Article
Mathematics
Mahmoud Elsisi, Hatim G. Zaini, Karar Mahmoud, Shimaa Bergies, Sherif S. M. Ghoneim
Summary: This paper presents a new design for a robot-manipulator controller based on the butterfly optimization algorithm (BOA) to achieve improved trajectory tracking performance by collaboratively minimizing response steady-state error, settling time, and overshoot.
Article
Computer Science, Information Systems
Rongjie Zhai, Ping Xiao, Da Shu, Yongjiu Sun, Min Jiang
Summary: An improved butterfly optimization algorithm (IBOA) is proposed to overcome the disadvantages in the path planning of mobile robots, including slow convergence, generation of local optimum solutions, and deadlock phenomenon. The IBOA introduces various strategies, such as kent mapping, adaptive weight coefficient, opposition-based learning strategy, and mutation strategy, to enhance the global search ability and solve the path planning problem. Simulation results show that IBOA has a strong ability to solve robot path planning problems and the proposed path simplification strategy effectively reduces the length of the optimal path in the grid map.
Article
Automation & Control Systems
Kun Shi, Zhengtian Wu, Baoping Jiang, Hamid Reza Karimi
Summary: Dynamic path planning for mobile robots is crucial due to their increasing usage. This study introduces an improved simulated annealing algorithm for avoiding moving obstacles in dynamic situations. The algorithm reduces computational effort through initial path selection and deletion operations. Simulation results demonstrate the algorithm's superiority over others in both static and dynamic environments.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Chemistry, Analytical
Qing Si, Changyong Li
Summary: An improved whale optimization algorithm is proposed to address the issues of the original algorithm in indoor robot path planning. The algorithm incorporates an improved logistic chaotic mapping, a nonlinear convergence factor, and a fused Corsi variance and weighting strategy to enhance the search capability and path quality. Experimental results demonstrate the superior performance of the improved algorithm compared to other algorithms in both test functions and path planning.
Article
Computer Science, Hardware & Architecture
Awei Zou, Lei Wang, Weimin Li, Jingcao Cai, Hai Wang, Tielong Tan
Summary: A fusion algorithm combining improved mayfly optimization algorithm and dynamic window approach is proposed, with Q-learning used for parameter adjustment in global path planning using IMOA-QL algorithm, and improved dynamic window approach for local path planning, significantly improving the accuracy and speed of solution.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Green & Sustainable Science & Technology
Yue Li, Jianyou Zhao, Zenghua Chen, Gang Xiong, Sheng Liu
Summary: Intelligent mobile robots are crucial for green and efficient warehouse operations, with significant impacts on the environment and the economy. Path planning is a key technology for achieving intelligence in mobile robots. To improve logistics efficiency and reduce resource waste and carbon emissions, this study investigates the problem of robot path optimization. Guided by sustainable development theory, the goal is to shorten and smoothen robot paths for environmental and social governance. A fusion algorithm combining an improved genetic algorithm and the dynamic window approach is proposed to enhance the robot's ability to avoid dynamic obstacles and quickly find shorter and smoother paths. By doing so, warehouse operations efficiency can be improved, logistics costs can be reduced, and a green supply chain can be achieved. The study implements an improved fusion algorithm for mobile robot path planning and demonstrates its superiority through comparative experiments, highlighting the importance of advanced algorithms in optimizing robot paths and suggesting future research directions.
Article
Engineering, Multidisciplinary
Jingyao Huang, Huihui Wu
Summary: In this paper, an improved coyote optimization algorithm (ICOA) is proposed to meet the requirements of global search capability, convergence speed, and stability for mobile robot path planning problems. By introducing new strategies and growth modes, ICOA exhibits strong optimization ability and diversity.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Quoc Bao Diep, Thanh Cong Truong, Swagatam Das, Ivan Zelinka
Summary: This article introduces an improved version of the Self-Organizing Migrating Algorithm named iSOMA and evaluates its performance. The iSOMA algorithm shows notable improvements compared to previous versions and achieves excellent results in multiple benchmark tests. Additionally, the article demonstrates the application of iSOMA in drone path planning.
APPLIED SOFT COMPUTING
(2022)
Article
Multidisciplinary Sciences
Fei-Fei Li, Yun Du, Ke-Jin Jia
Summary: This paper proposes an algorithm that integrates the improved artificial fish swarm algorithm with continuous segmented Bezier curves for the path planning and smoothing of mobile robots. The algorithm addresses the low accuracy, inflection points, and long planning paths in traditional artificial fish swarm algorithms by introducing feasible solutions, step sizes based on Dijkstra's algorithm. It also overcomes convergence and degradation issues through the use of dynamic feedback horizon and adaptive step sizes. The algorithm ensures continuous paths in both orientation and curvature, achieving 100% planning accuracy in simulations and satisfying the kinematic characteristics of the mobile robot.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Information Systems
Xuefeng Dai, Yang Wei
Summary: The paper proposes an improved moth-flame optimization (IMFO) algorithm which enhances the global search capability and convergence speed by introducing historical best flame average and quasi-opposition-based learning. Computer simulations confirm the algorithm's effectiveness and good performance in mobile robot path planning.
