3K method: time-optimal path planning for field robot

• Autorzy: Kwaśniewski Konrad K. , Gosiewski Zdzisław

Identyfikatory

DOI

10.2478/ama-2025-0020

• Języki publikacji: EN

Abstrakty

EN

In this study, a hybrid genetic-geometrical path finding method is presented. Its main feature is the division of the path-finding process into global and local path-finding to achieve a trajectory optimized under the shortest travel time condition in an environment filled with obstacles. To improve the reliability of the algorithm, a safety zone around obstacles is included. In this zone, the maximum velocity allowed for a robot is additionally limited to decrease the probability of collision due to noise in obstacle mapping, distraction from terrain irregularities or malfunction of the steering system. The simulation and real world experiment results are presented in another paper.

Słowa kluczowe

EN

mobile robot; path planning; path optimization; robot rover; autonomous; genetic algorithm

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2025
• Tom: Vol. 19, no. 1
• Strony: 172--176
• Opis fizyczny: Bibliogr. 25 poz., rys.

Twórcy

autor

Kwaśniewski Konrad K.

• Faculty of Electrical Engineering, Bialystok University of Technology, ul. Wiejska 45 C, 15-351 Bialystok, Poland

• https://orcid.org/0000-0003-2528-8904

autor

Gosiewski Zdzisław

• Faculty of Electrical Engineering, Bialystok University of Technology, ul. Wiejska 45 C, 15-351 Bialystok, Poland

• https://orcid.org/0000-0002-7437-2574

Bibliografia

• 1. Alazzam H, AbuAlghanam O, Sharieh A. Best path in mountain environment based on parallel A* algorithm and apache spark. The Journal of Supercomputing. 2022; 1–20.
• 2. Alymani M, Alsolai H, Maashi M, Alhebri A, Alshahrani H, Al-Wesabi FN, Mohamed A, Hamza MA. Dispersal foraging strategy with cuck-oo search optimization based path planning in unmanned aerial vehi-cle networks. IEEE Access 11. 2023; 31365–31372.
• 3. Bozek P, Karavaev YL, Ardentov AA, Yefremov KS. Neural network control of a wheeled mobile robot based on optimal trajectories. In-ternational journal of advanced robotic systems 2020;17: 2.
• 4. Cao X, Zuo F. A fuzzy-based potential field hierarchical reinforce-ment learning approach for target hunting by multi-auv in 3-d under-water environments. International Journal of Control 2021;94(5):1334–1343.
• 5. Duan S, Wang Q, Han X. Improved a-star algorithm for safety in-sured optimal path with smoothed corner turns. Journal of Mechani-cal Engineering. 2020;56(18): 205–215.
• 6. Fu W, Wang B, Li X, Liu L, Wang Y. Ascent trajectory optimization for hypersonic vehicle based on improved chicken swarm optimization. IEEE Access 7. 2019;151836–151850.
• 7. Gosiewski Z, Kwaśniewski K. Time minimization of rescue action realized by an autonomous vehicle. Electronics 9. 2020;12: 2099.
• 8. Halliday D, Resnick R, Walker J. Fundamentals of physics. John Wiley & Sons; 2013.
• 9. Jiang A, Yao X, Zhou J. Research on path planning of real-time obstacle avoidance of mechanical arm based on genetic algorithm. The Journal of Engineering. 2018;16: 1579–1586.
• 10. Kwaśniewski KK, Gosiewski Z. Genetic algorithm for mobile robot route planning with obstacle avoidance. acta mechanica et automati-ca. 2018; 12(2): 151–159.
• 11. Li G, Yamashita A, Asama H, Tamura Y. An efficient improved artificial potential field based regression search method for robot path planning. In 2012 IEEE International Conference on Mechatronics and Automation. 2012; 1227–1232.
• 12. Li L, Shi D, Jin S, Yang S, Zhou C, Lian Y, Liu H. Exact and Heuristic Multi-Robot Dubins Coverage Path Planning for Known Environ-ments. Sensors. 2023;23(5):2560.
• 13. Li L, Shi D, Jin S, Yang S, Lian Y, Liu H. SP2E: Online spiral cover-age with proactive prevention extremum for unknown environments. Journal of Intelligent & Robotic Systems; 2023; 108(2): 30.
• 14. Lo CC, Yu SW. A two-phased evolutionary approach for intelligent task assignment & scheduling. In 2015 11th international conference on natural computation (ICNC). 2015; 1092–1097.
• 15. Norhafezah K, Nurfadzliana A, Megawati O. Simulation of municipal solid waste route optimization by dijkstra’s algorithm. Journal of Fun-damental and Applied Sciences 9. 2017; 732–747.
• 16. Pawlowski A, Romaniuk S, Kulesza Z, Petrović M. Trajectory optimi-zation using learning from demonstration with meta-heuristic grey wolf algorithm. IAES International Journal of Robotics and Automa-tion (IJRA). 2022; 11(4): 263–277.
• 17. Petrović M, Miljković Z, