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Keywords = local motion planning

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20 pages, 3272 KiB  
Article
Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
by Dazhang You, Junjie Yu, Zhiyuan Jia, Yepeng Zhang and Zhiyuan Yang
Appl. Sci. 2025, 15(15), 8453; https://doi.org/10.3390/app15158453 - 30 Jul 2025
Viewed by 242
Abstract
Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle [...] Read more.
Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle avoidance, and smooth motion through innovative strategies. A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. First, the Chaotic Elite Pool strategy is employed to generate an elite population, enhancing population diversity and improving the quality of initial solutions, thereby boosting the algorithm’s global search capability. Second, adaptive weights are introduced, and the traditional simulated annealing algorithm is improved to obtain the Rapid Annealing Method. The improved simulated annealing algorithm is then combined with the White Shark algorithm to avoid getting stuck in local optima and accelerate convergence speed. Finally, third-order Bézier curves are used to smooth the path. Path length and path smoothness are used as fitness evaluation metrics, and an evaluation function is established in conjunction with a non-complete model that reflects actual motion to assess the effectiveness of path planning. Simulation results show that on the simple 20 × 20 grid map, the fusion of the Fused Multi-strategy White Shark Optimisation algorithm (FMWSO) outperforms WSO, D*, A*, and GWO by 8.43%, 7.37%, 2.08%, and 2.65%, respectively, in terms of path length. On the more complex 40 × 40 grid map, it improved by 6.48%, 26.76%, 0.95%, and 2.05%, respectively. The number of turning points was the lowest in both maps, and the path smoothness was lower. The algorithm’s runtime is optimal on the 20 × 20 map, outperforming other algorithms by 40.11%, 25.93%, 31.16%, and 9.51%, respectively. On the 40 × 40 map, it is on par with A*, and outperforms WSO, D*, and GWO by 14.01%, 157.38%, and 3.48%, respectively. The path planning performance is significantly better than other algorithms. Full article
(This article belongs to the Section Robotics and Automation)
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27 pages, 31172 KiB  
Article
Digital Twin for Analog Mars Missions: Investigating Local Positioning Alternatives for GNSS-Denied Environments
by Benjamin Reimeir, Amelie Leininger, Raimund Edlinger, Andreas Nüchter and Gernot Grömer
Sensors 2025, 25(15), 4615; https://doi.org/10.3390/s25154615 - 25 Jul 2025
Viewed by 208
Abstract
Future planetary exploration missions will rely heavily on efficient human–robot interaction to ensure astronaut safety and maximize scientific return. In this context, digital twins offer a promising tool for planning, simulating, and optimizing extravehicular activities. This study presents the development and evaluation of [...] Read more.
Future planetary exploration missions will rely heavily on efficient human–robot interaction to ensure astronaut safety and maximize scientific return. In this context, digital twins offer a promising tool for planning, simulating, and optimizing extravehicular activities. This study presents the development and evaluation of a digital twin for the AMADEE-24 analog Mars mission, organized by the Austrian Space Forum and conducted in Armenia in March 2024. Alternative local positioning methods were evaluated to enhance the system’s utility in Global Navigation Satellite System (GNSS)-denied environments. The digital twin integrates telemetry from the Aouda space suit simulators, inertial measurement unit motion capture (IMU-MoCap), and sensor data from the Intuitive Rover Operation and Collecting Samples (iROCS) rover. All nine experiment runs were reconstructed successfully by the developed digital twin. A comparative analysis of localization methods found that Simultaneous Localization and Mapping (SLAM)-based rover positioning and IMU-MoCap localization of the astronaut matched Global Positioning System (GPS) performance. Adaptive Cluster Detection showed significantly higher deviations compared to the previous GNSS alternatives. However, the IMU-MoCap method was limited by discontinuous segment-wise measurements, which required intermittent GPS recalibration. Despite these limitations, the results highlight the potential of alternative localization techniques for digital twin integration. Full article
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16 pages, 5555 KiB  
Article
Optimization of a Navigation System for Autonomous Charging of Intelligent Vehicles Based on the Bidirectional A* Algorithm and YOLOv11n Model
by Shengkun Liao, Lei Zhang, Yunli He, Junhui Zhang and Jinxu Sun
Sensors 2025, 25(15), 4577; https://doi.org/10.3390/s25154577 - 24 Jul 2025
Viewed by 281
Abstract
Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the [...] Read more.
Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article
(This article belongs to the Special Issue Vision-Guided System in Intelligent Autonomous Robots)
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22 pages, 4827 KiB  
Article
Development of a Multifunctional Mobile Manipulation Robot Based on Hierarchical Motion Planning Strategy and Hybrid Grasping
by Yuning Cao, Xianli Wang, Zehao Wu and Qingsong Xu
Robotics 2025, 14(7), 96; https://doi.org/10.3390/robotics14070096 - 15 Jul 2025
Viewed by 512
Abstract
A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a [...] Read more.
A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications. Full article
(This article belongs to the Section Sensors and Control in Robotics)
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27 pages, 28182 KiB  
Article
Addressing Local Minima in Path Planning for Drones with Reinforcement Learning-Based Vortex Artificial Potential Fields
by Boyi Xiao, Lujun Wan, Xueyan Han, Zhilong Xi, Chenbo Ding and Qiang Li
Machines 2025, 13(7), 600; https://doi.org/10.3390/machines13070600 - 11 Jul 2025
Viewed by 203
Abstract
In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper [...] Read more.
In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments. Full article
(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)
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25 pages, 6846 KiB  
Article
DGA-ACO: Enhanced Dynamic Genetic Algorithm—Ant Colony Optimization Path Planning for Agribots
by Zhenpeng Zhang, Pengyu Li, Shanglei Chai, Yukang Cui and Yibin Tian
Agriculture 2025, 15(12), 1321; https://doi.org/10.3390/agriculture15121321 - 19 Jun 2025
Viewed by 479
Abstract
Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise [...] Read more.
Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise operational safety, (2) premature convergence to local optima, and (3) excessive energy consumption due to suboptimal trajectories. To overcome these challenges, this study proposes an enhanced Dynamic Genetic Algorithm—Ant Colony Optimization (DGA-ACO) framework. It integrates a 2D risk-penalty mapping model with dynamic obstacle avoidance mechanisms, improves max–min ant system pheromone allocation through adaptive crossover-mutation operators, and incorporates a hidden Markov model for accurately forecasting obstacle trajectories. A multi-objective fitness function simultaneously optimizes path length, energy efficiency, and safety metrics, while genetic operators prevent algorithmic stagnation. Simulations in different scenarios show that DGA-ACO outperforms Dijkstra, A*, genetic algorithm, ant colony optimization, and other state-of-the-art methods. It achieves shortened path lengths and improved motion smoothness while achieving a certain degree of dynamic obstacle avoidance in the global path-planning process. Full article
(This article belongs to the Special Issue Research Advances in Perception for Agricultural Robots)
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51 pages, 13105 KiB  
Review
Current Status and Trends of Wall-Climbing Robots Research
by Shengjie Lou, Zhong Wei, Jinlin Guo, Yu Ding, Jia Liu and Aiguo Song
Machines 2025, 13(6), 521; https://doi.org/10.3390/machines13060521 - 15 Jun 2025
Viewed by 1256
Abstract
A wall-climbing robot is an electromechanical device capable of autonomous or semi-autonomous movement on intricate vertical surfaces (e.g., walls, glass facades, pipelines, ceilings, etc.), typically incorporating sensing and adaptive control systems to enhance task performance. It is designed to perform tasks such as [...] Read more.
A wall-climbing robot is an electromechanical device capable of autonomous or semi-autonomous movement on intricate vertical surfaces (e.g., walls, glass facades, pipelines, ceilings, etc.), typically incorporating sensing and adaptive control systems to enhance task performance. It is designed to perform tasks such as inspection, cleaning, maintenance, and rescue while maintaining stable adhesion to the surface. Its applications span various sectors, including industrial maintenance, marine engineering, and aerospace manufacturing. This paper provides a systematic review of the physical principles and scalability of various attachment methods used in wall-climbing robots, with a focus on the applicability and limitations of different attachment mechanisms in relation to robot size and structural design. For specific attachment methods, the design and compatibility of motion and attachment mechanisms are analyzed to offer design guidance for wall-climbing robots tailored to different operational tasks. Additionally, this paper reviews localization and path planning methods for wall-climbing robots, comparing graph search, sampling-based, and feedback-based algorithms to guide strategy selection across varying environments and tasks. Finally, this paper outlines future development trends in wall-climbing robots, including the diversification of locomotion mechanisms, hybridization of attachment systems, and advancements in intelligent localization and path planning. This work provides a comprehensive theoretical foundation and practical reference for the design and application of wall-climbing robots. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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27 pages, 4552 KiB  
Article
Enhancing Disaster Resilience in Hospitals Through Flow Space-Optimized Evacuation Routes
by Yilai Wu, Jingwei Xia and Xuekelaiti Haiyirete
Sustainability 2025, 17(12), 5419; https://doi.org/10.3390/su17125419 - 12 Jun 2025
Cited by 1 | Viewed by 451
Abstract
Hospitals are an important piece of infrastructure for global emergency management, and their evacuation efficiency is crucial during large-scale disasters or public health crises. Traditional evacuation methods mainly focus on proximity and often overlook dynamic pedestrian density and channel capacity, leading to local [...] Read more.
