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mobile robot path planning based on ORCA and improved DWA method

INTERNATIONAL JOURNAL OF CONTROL 2025年

作者： Dai, Juan Jing, Yanzhang Su, Zhong Zhu, Cui Beijing Informat Sci & Technol Univ Beijing Key Lab High Dynam Nav Technol Beijing 100192 Peoples R China Minist Educ Key Lab Modern Measurement & Control Technol Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Sch Automat Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing Peoples R China

This paper focussed on the development of a dynamic and efficient obstacle avoidance path planning algorithm based on ORCA-DWA algorithm, which combines the Optimal Reciprocal Collision Avoidance (ORCA) algorithm and an improved Dynamic Window Approach (DWA), for improving the quality and efficiency of globally planned paths and ensuring obstacle avoidance between robots for local path planning. This combined ORCA-DWA approach effectively combines the speed of DWA planning with the preferred speed of the ORCA algorithm, it not only solves the problem of the ORCA algorithm's difficulty in determining the preferred speed, but also does not deviate from its optimal trajectory while avoiding obstacles. Additionally, an improved dynamic windowing method is proposed to enhance the adaptability to the environment. As a result, the mobile robot can not only use the DWA algorithm to achieve global path optimisation during navigation, but also achieve obstacle avoidance with the shortest time and path while following the robot's own constraints and considering the robot's radius. Simulation results prove that the method can greatly reduce the length and time of path planning and show that this new algorithm can make the robot's speed smoother.

关键词： Path planning mobile robot ORCA algorithm DWA algorithm dynamic obstacle avoidance

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An Improved HM-SAC-CA Algorithm for mobile robot Path Planning in Unknown Complex Environments

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IEEE ACCESS 2025年 13卷 21152-21163页

作者： Jiao, Ting Hu, Conglin Kong, Lingxin Zhao, Xihao Wang, Zhongbao Shanxi Univ Sch Automat & Software Engn Taiyuan 030031 Shanxi Peoples R China

Path planning and its optimization is a critical and difficult task for a mobile robot in a complex and unknown environment. To tackle this problem, we propose an improved SAC (HM-SAC-CA) algorithm for path planning in unknown complex environments. First, based on the SAC maximum entropy framework, a deep reinforcement learning algorithm with clipped automatic entropy adjustment is proposed to improve the quality of policy learning by suppressing entropy evaluation. Second, an innovative hierarchical experience storage structure is constructed during experience replay, and the overfitting phenomenon caused by using good experiences is eliminated by a bias-free sampling strategy. Finally, a posture reward function and a staged incentive mechanism are proposed. The staged incentive mechanism uses both the sparse reward function and the posture reward function in stages to reduce the blindness of exploration during training and accelerate the training learning process. Experiments are conducted using a simulated Turtlebot3 and a real mobile robot and the results validate the performance of the proposed work.

关键词： Path planning Training mobile robots Entropy Temperature distribution Space exploration Optimization Gold Deep reinforcement learning Decision making Complex environment deep reinforcement learning mobile robot path planning

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A novel autonomous exploration algorithm via LiDAR/IMU SLAM and hierarchical subsystem for mobile robot in unknown indoor environments

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷 016307-016307页

作者： Gao, Zhilin Xie, Fei Huang, Yihan Zhao, Jing Luo, Haisen Yan, Xinchen Zhao, Fei Lv, Pin Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310058 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

Autonomous exploration in unknown environments is an essential capability for mobile robots. The complexity of autonomous exploration, however, means that existing algorithms struggle to balance efficiency and comprehensiveness, causing low mapping accuracy and redundant path planning. To perform accurate and efficient exploration tasks, we have proposed a novel autonomous exploration algorithm via LiDAR/IMU (Inertial Measurement Unit) Simultaneous Localization and Mapping (SLAM) and hierarchical subsystem for mobile robot in unknown environments. Firstly, to enhance mapping accuracy for mobile robot exploration, LiDAR/IMU SLAM is improved with the assistance of backward propagation and iterated Kalman filter, and Bidirectional Rapidly-Exploring Random Trees* (BI-RRT*) is applied for efficient frontier point detection. Secondly, we optimize local path planning by leveraging information theory through perceptual quality evaluation, which is then integrated with global path planning utilizing an enhanced Travelling Salesman Problem solver and a sparse grid map to amplify exploration efficiency. Thirdly, an enhanced hierarchical autonomous exploration method for mobile robots is proposed, which incorporates local path planning for seamless navigation around highly promising exploration spots, coupled with global path planning to effectively interconnect various sub-regions. Finally, simulations and field tests have demonstrated that the proposed method explores an unknown indoor environment with a 30.8% reduction in exploration time and a 29.9% reduction in exploration path in comparison with Dynamic Stage Viewpoint Planner. The map constructed in this paper has more accurate details and exploration paths have been shortened to ensure effective exploration.

关键词： autonomous exploration SLAM mobile robot path planning

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mobile robot Path Planning Method Based on Weight Coefficient Improved A* Algorithm 17th

Mobile Robot Path Planning Method Based on Weight Coefficien...

