting the necessity for improvements to meet modern robotic challenges.
The distinctive techniques developed will be described, including target biasing, elliptical sampling, and the revised cost function. Important facts and the methodology will be explained.
The results from simulations demonstrating reductions in runtime and path length will be presented, underscoring the effectiveness of the improved algorithm compared to its predecessor.
The article will close with reflections on the enhanced algorithm's potential impacts on future robotic applications, summarizing achievements and suggested areas for future research and exploration.