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This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generated by RRT-connect. To check the proposed algorithm’s performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples.

This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

This article introduces a hybrid approach to enhance indoor pathfinding and navigation within complex multistory environments by integrating rapidly-exploring random tree (RRT)-Connect and Dijkstra’s algorithm. We propose a novel solution leveraging the strengths of RRT-connect for rapid path generation, combined with Dijkstra’s algorithm for refining and optimizing the final route. Our method leverages the rapid exploration of RRT—Connect while refining paths using Dijkstra’s algorithm, resulting in fewer nodes explored compared to Lazy Theta* while maintaining efficiency. Experimental results demonstrate that our hybrid approach significantly reduces computational overhead, with RRT-Connect exploring approximately 1,750 nodes—outperforming RRT (2,000 nodes), RRT* (1,850 nodes), and Dijkstra (1,780 nodes). The algorithm achieves up to 50% faster execution in narrow spaces compared to traditional RRT, making it well-suited for real-time navigation. Additionally, parallel processing optimizes performance, ensuring efficient pathfinding in dynamic environments. A Next.js-based frontend visualization system further enhances usability by rendering path nodes in real time. This hybrid approach balances rapid exploration, optimal path computation, and computational efficiency, making it a robust solution for indoor navigation in large-scale and complex environments.

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency. During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path. A pruning optimization strategy is also proposed to eliminate the redundant nodes from the path. Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path. Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm. By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation.

This paper addresses the path planning problem in high-dimensional complex environments and proposes an improved FRRT*-Connect algorithm to enhance the efficiency, precision, and robustness of path generation. The algorithm first introduces a goal-directed attractive force control mechanism, integrating artificial potential field methods to guide the tree expansion more effectively toward the goal, thereby reducing redundant sampling and significantly improving convergence speed. Secondly, an adaptive step-size strategy is proposed, dynamically adjusting the tree expansion step size based on the complexity of the environment, which enhances the algorithm’s adaptability in narrow passages and complex topological structures, effectively avoiding local minima. The results show that, compared to the RRT*-Connect algorithm, the proposed method exhibits significant advantages in path quality, convergence efficiency, and success rate: the average path length is reduced by 19.7%, convergence speed is improved by 58.4%, and the success rate reaches 98% in narrow passage scenarios. These improvements effectively overcome the issues of path redundancy, slow convergence, and local minima inherent in traditional RRT-based algorithms, demonstrating superior performance in challenging scenarios with complex obstacles and narrow passages.

The RRT*-Connect algorithm enhances efficiency through dual tree bias growth, yet this bias can be inherently blind, potentially affecting the algorithm’s heuristic performance. In contrast, the Informed RRT* algorithm narrows the planning problem’s scope by leveraging an informed region, thereby improving convergence efficiency towards optimal solutions. However, this approach relies on the prior establishment of feasible paths. Combining these two algorithms can address the challenges posed by Informed RRT while also accelerating convergence towards optimality, albeit without resolving the issue of blind bias in dual trees.In this paper, we proposed a novel algorithm: Dynamic Informed Bias RRT*-Connect. This algorithm, grounded in potential and explicit informed bias sampling, introduces a dynamical bias points set that guides dual tree growth with precision objectives. Additionally, we enhance the evaluation framework for algorithmic heuristics by introducing two innovative metrics that effectively capture the algorithm’s characteristics. The improvements observed in traditional indicators demonstrate that the proposed algorithm exhibits greater heuristic compared to RRT*-Connect and Informed RRT*-Connect. These findings also suggest the viability of the new metrics introduced in our evaluation framework.

Winter jujube harvesting is traditionally labor-intensive, yet declining labor availability and rising costs necessitate robotic automation to maintain agricultural competitiveness. Path planning for robotic arms in orchards faces challenges due to the unstructured, dynamic environment containing densely packed fruits and branches. To overcome the limitations of existing robotic path planning methods, this research proposes BMGA-RRT Connect (BVH-based Multilevel-step Gradient-descent Adaptive RRT), a novel algorithm integrating adaptive multilevel step-sizing, hierarchical Bounding Volume Hierarchy (BVH)-based collision detection, and gradient-descent path smoothing. Initially, an adaptive step-size strategy dynamically adjusts node expansions, optimizing efficiency and avoiding collisions; subsequently, a hierarchical BVH improves collision-detection speed, significantly reducing computational time; finally, gradient-descent smoothing enhances trajectory continuity and path quality. Comprehensive 2D and 3D simulation experiments, dynamic obstacle validations, and real-world winter jujube harvesting trials were conducted to assess algorithm performance. Results showed that BMGA-RRT Connect significantly reduced average computation time to 2.23 s (2D) and 7.12 s (3D), outperforming traditional algorithms in path quality, stability, and robustness. Specifically, BMGA-RRT Connect achieved 100% path planning success and 90% execution success in robotic harvesting tests. These findings demonstrate that BMGA-RRT Connect provides an efficient, stable, and reliable solution for robotic harvesting in complex, unstructured agricultural settings, offering substantial promise for practical deployment in precision agriculture.

Pruning branches of apple trees is a labor-intensive task. Pruning robots can save manpower and reduce costs. A full map of the apple orchard with collision-free paths, which is navigation planning, is essential. To improve the navigation efficiency of the apple tree pruning robot, an improved RRT-Connect algorithm was proposed. Firstly, to address the disadvantage of randomness in the expansion of the RRT-Connect algorithm, a goal-biased strategy was introduced. Secondly, to shorten the path length, the mechanism of the nearest node selection was optimized. Finally, the path was optimized where path redundancy nodes were removed, and Bezier curves were used to deal with path sharp nodes to further reduce the path length and improve the path smoothness. The experimental results of apple orchard navigation show that the improved algorithm proposed in this paper can cover the whole apple orchard, and the path length is 32% shorter than that of the RRT-Connect algorithm. The overall navigation time is 35% shorter than that of the RRT-Connect algorithm. This shows that the improved algorithm has better adaptability and planning efficiency in the apple orchard environment. This will contribute to the automation of orchard operations and provide valuable references for future research on orchard path planning.