Hospitals are an important piece of infrastructure for global emergency management, and their evacuation efficiency is crucial during large-scale disasters or public health crises. Traditional evacuation methods mainly focus on proximity and often overlook dynamic pedestrian density and channel capacity, leading to local congestion and increased risk. This study introduces a dynamic optimization evacuation path planning framework based on flow space theory to address the overall inefficiency in hospital evacuation. We model the hospital space as a dynamic network flow, analyze evacuation time through walking and queuing time, and apply a density–velocity correction model to adjust path allocation in real time. Using the MassMotion 11.0 platform to compare the evacuation of simulated hospital models before and after path optimization, the results showed that the average evacuation time was reduced by 10.58%, the waiting time in high-density areas was shortened, and the overall efficiency was improved. Empirical exercises show that path optimization can shorten evacuation time, demonstrating that spatial optimization strategies enhance hospital resilience. These results confirm the practical value of the flow space theory in emergency management for dealing with disasters. The flow space theory enriches the theoretical system of evacuation planning and contributes to a more in-depth study of people’s evacuation behaviors and the optimization of evacuation strategies. Full article
(This article belongs to the Special Issue Sustainable Disaster Management: Theory and Practice)
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30 pages, 10731 KiB  
Article
Real-Time 3D Vision-Based Robotic Path Planning for Automated Adhesive Spraying on Lasted Uppers in Footwear Manufacturing
by Ya-Yung Huang, Jun-Ting Lai and Hsien-Huang Wu
Appl. Sci. 2025, 15(11), 6365; https://doi.org/10.3390/app15116365 - 5 Jun 2025
Viewed by 518
Abstract
The automation of adhesive application in footwear manufacturing is challenging due to complex surface geometries and model variability. This study presents an integrated 3D vision-based robotic system for adhesive spraying on lasted uppers. A triangulation-based scanning setup reconstructs each upper into a high-resolution [...] Read more.
The automation of adhesive application in footwear manufacturing is challenging due to complex surface geometries and model variability. This study presents an integrated 3D vision-based robotic system for adhesive spraying on lasted uppers. A triangulation-based scanning setup reconstructs each upper into a high-resolution point cloud, enabling customized spraying path planning. A six-axis robotic arm executes the path using an adaptive transformation matrix that aligns with surface normals. UV fluorescent dye and inspection are used to verify adhesive coverage. Experimental results confirm high repeatability and precision, with most deviations within the industry-accepted ±1 mm range. While localized glue-deficient areas were observed around high-curvature regions such as the toe cap, these remain limited and serve as a basis for further system enhancement. The system significantly reduces labor dependency and material waste, as observed through the replacement of four manual operators and the elimination of adhesive over-application in the tested production line. It has been successfully installed and validated on a production line in Hanoi, Vietnam, meeting real-world industrial requirements. This research contributes to advancing intelligent footwear manufacturing by integrating 3D vision, robotic motion control, and automation technologies. Full article
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28 pages, 3296 KiB  
Article
Investigating the Neural Mechanisms of Self-Controlled and Externally Controlled Movement with a Flexible Exoskeleton Using EEG Source Localization
by Takayuki Kodama, Masahiro Yoshikawa, Kosuke Minamii, Kazuhei Nishimoto, Sayuna Kadowaki, Yuuki Inoue, Hiroki Ito, Hayato Shigeto, Kohei Okuyama, Kouta Maeda, Osamu Katayama, Shin Murata and Kiichiro Morita
Sensors 2025, 25(11), 3527; https://doi.org/10.3390/s25113527 - 3 Jun 2025
Viewed by 654
Abstract
Background: Self-controlled motor imagery combined with assistive devices is promising for enhancing neurorehabilitation. This study developed a soft, Flexible Exoskeleton (flexEXO) for finger movements and investigated whether self-controlled motor tasks facilitate stronger cortical activation than externally controlled conditions. Methods: Twenty-one healthy participants performed [...] Read more.
