ArticleScientific reports2025
A novel RRT*-Connect algorithm for path planning on robotic arm collision avoidance.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Reinforcement learning-guided multi-objective trajectory planning for obstacle avoidance in robotic manipulators.Scientific reports · 2026Article
- SAI-RRT*: Safety-Aware Informed RRT* for Multi-Joint Manipulator Path Planning in Static Environments.Sensors (Basel, Switzerland) · 2026Article
- Progress-driven sampling and multi-channel extension for bidirectional manipulator path planning in constrained environments.Scientific reports · 2026Article
- Bridge Points Guided Neural Motion Planning in Complex Environments with Narrow Passages.Sensors (Basel, Switzerland) · 2026Article
- Fuzzy adaptive nonlinear MIMO control for rigid coupled multibody robots using reinforcement learning model.Scientific reports · 2026Article
- Dynamic quality aware path planning for 6 DoF robotic arms using BiRRT and metaheuristic optimization based on B spline paths.Scientific reports · 2026Article
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Authors and funding
5 authors.
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Abstract
To address the limitations of the original algorithm, several optimization techniques are proposed. This article presents an original RRT*-Connect algorithm for the planning of obstacle avoidance paths on robotic arms. These strategies include implementing a target biasing algorithm, using elliptic space sampling to enhance the sampling process, the revision of the cost function to better guide path planning, and implementing an artificial potential field and gradient descent strategy to design adaptive step sizes. Furthermore, the use of segmented Bézier curves facilitates the generation of a more fluid trajectory when constructing the final path. The effectiveness of these augmentation strategies is corroborated by both simulations and experimental verification on a robotic arm. The simulations showed a 19.39% reduction in average run time and a 5% reduction in average path length compared to the existing RRT*-Connect algorithm. Therefore, The enhanced algorithm meets the requirement for optimal obstacle avoidance path planning by consistently finding the shortest path while avoiding obstacles.
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