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17th International Conference on Intelligent robotics and Applications

作者： Qin, Tao Chu, Hao Wang, Jia Ren, Peng Fu, Min Wu, Xi Hubei Univ Arts & Sci Sch Mech Engn Xiangyang 441053 Hubei Peoples R China Xiangyang Key Lab Rehabil Med & Rehabil Engn Tech Xiangyang 441053 Hubei Peoples R China Hubei Univ Arts & Sci Affiliated Hosp Xiangyang Cent Hosp Dept Rehabil Med Xiangyang 441021 Peoples R China

ISBN: (纸本)9789819607945;9789819607952

Aiming at the shortcomings of traditional A* algorithm planning such as long path calculation time, multiple search nodes, and multiple turning points, an improved A* algorithm based on weight coefficient optimization is proposed. By comparing Euclidean distance, Chebyshev distance, and Manhattan distance, it is shown that Manhattan distance has shorter search time and fewer turning points, so Manhattan distance is selected as the estimated cost function;By improving the weight coefficients of the actual cost function and the estimated cost function, the disadvantages of traditional A* algorithm such as long calculation time and multiple search nodes are solved;The resulting path is smoothed through a Bezier curve, resulting in a smooth path. Experiments show that the improved A* algorithm has short computing time, fewer search nodes, fewer turning points, and smooth trajectory.

关键词： mobile robot A* algorithm Path planning

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ARE-QL: an enhanced Q-learning algorithm with optimized search for mobile robot path planning

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PHYSICA SCRIPTA 2025年第3期100卷 036015-036015页

作者： Zhang, Yunjie Liu, Yue Chen, Yadong Yang, Zhenjian Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin Peoples R China

This paper addresses challenges in Q-learning for mobile robot path planning, specifically low learning efficiency and slow convergence. An ARE-QL algorithm with an optimized search range is proposed to address these issues. Firstly, the reward function of Q-learning is enhanced. A dynamic continuous reward mechanism, based on heuristic environmental information, is introduced to reduce the robot's search space and improve learning efficiency. Secondly, integrating the pheromone mechanism from the ant colony algorithm introduces a pheromone-guided matrix and path filtering, optimizing the search range and accelerating convergence to the optimal path. Additionally, an adaptive exploration strategy based on state familiarity enhances the algorithm's efficiency and robustness. Simulation results demonstrate that the ARE-QL algorithm outperforms standard Q-learning and other improved algorithms. It achieves faster convergence and higher path quality across various environmental complexities. The ARE-QL algorithm enhances path planning efficiency while demonstrating strong adaptability and robustness, providing new insights and solutions for mobile robot path planning research.

关键词： path planning Q-learning mobile robot reinforcement learning ant colony algorithm

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Approximate Inference Particle Filtering for mobile robot SLAM

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 7967-7978页

作者： Zhang, Shuo Shan, Jinjun Liu, Yibo York Univ Dept Earth & Space Sci & Engn Toronto ON M3J 1P3 Canada

This paper proposes approximate inference particle filtering for mobile robot simultaneous localization and mapping (SLAM) with landmarks. Range-bearing measurements are obtained by detecting landmarks using an onboard laser range finder and the maximum likelihood approach is used to handle unknown data associations. The system model is created depending on the robot motion and range-bearing measurements. The new particle filter is developed to estimate the robot pose and landmark locations separately based on approximate inferences, where the intractable distributions of the robot pose and landmark locations are approximated by optimal Gaussian distributions minimizing Kullback-Leibler divergences. Simulations and experiments are provided to show the performance of the proposed particle filter. Note to Practitioners-The SLAM technique is crucial to an autonomous mobile robot. The laser range finder perceives surroundings and is used for mobile robot SLAM in global positioning system denied environments. This paper proposes a mobile robot SLAM method based on the robot motion model and the onboard laser range finder. Range-bearing measurements are extracted from raw data of the laser range finder as the inputs of the method. The simulations and experiments indicate the proposed method has better accuracy and more robust to unknown data associations. The proposed method can be applied to many practical applications such as transport operations and mowing with a mobile robot. In the future, we will address the SLAM technique for multiple mobile robots.

关键词： mobile robot landmark SLAM particle filtering approximate inference mobile robot landmark SLAM particle filtering approximate inference

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Laser_RobMap: An open source ROS2 compatible tool for 3D mapping using a mobile robot and 2D LiDAR

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SOFTWAREX 2025年 30卷

作者： Yanyachi, Daniel D. Anco-Aguero, Yamir H. Echaiz, German A. Esquivel, Miguel A. Mamani-Saico, Alfredo Yanyachi, Pablo R. Univ Nacl San Agustin Arequipa Arequipa 04000 Peru

We propose a tool for implementing a low-cost 3D mapping system using a vertically mounted 2D LiDAR sensor on a mobile robot, compatible with ROS2. The tool adapts to any ROS2-enabled robot that provides inertial and odometry data, with configurable parameters to optimize performance on systems with limited computational resources. Its main objective is to deliver a cost-effective 3D mapping solution for autonomous navigation using 3D SLAM. The results can be exported as 3D point clouds in PCD, LAS, and PLY formats, with an optional voxelization feature for efficient data management.

