Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-6· 0 citations· 12 references
Abstract
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.
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.
Xin-Guang Li, Shilong Zhao, Xiao-Qi Guo· Proceedings of the Instituti...· 0 citations
Robotic manipulators operating in cluttered environments require collision-free trajectories that remain executable under kinematic and dynamic constraints. This paper proposes a reinforcement learning (RL)-guided multi-objective trajectory planning framework, termed RL-MOP-HNE, for a 6-DOF UR5 manipulator. The planning model simultaneously minimizes path length, energy consumption, and execution time while satisfying collision-avoidance, kinematic, and dynamic constraints. A tabular SARSA agent is embedded into the evolutionary search to adaptively select search behaviours according to the current optimization state. To improve the balance between exploration and exploitation, a Gaussian-perturbation adaptive hybrid crossover operator is integrated with a hierarchical neighborhood evolution (HNE) strategy, enabling progressive population refinement throughout the search process. The proposed method is evaluated in three representative environments with increasing planning complexity, including single-obstacle, narrow three-obstacle, and irregular five-obstacle scenarios, and is compared with MOEA/D, MOPSO, MSCLPSO, NSGA-II, and RL-NSGA-II. Experimental results show that RL-MOP-HNE generates feasible trajectories in all test cases and achieves the lowest dynamic-stability-prioritized composite scores among the compared algorithms. The planned trajectories exhibit smoother joint motion and lower velocity fluctuations, although these improvements are generally accompanied by longer execution times. Complementary analyses, including time scaling, manipulability, clearance evaluation, statistical significance tests, and ablation studies, further explain the performance characteristics of the proposed framework and quantify the contribution of its key components. The proposed framework is therefore well suited to robotic applications where motion stability and dynamic executability are of greater importance than minimum-time operation.
Zhen-Long Zhao, Shu-Tao Hao, Bi-Hao Jin et al.· Scientific Reports· 0 citations
A softmin-based adaptive blending mechanism that automatically selects the most suitable path from the champion solutions on the Pareto front according to regional environmental conditions, thereby eliminating the need for manual user intervention is introduced.
Osman Emre Turan, Oğuz Mısır, Mustafa Özden· Measurement science and tech...· 0 citations
Autonomous mobile robots face significant challenges when navigating complex environments that contain some static and dynamic obstacles. Traditional safety control methods based on control barrier functions (CBFs) with quadratic programming (QP) often suffer from high computational burden and vulnerability to deadlock situations, especially in cluttered environments. To this end, a novel two-phased robust safety control framework is proposed in this paper by integrating dynamic programming (DP) for global path planning and CBFs-QP controller for local real-time obstacle avoidance, to enhance the safety robustness of autonomous robots in complex obstacle configurations while reducing computational and energy consumption. Moreover, an improved A* algorithm is presented to resolve the deadlock problem caused by dynamic obstacles. In the first phase, DP-based global path planning with redundancy pruning is performed as a one-shot pre-computation to generate an optimal path before autonomous robots set off. In the second phase, CBFs are used for real-time safety and local obstacle avoidance by tracking the global path information. If deadlock situations still arise, then an A* algorithm is adopted to identify a feasible path across dynamic obstacles. With the assistance of the A* algorithm, CBFs can effectively guarantee local real-time tackling.
Junming Ren, Lihan Chen, Lijun Long· Transactions of the Institut...· 0 citations
This work presents a prioritized Safe Interval Path Planning algorithm (SIPP-PP) with a novel limited goal reservation strategy to prevent goal-blocking conflicts while allowing shared goal regions, and demonstrates a multi-robot planner capable of real-time operation in dense scenarios, satisfying the stringent requirements of industrial applications such as drive units in fulfillment centers.
Rajat Kumar, Kristin Predeck, Ken Meszaros et al.· 0 citations
Sampling-based motion planning algorithms have been extensively adopted for the global path planning of mobile robots and industrial manipulators in complex static environments, owing to their probabilistic completeness and computational scalability in high-dimensional configuration space. However, most existing variants predominantly rely on single optimization strategy and multi-strategy fusion approaches often suffer from insufficient collaborative design among modules. These limitations hinder the ability to simultaneously balance exploration, convergence speed, and path quality, particularly in cluttered scenes with narrow passages. To address these challenges, this study proposes a novel multi-strategy integrated RRT* (M-RRT*) path planning framework that enables coordinated optimization across all modules. First, a three-layer hybrid sampling strategy is designed, combining goal-biased sampling, obstacle Gaussian sampling, and uniform global sampling to adaptively balance global exploration and local convergence efficiency. Second, an adaptive bidirectional tree expansion mechanism can dynamically adjust the two trees expansion state. Third, a three-point local shortcut optimization serves as the post-processing module to further refine the path quality. Extensive comparative experiments are conducted across three typical 2D environments: cluttered obstacles, maze, and narrow passages environments. The results demonstrate that, compared to RRT*, the M-RRT* reduces average path length by 24.1%, sampling nodes by 84.2%, average planning time by 83.0%, and maintains a 100% success rate across all test environments—notably enhancing planning reliability in complex constrained environments. When benchmarked against Informed-RRT* and B-RRT*, M-RRT* achieves a superior equilibrium between efficiency, success rate, and path quality, exhibiting more pronounced comprehensive advantages in complex maze and narrow passage scenarios. These quantitative results validate that the M-RRT* surpasses existing baselines in overall performance and holds application potential for AGVs, inspection robots, and industrial logistics equipment.
jian liu, Bo Tao, Du Jiang et al.· Engineering Research Express· 0 citations
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