Article
Computer Science, Artificial Intelligence
Junhui Yi, Qingni Yuan, Ruitong Sun, Huan Bai
Summary: This paper proposes an improved P_RRT* algorithm for path planning of manipulators in three-dimensional space, aiming to address the slow convergence speed and low search efficiency of the potential function-based RRT* algorithm (P_RRT*). The algorithm utilizes random sampling based on a potential function, two expansion methods, redundant node deletion, and maximum curvature constraint to improve search efficiency and optimize the generated path. Experimental results in Python and ROS demonstrate the effectiveness and superiority of the improved algorithm.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Chemistry, Analytical
Hao Ge, Zhanfeng Ying, Zhihua Chen, Wei Zu, Chunzheng Liu, Yicong Jin
Summary: In this paper, an improved A* algorithm considering energy consumption is proposed for path planning of spherical robots. The algorithm aims to minimize both the energy consumption and path length of the robot. The effectiveness of the algorithm is verified through simulation analysis.
Article
Automation & Control Systems
Xiangjian Li, Huashan Liu, Menghua Dong
Summary: This article proposes a general motion planning framework that integrates deep reinforcement learning (DRL) to address the challenges of redundant robot manipulator motion planning and inverse kinematics optimization in environments with obstacles. Experimental results demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Yonggang Li, Rencai Jin, Xiangrong Xu, Yuandi Qian, Haiyan Wang, Shanshan Xu, Zhixiong Wang
Summary: This paper presents an optimized A* algorithm and an algorithm that combines the improved A* algorithm with the dynamic window method to address the issues of multiple turning points, large turning angles, and long running time in path planning. The experimental results show that these algorithms are more effective in complex dynamic environments.
Article
Computer Science, Artificial Intelligence
Md. Rafiqul Islam, Pranta Protik, Sudipto Das, Pritam Khan Boni
Summary: The study introduces a metaheuristic algorithm based on chemical reaction optimization for mobile robot path planning, which optimizes path length, smoothness, and execution time by redesigning basic operators and introducing new repair operators. The algorithm is proven superior through comparison with other algorithms and empirical results on complex maps.
Article
Computer Science, Information Systems
Jun Dai, Yi Zhang, Hua Deng
Summary: In this paper, a novel voltage-based weighted hybrid force/position control algorithm is proposed for redundant robot manipulators. The algorithm simplifies the controller design by establishing a mapping between voltage and terminal position and orientation, using motor current as feedback to replace the tedious calculation of the dynamic model. The proposed algorithm eliminates the selection matrix and directly sums the force and position control laws through a weighted way. Comparative simulations and a transport experiment validate the effectiveness of the algorithm in improving operation capability and control accuracy.
Article
Engineering, Electrical & Electronic
Jun Dai, Yi Zhang, Hua Deng
Summary: This study proposes a master-slave planning method based on bidirectional RRT* to solve the closed-chain constraints problem in tight coordination of dual redundant manipulators. Bidirectional RRT* is used for path planning of the master manipulator, while the path of the slave manipulator is calculated using terminal generalized velocity constraints. A local path replanning strategy is also proposed to address the issue of discontinuous joint path of the slave manipulator. Experimental results demonstrate that the proposed method can solve the discontinuity problem, increase the success rate, shorten the planning time, and satisfy closed-chain constraints.