关键词： mobile robot 3D mapping LiDAR robot operating system Real-time processing

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Effects of mobile robot Passing-Motion Path Curvature on Human Affective States in a Hallway Environment

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INTERNATIONAL JOURNAL OF SOCIAL robotICS 2025年 1-11页

作者： Greenberg, Benjamin Gonzalez-Bravo, Uriel Yi, Jingang Feldman, Jacob Rutgers State Univ Dept Mech & Aerosp Engn 98 Brett Rd Piscataway NJ 08854 USA Rutgers State Univ Dept Psychol 152 Frelinghuysen Rd Piscataway NJ 08854 USA

For social robots to move in ways that are socially desirable to the people that they encounter, it is necessary to understand the types of robot motions that people find more or less appealing. An experiment was conducted in which participants (N=20\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=20$$\end{document}) encountered a mobile robot in a virtual reality hallway environment. The robot passed them following trajectories shaped like cubic Bezier curves. The sharpness of curvature of the robot motions was varied, as was the distance from the person at which the passing-motion was initiated. Participants scored each motion both on how arousing and pleasurable they found the experience of being passed by the robot to determine the affective state induced by the robot motion. Our data show that the curvature of passing-motions has an effect on the affective state of the person being passed, while other proxemic constraints are held constant. Participants respond more positively to paths that have moderate curvature, and more negatively to passing-motions that are either too sharply curved, or too straight. This finding enables the development of path planning algorithms that respect the empirically-defined path curvature constraint to improve human acceptability in this context.

关键词： Human-robot interaction mobile robot Social rules Virtual reality Path planning

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Path planning of mobile robot based on improved double deep Q-network algorithm

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FRONTIERS IN NEUROrobotICS 2025年 19卷 1512953页

作者： Wang, Zhenggang Song, Shuhong Cheng, Shenghui Anhui Polytech Univ Coll Elect Engn Wuhu Peoples R China

Aiming at the problems of slow network convergence, poor reward convergence stability, and low path planning efficiency of traditional deep reinforcement learning algorithms, this paper proposes a BiLSTM-D3QN (Bidirectional Long and Short-Term Memory Dueling Double Deep Q-Network) path planning algorithm based on the DDQN (Double Deep Q-Network) decision model. Firstly, a Bidirectional Long Short-Term Memory network (BiLSTM) is introduced to make the network have memory, increase the stability of decision making and make the reward converge more stably;secondly, Dueling Network is introduced to further solve the problem of overestimating the Q-value of the neural network, which makes the network able to be updated quickly;Adaptive reprioritization based on the frequency penalty function is proposed. Experience Playback, which extracts important and fresh data from the experience pool to accelerate the convergence of the neural network;finally, an adaptive action selection mechanism is introduced to further optimize the action exploration. Simulation experiments show that the BiLSTM-D3QN path planning algorithm outperforms the traditional Deep Reinforcement Learning algorithm in terms of network convergence speed, planning efficiency, stability of reward convergence, and success rate in simple environments;in complex environments, the path length of BiLSTM-D3QN is 20 m shorter than that of the improved ERDDQN (Experience Replay Double Deep Q-Network) algorithm, the number of turning points is 7 fewer, the planning time is 0.54 s shorter, and the success rate is 10.4% higher. The superiority of the BiLSTM-D3QN algorithm in terms of network convergence speed and path planning performance is demonstrated.

关键词： deep reinforcement learning mobile robot path planning BiLSTM Dueling Network

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Deep Reinforcement Learning-Based Mapless Navigation for mobile robot in Unknown Environment With Local Optima

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IEEE robotICS AND AUTOMATION LETTERS 2025年第1期10卷 628-635页

作者： Hu, Yiming Wang, Shuting Xie, Yuanlong Zheng, Shiqi Shi, Peng Rudas, Imre Cheng, Xiang Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Univ Adelaide Sch Elect & Mech Engn Adelaide SA 5005 Australia Obuda Univ Res & Innovat Ctr H-1034 Budapest Hungary

Local optima issues challenge mobile robots mapless navigation with the dilemma of avoiding collisions and approaching the target. Planning-based methods rely on environmental models and manual strategies to guide the robot. In contrast, learning-based methods can process original sensor data to navigate the robot in real-time but struggle with local optima. To address this, we designed reward rules that punish the robot for previously visited areas that may trap the robot, and reward it for exploring local areas in diverse ways and escaping from local optima areas. Then, we improved the Soft Actor-Critic (SAC) algorithm by making its temperature parameter adaptive to the current training status, and memorizing it in experiences for strategy updating, bringing additional exploratory behaviors and necessary stability into the training. Finally, with the assistance of auxiliary networks, the robot learns to handle various navigation tasks with local optima risks. Simulations demonstrate the advantages of our method in terms of both success rate and path efficiency compared to several existing methods. Experiments verified the proposed method in real-world scenarios.

关键词： robots Navigation Training Collision avoidance robot kinematics Trajectory Feature extraction robot sensing systems Laser radar Deep reinforcement learning mobile robot mapless navigation mapless navigation

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