Optimizing path planning in mobile robot systems using motion capture technology

被引:22
作者
Al-Kamil, Safa Jameel [1 ]
Szabolcsi, Robert [2 ]
机构
[1] Obuda Univ, Doctoral Sch Safety & Secur Budapest, Budapest, Hungary
[2] Obuda Univ, Fac Mech & Safety Engn, Dept Mechatron Engn, Budapest, Hungary
关键词
Mobile robot; Path planning; Industry; Navigation; Motion capture; OPTIMIZATION; VEHICLES;
D O I
10.1016/j.rineng.2024.102043
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Recent advancements in mechatronics and robotics have led to the emergence of a wide range of technologies with potential applications across various industries. This progress has been observed in recent years and is expected to continue as the integration of robots into daily life becomes more widespread. Despite these developments, deploying robots in industrial environments, particularly in assembly operations, still presents several challenges, including effectively distributing skills-based tasks between human and robotic workers. This paper proposes an approach to improve the performance of mobile robot systems for optimal path planning. The technique utilizes motion capture technology to collect real-time data on the robot's movements, generate optimal path planning strategies, and enable remote control and monitoring of the robot's activities. The proposed approach can significantly enhance mobile robot systems' capabilities in various industrial settings. The results of our study demonstrate that the integration of motion capture technology can substantially improve the accuracy and efficiency of path planning in mobile robot systems and enhance their overall performance. A series of experiments demonstrate its effectiveness in generating optimal path-planning strategies while minimizing the risk of collisions and other hazards.
引用
收藏
页数:9
相关论文
共 40 条
[1]   DESIGN ALL-WHEEL DRIVE VEHICLES BASED ON DIFFERENTIAL SPEED CONTROL SYSTEMS [J].
Al-Kamil, Safa J. D. ;
Szakacs, Tamas .
MECHATRONIC SYSTEMS AND CONTROL, 2021, 49 (01) :25-29
[2]   A comparative analysis of metaheuristic algorithms for solving the inverse kinematics of robot manipulators [J].
Alexis Abdor-Sierra, Javier ;
Alejandro Merchan-Cruz, Emmanuel ;
Gustavo Rodriguez-Canizo, Ricardo .
RESULTS IN ENGINEERING, 2022, 16
[3]   A Genetic Algorithm (GA) and Swarm-Based Binary Decision Diagram (BDD) Reordering Optimizer Reinforced With Recent Operators [J].
Awad, Ahmed ;
Hawash, Amjad ;
Abdalhaq, Baker .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2023, 27 (03) :535-549
[4]   Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach [J].
Balasubramanian E. ;
Elangovan E. ;
Tamilarasan P. ;
Kanagachidambaresan G.R. ;
Chutia D. .
Journal of Ambient Intelligence and Humanized Computing, 2023, 14 (10) :13847-13867
[5]   A Review of Vision-Based Motion Analysis in Sport [J].
Barris, Sian ;
Button, Chris .
SPORTS MEDICINE, 2008, 38 (12) :1025-1043
[6]   Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes [J].
Bortolini, Marco ;
Faccio, Maurizio ;
Gamberi, Mauro ;
Pilati, Francesco .
COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 139
[7]  
Branicky MS, 2001, IEEE INT CONF ROBOT, P1481, DOI 10.1109/ROBOT.2001.932820
[8]   Path planning for manipulators based on an improved probabilistic roadmap method [J].
Chen, Gang ;
Luo, Ning ;
Liu, Dan ;
Zhao, Zhihui ;
Liang, Changchun .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2021, 72
[9]  
Christiansen M.P., 2018, Collaborative Model-Based Design of Automated and Robotic Agricultural Vehicles in the Crescendo Tool: Computer Science (Robotics)
[10]   Mobile robot path planning using artificial bee colony and evolutionary programming [J].
Contreras-Cruz, Marco A. ;
Ayala-Ramirez, Victor ;
Hernandez-Belmonte, Uriel H. .
APPLIED SOFT COMPUTING, 2015, 30 :319-328