Article
Automation & Control Systems
Runwei Guan, Shanliang Yao, Lulu Liu, Xiaohui Zhu, Ka Lok Man, Yong Yue, Jeremy Smith, Eng Gee, Yutao Yue
Summary: With the development of Unmanned Surface Vehicles (USVs), the perception of inland waterways has become significant. Traditional RGB cameras cannot work effectively in adverse weather and at night, which has led to the emergence of 4D millimeter-wave radar as a new perception sensor. However, the radar suffers from water-surface clutter and irregular shape of point cloud. To address these issues, this paper proposes a high-performance panoptic perception model called Mask-VRDet, which fuses features of vision and radar using graph neural network.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Adrien Le Reun, Kevin Subrin, Anthony Dubois, Sebastien Garnier
Summary: This study aims to evaluate the quality and health of aerospace parts using a high-dimensional robotic cell. By utilizing X-ray Computed Tomography devices, the interior of the parts can be reconstructed and anomalies can be detected. A methodology is proposed to assess both the raw process capability and the improved process capability, with three strategies developed to improve the robot behavior model and calibration.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Weiming Ba, Jung-Che Chang, Jing Liu, Xi Wang, Xin Dong, Dragos Axinte
Summary: This paper proposes a hybrid scheme for kinematic control of continuum robots, which avoids errors through tension supervision and accurate piecewise linear approximation. The effectiveness of the controller is verified on different continuum robotic systems.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Gabriele Abbate, Alessandro Giusti, Viktor Schmuck, Oya Celiktutan, Antonio Paolillo
Summary: In this study, a learning-based approach is proposed to predict the probability of human users interacting with a robot before the interaction begins. By considering the pose and motion of the user, the approach labels the robot's encounters with humans in a self-supervised manner. The method is validated and deployed in various scenarios, achieving high accuracy in predicting user intentions to interact with the robot.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Tiago Cortinhal, Eren Erdal Aksoy
Summary: This work presents a new depth-and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation between LiDAR and camera sensors. The model is able to translate raw LiDAR point clouds to RGB-D camera images by solely relying on semantic scene segments, and it has practical applications in fields like autonomous vehicles.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Marios Krestenitis, Emmanuel K. Raptis, Athanasios Ch. Kapoutsis, Konstantinos Ioannidis, Elias B. Kosmatopoulos, Stefanos Vrochidis
Summary: This paper addresses the issue of informative path planning for a UAV used in precision agriculture. By using a non-uniform scanning approach, the time spent in areas with minimal value is reduced, while maintaining high precision in information-dense regions. A novel active sensing and deep learning-based coverage path planning approach is proposed, which adjusts the UAV's speed based on the quantity and confidence level of identified plant classes.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shota Kokubu, Pablo E. Tortos Vinocour, Wenwei Yu
Summary: In this study, a new modular soft actuator was proposed to improve the support performance of soft rehabilitation gloves (SRGs). Objective evaluations and clinical tests were conducted to demonstrate the effectiveness and functionality of the proposed actuator and SRG.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Jinliang Zhu, Yuanxi Sun, Jie Xiong, Yiyang Liu, Jia Zheng, Long Bai
Summary: This paper proposes an active prosthetic knee joint with a variable stiffness parallel elastic actuation mechanism. Numerical verifications and practical experiments demonstrate that the mechanism can reduce torque and power, thus reducing energy consumption and improving the endurance of the prosthetic knee joint.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Yong You, Jingtao Wu, Yunlong Meng, Dongye Sun, Datong Qin
Summary: A new power-cycling variable transmission (PCVT) is proposed and applied to construction vehicles to improve transmission efficiency. A shift correction strategy is developed based on identifying the changes in construction vehicles' mass and gradient. Simulation results show that the proposed method can correct shift points, improve operation efficiency, and ensure a safer operation process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Shaorui Liu, Wei Tian, Jianxin Shen, Bo Li, Pengcheng Li
Summary: This paper proposes a two-objective optimization technique for multi-robot systems, addressing the issue of balancing productivity and machining performance in high-quality machining tasks.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pengchao Ding, Faben Zhu, Hongbiao Zhu, Gongcheng Wang, Hua Bai, Han Wang, Dongmei Wu, Zhijiang Du, Weidong Wang
Summary: We propose an autonomous approaching scheme for mobile robot traversing obstacle stairwells, which overcomes the restricted field of vision caused by obstacles. The scheme includes stair localization, structural parameter estimation, and optimization of the approaching process.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Pedro Azevedo, Vitor Santos
Summary: Accurate detection and tracking of vulnerable road users and traffic objects are vital tasks for autonomous driving and driving assistance systems. This paper proposes a solution for object detection and tracking in an autonomous driving scenario, comparing different object detectors and exploring the deployment on edge devices. The effectiveness of DeepStream technology and different object trackers is assessed using the KITTI tracking dataset.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
Article
Automation & Control Systems
Benjamin Beiter, Divya Srinivasan, Alexander Leonessa
Summary: Powered exoskeletons can significantly reduce physical workload and have great potential impact on future labor practices. To truly assist users in achieving task goals, a shared autonomy control framework is proposed to separate the control objectives of the human and exoskeleton. Positive Power control is introduced for the human-based controller, while 'acceptance' is used as a measure of matching the exoskeleton's control objective to the human's. Both control objectives are implemented in an optimization-based Whole-Body-Control structure. The results verify the effectiveness of the control framework and its potential for improving cooperative control for powered exoskeletons.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2024)
