Path Planning for Robotic Arm in Catenary Maintenance: An Improved RRT Algorithm Based on Obstacle Node

OS-RRT: a path planning algorithm for mobile robots based on optimized sampling

Path planning for robotic arms using an improved Q-RRT* algorithm in a digital twin system

This study addresses the shortcomings of the traditional RRT* algorithm in path planning, such as high path costs and slow convergence speeds, and proposes an improved algorithm with dual-tree collaborative characteristics—KQ-RRT*-Connect. The algorithm builds upon the Q-RRT* framework by designing a dual random tree target-biased sampling strategy, dynamically adjusting the sampling probability density, employing a KD-tree spatial index structure to optimize nearest neighbor queries, introducing a bidirectional midpoint optimization model, and combining a second-order Bessel curve to optimize paths. Through these multi-optimization mechanisms, the algorithm simultaneously enhances the efficiency and quality of path planning. The algorithm has been validated through two-dimensional and three-dimensional environment simulations in MATLAB and further experimentally verified on a masonry robot digital twin platform and a UR10 robotic arm system. Experimental results show that the algorithm effectively suppresses exploration of invalid regions, reducing the time complexity of neighborhood search from O ( n ) to O (log n ), and improves path continuity by reducing redundant nodes; The proposed modifications reduced the initial path calculation time of O-RRT* by 90%, reduced the number of iterations required by 85%, and reduced the path cost by more than 4.5%. It also demonstrates strong robustness and promising engineering application prospects.

Hierarchical planning method for mobile robot path-following in dynamic complex environments

A node detection strategy grounded in the safe workspace effectively prevents collisions between the generated path and surrounding obstacles, and a two-stage heuristic search strategy is designed, incorporating an intermediate node mechanism to substantially enhance search efficiency.

Research on Robot Path Planning Based on Multi-Strategy Improved RRT* Algorithm

To tackle challenges like route redundancy, prolonged computation duration, and convoluted paths within the RRT* algorithm for robot path planning, this research proposes a multi-strategy enhanced APF-RRT* algorithm termed MSAP-RRT*. In order to decrease the number of sampling points and increase the effectiveness of path planning, this method first combines the RRT* algorithm with a dynamic target-biased sampling strategy. Second, to improve the repulsive potential field function in the artificial potential field, obstacle density and the target distance adjustment factor are contained. This enables real-time adjustment of the repulsive force magnitude to improve the quality of path planning. To speed up convergence and cut down on running time, an adaptive step size method based on obstacle density and target distance adjustment factor is then presented. Lastly, cubic B-spline curves and greedy pruning are combined to optimize the original path, reducing its length and enhancing its quality. Simulation comparative analyses conducted in typical scenarios show that the MSAP-RRT* algorithm reduces the average path length by 6.49%, the average runtime by 82.10%, the average number of nodes by 90.70%, and the average number of iterations by 85.20% when compared to the improved APF-RRT* algorithm. Robots are strongly guaranteed to create safe and effective routes in areas with lots of obstacles according to the MSAP-RRT* algorithm.

An Obstacle Avoidance Path Planning Method Based on Improved OPSN for Robotic Arm

This paper proposes a three-dimensional obstacle avoidance path planning method for a single-arm manipulator based on an improved Optimization Problem Solving Network (OPSN). To address the difficulties caused by non-convex search spaces, complex obstacle constraints, and the poor performance of conventional swarm intelligence algorithms in narrow feasible regions, the end-effector trajectory is modeled as a polyline with fixed start and goal points and several intermediate waypoints. Path length, trajectory smoothness, and task-related height preference are jointly incorporated into the objective function, while workspace boundary constraints, obstacle safety distance constraints, and minimum height constraints are explicitly embedded into the network structure. In addition, an elite-initialization strategy is introduced to improve the original OPSN, whose initial inputs are purely random and cannot exploit useful historical information across restarts. The proposed strategy maintains exploration in the early stage and generates new initializations from an elite pool in the later stage through adaptive perturbation and weighted combination. Comparative experiments in three representative scenarios show that the improved OPSN achieves superior or competitive overall performance, especially in narrow-passage environments, where it exhibits stronger feasible-solution search capability and shorter planned paths.

Hierarchical Path Planning for On‐Board Mechanical Arm Construction Robots Based on I‐Bi‐RRT‐APF and LLM‐DT

Path planning for construction robots in complex construction environments suffers from insufficient adaptability, poor path quality, and high computational cost. This paper proposes a hierarchical path planning method based on improved‐bidirectional‐rapidly‐exploring random tree‐artificial potential field. The proposed method integrates the following key techniques: (1) a semantically enhanced unified bounding volume hierarchy collision detection mechanism for identifying multiple categories of construction obstacle; (2) goal‐biased sampling and dynamic step size to enhance global search efficiency, combined with a modified potential field to improve obstacle avoidance; turning‐angle constraints and B‐spline smoothing are applied to optimize trajectory feasibility; (3) a weakly coupled mechanism model of the mobile vehicle and the robotic arm to reduce computational complexity. Digital twin and large language model are introduced to further enhance the engineering practicality. Ablation experiments are conducted in multiple simulation scenarios based on a scaled cable‐truss structure, demonstrating the superiority of the method in effectiveness and efficiency.