Jul 2026· Proceedings of the Institution of mechanical engineers. Part C, journal of mechanical engineering science· 0 citations· 25 references
TL;DR
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.
Abstract
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.
Mobile robots are increasingly used, but navigation in complex dynamic environments suffers from issues like local optima, poor obstacle avoidance, and difficulty in coordination. This paper proposes a field swarm cooperative algorithm that integrates improved potential field and Particle Swarm Optimization. It uses simulated annealing to escape local optima, leverages the potential field force to enhance obstacle avoidance, and establishes a three-objective cooperative framework. Simulation outcomes indicate that in simple obstacle environments, the proposed algorithm successfully overcomes the local minima problem in U-shaped traps using the traditional Artificial Potential Field method. In the complex static library scenario, the path length of the algorithm proposed by the study is 218.7 m, the average safe distance is 0.56 m, and the smoothness is 3.89 rad. All the indicators have achieved balanced and superior results. Compared with the single path length optimization mode, the multi-objective collaborative optimization mode, at the cost of a slight increase in path length (p>0.05), has achieved significant improvements in the average safe distance and smoothness (p<0.001).The simulation results show that the algorithm proposed in this paper is significantly superior to the comparison methods, and multi-objective equilibrium, and has important practical value for improving the intelligence level and task execution efficiency of robots.
Qiu-Ping Pan, Meng-Ru Chen· Research on Engineering Stru...· 0 citations
In grid-based robotic path planning, traditional A* algorithms are often constrained by severe path redundancy, low search efficiency, and poor adaptability to complex environments with varying obstacle densities. To overcome these limitations, this article proposes the Hierarchical Adaptive Neighborhood Search A* (HANSA*) algorithm, which integrates a dual adaptive search mechanism with local environmental complexity evaluation. This mechanism enables dynamic switching between a 16-neighborhood pattern for rapid global exploration and a directional 5-neighborhood pattern for localized refinement based on real-time planning progress and local obstacle density. Phase-specific heuristic functions are introduced to balance search efficiency, path quality, and trajectory smoothness across different planning stages. Furthermore, a two-stage path optimization strategy is employed to reduce path redundancy, remove unnecessary waypoints, and alleviate sharp corners. Experimental results in dense and complex environments demonstrate that the HANSA* algorithm outperforms conventional approaches, exhibiting improved adaptability and robustness under varying obstacle-density distributions while generating paths with lower redundancy and fewer sharp corners. The results also show that HANSA* can replan paths after newly detected obstacles are incorporated into the updated occupancy grid map. These findings confirm the effectiveness of HANSA* for grid-based global path planning of mobile robots in complex environments.
Ding Cheng, J. Jia, Jie Wen et al.· Robotica (Cambridge. Print)· 0 citations
It is argued that the future of robot path planning will be dominated by hybrid systems that combine global planning, local replanning, optimization, and learning-based prediction, enabling robots to operate more safely, intelligently, and adaptively in complex real-world environments.
Chanyu Wang· Theoretical and Natural Scie...· 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
Autonomous navigation in mobile robots requires reliable path following and obstacle avoidance, especially in dynamic environments. Differential drive robots have limitations due to non-holonomic constraints, making accurate path tracking more challenging. This paper proposes a Model Predictive Control (MPC) for path following integrated with cost map-based obstacle avoidance within the Robot Operating System (ROS) framework. The proposed method combines global path planning using the A star algorithm with a predictive local controller that incorporates environmental information from the cost map to ensure safe navigation. The MPC formulation minimizes tracking errors while considering system constraints to produce smooth and stable motion. The navigation system is evaluated in an indoor environment with static and dynamic obstacles. Results show that the proposed approach improves path following accuracy by approximately 22.21 percent, reduces heading error by about 29.14 percent, and produces smoother and more stable control inputs compared to conventional methods. These improvements are achieved with only a marginal increase in path length of approximately 0.4 percent, indicating an acceptable trade-off between efficiency and safety. Overall, the integration of MPC with cost map-based environmental representation provides a robust and effective solution for mobile robot navigation in complex environments.
T. Agustinah, Fadlan Hafiz Harahap, Y. E. Nugraha et al.· International Seminar on Int...· 0 citations
The proposed OB-RRT algorithm incorporates obstacle-node information derived from collision samples to guide tree expansion and improve exploration efficiency, and is validated on a 6-DoF robotic arm in a catenary maintenance scenario using a digital twin framework.
Duo Zhao, Ganke Huang, Min-Yu Liu et al.· Journal of Intelligent &...· 0 citations
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