Aug 2026· Machines· Vol 14, pp. 925· 0 citations· 19 references
TL;DR
The experimental results demonstrate that the proposed Field-guided RRT achieves a better balance among path efficiency, planning time, obstacle-clearance maintenance, and trajectory execution capability compared with conventional RRT-based methods.
Abstract
End-effector obstacle-avoidance trajectory planning is essential for improving the autonomy, safety, and executability of industrial robotic manipulators in constrained workspaces. Conventional Rapidly Exploring Random Tree (RRT) planners provide effective exploration capability but often suffer from stochastic tree expansion, redundant trajectories, and insufficient directional guidance near obstacle regions, which limits planning efficiency and trajectory quality. This study proposes a clearance-field-guided RRT framework with behavior-cloning-assisted refinement for end-effector obstacle-avoidance trajectory planning of industrial robotic manipulators. The proposed method formulates the planning problem in Cartesian space based on an end-effector kinematic model and introduces local clearance-field guidance into the RRT sampling process. Candidate samples are evaluated by considering obstacle clearance, reference-line deviation, and goal distance, enabling the search tree to preferentially expand toward effective traversable regions while maintaining the exploration capability of conventional RRT. Behavior cloning is further introduced as an offline auxiliary strategy to investigate the influence of expert trajectories on local motion-direction learning and trajectory continuity. A Python–Unity joint simulation–verification framework and a physical manipulator experimental platform are established to evaluate the feasibility and practical executability of the generated trajectories. Python is used for offline trajectory generation, expert dataset construction, behavior-cloning training, and performance evaluation, while Unity is employed for three-dimensional manipulator modeling and trajectory reproduction. The experimental results demonstrate that the proposed Field-guided RRT achieves a better balance among path efficiency, planning time, obstacle-clearance maintenance, and trajectory execution capability compared with conventional RRT-based methods. The proposed framework provides an effective solution for collision-free end-effector trajectory planning in industrial applications such as assembly, welding, component placement, and robotic inspection.
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.
Jianhan Fan, C. Peng, Jianxiao Zou et al.· 2026 IEEE International Conf...· 0 citations
This study proposes a novel real-time trajectory planning framework designed to address the complex navigation challenges of collaborative robots operating in shared workspaces. The developed framework effectively integrates Model Predictive Path Integral (MPPI) control with Artificial Potential Field (APF) mechanisms to ensure robust and adaptive motion generation. To comprehensively evaluate its performance, the approach was validated in diverse environments, including no obstacle, high multi obstacle, and wall configurations, using complex setups of two, three, and four UR5 manipulators. Throughout these extensive tests, the proposed algorithm consistently achieved a 100% success rate in real-time collision avoidance while strictly adhering to the inherent kinematic limits of the robots. Comparative analyses further highlighted the superior efficiency of this method. In the most challenging environment, the proposed framework outperformed a baseline jerk-and-acceleration controller by completing operations 21.3% faster with a 3.8% lower jerk, and significantly surpassed a pure acceleration controller with a 41% faster operation time alongside a 16.7% reduction in jerk.
Nezih Bora Yavas, Zafer Bingul· 2026 IEEE/ASME International...· 0 citations
To address the challenge of achieving efficient and safe autonomous navigation for mobile robots in complex dynamic environments, this paper proposes a hierarchical planning architecture based on point-by-point tracking control. A node detection strategy grounded in the safe workspace effectively prevents collisions between the generated path and surrounding obstacles. A two-stage heuristic search strategy is designed, incorporating an intermediate node mechanism to substantially enhance search efficiency. Furthermore, the potential field model of the Artificial Potential Field (APF) method is optimized, and a node attraction strategy is introduced to improve overall path quality. Simulation results demonstrate that, compared to baseline algorithms, the global planner achieves significant improvements in path length, computation time, and the number of sampling iterations. The local planner also exhibits superior performance in both computation time and path quality relative to other comparative algorithms. Finally, both ROS-based simulations and physical experiments validate that the proposed hierarchical planning framework delivers exceptional path planning efficiency and obstacle avoidance capability in complex dynamic environments.
Xinguang Li, Shilong Zhao, Xiaoqin Guo· Proceedings of the Instituti...· 0 citations
This paper presents an obstacle-node-based Rapidly-exploring Random Tree (OB-RRT) algorithm for robotic arm path planning in constrained maintenance environments. The proposed method incorporates obstacle-node information derived from collision samples to guide tree expansion and improve exploration efficiency. The performance of OB-RRT is evaluated through simulations in 2D and 3D environments, with comparisons to RRT, GB-RRT, RRT-Connect, RRT*, and Informed-RRT*. The results indicate that, while optimization-based planners achieve better path optimality, they generally require higher computational cost. In contrast, OB-RRT provides a favorable trade-off between planning efficiency and path quality. Furthermore, the proposed method is validated on a 6-DoF robotic arm in a catenary maintenance scenario using a digital twin framework. The planned trajectories are successfully executed on a real robotic system, demonstrating feasibility for practical applications.
Duo Zhao, Ganke Huang, Minyu Liu et al.· Journal of Intelligent &...· 0 citations
This follow-up work tests the feasibility of the neuro-inspired self-supervised learning framework for trajectory planning that leverages forward and inverse models as the internal supervisory mechanism in an environment that contains an obstacle, and demonstrates the tendency of the planner to exploit the learning signal provided by the forward and inverse models.
Miroslav Krupa, Miroslav Cibula, Kristína Malinovská· 0 citations
The Safe-Koopman Framework is introduced, an operator-theoretic motion planning method that generalizes obstacle-free demonstrations to planar planning tasks with obstacles and avoids collisions observed in the unconstrained Koopman baseline.
Xucheng Liu, Ruiqi Ke, Yandong Wang et al.· IEEE Transactions on Neural...· 0 citations