Background: Self-controlled motor imagery combined with assistive devices is promising for enhancing neurorehabilitation. This study developed a soft, Flexible Exoskeleton (flexEXO) for finger movements and investigated whether self-controlled motor tasks facilitate stronger cortical activation than externally controlled conditions. Methods: Twenty-one healthy participants performed grasping tasks under four conditions: Self-Controlled Motion (SCC), Other-Controlled Motion (OCC), Self-Controlled Imagery Only (SCIOC), and Other-Controlled Imagery Only (OCIOC). EEG data were recorded, focusing on event-related desynchronization (ERD) in the μ and β bands during imagery and motion and event-related synchronization (ERS) in the β band during feedback. Source localization was performed using eLORETA. Results: Higher μERD and βERD were observed during self-controlled tasks, particularly in the primary motor cortex and supplementary motor area. Externally controlled tasks showed enhanced activation in the inferior parietal lobule and secondary somatosensory cortex. βERS did not differ significantly across conditions. Source localization revealed that self-controlled tasks engaged motor planning and error-monitoring regions more robustly. Conclusions: The flexEXO device and the comparison of brain activity under different conditions provide insights into the neural mechanisms of motor control and have implications for neurorehabilitation. Full article
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26 pages, 2959 KiB  
Review
Intelligent Recognition and Automated Production of Chili Peppers: A Review Addressing Varietal Diversity and Technological Requirements
by Sheng Tai, Zhong Tang, Bin Li, Shiguo Wang and Xiaohu Guo
Agriculture 2025, 15(11), 1200; https://doi.org/10.3390/agriculture15111200 - 31 May 2025
Cited by 2 | Viewed by 853
Abstract
Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences [...] Read more.
Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences in key quality indicators, and the resulting specific harvesting needs. It then reviews recent progress in intelligent perception, recognition, and automation within the chili pepper industry. For perception and recognition, the review covers the evolution from traditional image processing to deep learning-based methods (e.g., YOLO and Mask R-CNN achieving a mAP > 90% in specific studies) for pepper detection, segmentation, and fine-grained cultivar identification, analyzing the performance and optimization in complex environments. In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. The main current research challenges are listed including the environmental adaptability/robustness, efficiency/real-time performance, multi-cultivar adaptability/flexibility, system integration, and cost-effectiveness. Finally, future directions are given (e.g., multimodal sensor fusion, lightweight models, and edge computing applications) in the hope of guiding the intelligent growth of the chili pepper industry. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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25 pages, 3990 KiB  
Article
Study on Trajectory Planning for Polishing Free-Form Surfaces of XY-3-RPS Hybrid Robot
by Xiaozong Song, Junfeng An and Xingwu Ma
Actuators 2025, 14(6), 269; https://doi.org/10.3390/act14060269 - 29 May 2025
Viewed by 518
Abstract
Free-form surface polishing is a key process in precision machining within high-end manufacturing, where optimizing the polishing trajectory directly influences both processing quality and efficiency. Traditional trajectory planning methods for free-form surface polishing in high-curvature regions suffer from issues such as a lack [...] Read more.
Free-form surface polishing is a key process in precision machining within high-end manufacturing, where optimizing the polishing trajectory directly influences both processing quality and efficiency. Traditional trajectory planning methods for free-form surface polishing in high-curvature regions suffer from issues such as a lack of precision, low trajectory continuity, and inefficiency. This paper proposes an improved trajectory planning method based on curvature characteristics, incorporating dynamic partitioning and boundary smoothing algorithms. These methods dynamically adjust according to surface curvature, enhancing processing efficiency and surface quality. Additionally, a hybrid optimization framework combining a genetic algorithm (GA) and local search (LS) is proposed to address the challenges of balancing global optimization with local fine-tuning in traditional trajectory planning methods. These challenges often result in large errors, low machining efficiency, and unstable surface quality. The method optimizes the overall trajectory distribution through a global search using GA while locally refining the high-curvature regions with LS. This combination improves trajectory uniformity and smoothness, and the results demonstrate significant increases in machining efficiency and accuracy. Finally, the feasibility of the trajectory planning method was verified through motion simulation. This paper also provides a detailed description of the mathematical modeling, algorithm implementation, and simulation analysis of the XY-3-RPS hybrid robot for trajectory optimization, offering both a theoretical foundation and engineering support for its application in free-form surface polishing. Full article
(This article belongs to the Section Actuators for Robotics)
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16 pages, 1458 KiB  
Article
Mobile Robot Path Planning Considering Obstacle Gap Features
by Hongwei Wang, Li He, Shuai Zhang, Ruoyang Bai and Yunhang Wang
Appl. Sci. 2025, 15(11), 5979; https://doi.org/10.3390/app15115979 - 26 May 2025
Viewed by 405
Abstract
In order to fully harness obstacle information in path planning and improve the coordination between global and local path planning, a novel mobile robot path planning method is proposed. The novelty of the proposed path planning strategy lies in its integration of obstacle [...] Read more.
In order to fully harness obstacle information in path planning and improve the coordination between global and local path planning, a novel mobile robot path planning method is proposed. The novelty of the proposed path planning strategy lies in its integration of obstacle gap characteristics into both global and local planning processes. Specifically, this method addresses the issues of low search efficiency, excessive redundant points, and poor path quality in the traditional A* algorithm for global path planning by extracting gap grids in the global grid map and incorporating their influence into the heuristic function, thereby guiding the search more effectively. The generated global path is further optimized at gap points to remove redundant nodes. For local path planning, which employs the Dynamic Window Approach (DWA) and often exhibits weak compatibility with global planning and a lack of smoothness through obstacle gaps, this method calculates feasible steering angles based on the distance between the robot and obstacles as well as gap attributes. Additionally, the geometric relationship between global and local paths is established using the Bernstein equation, generating segmented guidance control points for DWA. Simulation experiments demonstrate that the proposed algorithm significantly enhances path efficiency and obstacle avoidance capability in tight space environments, reducing path length by approximately 4.79% and motion time by approximately 15.22% compared to conventional algorithms. Full article
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34 pages, 20595 KiB  
Article
Collision-Free Path Planning in Dynamic Environment Using High-Speed Skeleton Tracking and Geometry-Informed Potential Field Method
by Yuki Kawawaki, Kenichi Murakami and Yuji Yamakawa
Robotics 2025, 14(5), 65; https://doi.org/10.3390/robotics14050065 - 17 May 2025
Viewed by 896
Abstract
In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task [...] Read more.
In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task execution. To meet these demands, we design a comprehensive system composed of two primary components: high-speed skeleton tracking and path planning. For tracking, we implement a high-speed skeleton tracking method that combines deep learning-based detection with optical flow-based motion extraction. In addition, we introduce a dynamic search area adjustment technique that focuses on the target joint to extract the desired motion more accurately. For path planning, we propose a high-speed, geometry-informed potential field model that addresses four key challenges: (P1) avoiding local minima, (P2) suppressing oscillations, (P3) ensuring adaptability to dynamic environments, and (P4) handling obstacles with arbitrary 3D shapes. We validated the effectiveness of our high-frequency feedback control and the proposed system through a series of simulations and real-world collision-free path planning experiments. Our high-speed skeleton tracking operates at 250 Hz, which is eight times faster than conventional deep learning-based methods, and our path planning method runs at over 10,000 Hz. The proposed system offers both versatility across different working environments and low latencies. Therefore, we hope that it will contribute to a foundational motion generation framework for human–robot collaboration (HRC), applicable to a wide range of downstream tasks while ensuring safety in dynamic environments. Full article
(This article belongs to the Special Issue Visual Servoing-Based Robotic Manipulation)
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21 pages, 7060 KiB  
Article
Optimization of Unmanned Excavator Operation Trajectory Based on Improved Particle Swarm Optimization
by Tingting Wang, Xiaohui He, Yunkang Zhou and Faming Shao
Actuators 2025, 14(5), 226; https://doi.org/10.3390/act14050226 - 1 May 2025
Viewed by 418
Abstract
To realize the autonomous operation of unmanned excavators, this study takes the four-axis manipulator arm of an unmanned excavator as the research object, uses the five-order B-spline curve for operation trajectory planning, and proposes an improved particle swarm optimization algorithm for the continuous [...] Read more.
To realize the autonomous operation of unmanned excavators, this study takes the four-axis manipulator arm of an unmanned excavator as the research object, uses the five-order B-spline curve for operation trajectory planning, and proposes an improved particle swarm optimization algorithm for the continuous trajectory optimization problem of excavator single operation. The specific contents are as follows: based on the standard PSO algorithm, dynamic parameter update is used to enhance the global search ability in the early stage and improve the local search accuracy in the later stage; the diversity monitoring mechanism is enhanced to avoid premature maturity convergence; multi-particle SA perturbation is introduced, and the new solution is accepted according to the Metropolis criterion to enhance global search ability. The adaptive cooling rate flexibly responds to different search situations and improves the search efficiency and quality of the solution. To verify the effectiveness of the improved PSO–SA algorithm, this study compares it with the standard PSO algorithm, the standard PSO–SA algorithm, and the MPSO algorithm. The simulation results show that the improved PSO–SA algorithm can converge to the global optimal solution more quickly, has the shortest time in trajectory planning, and the generated trajectory has higher tracking accuracy, which ensures that the vibration and impact of the manipulator during motion are effectively suppressed. Full article
(This article belongs to the Section Actuators for Surface Vehicles)
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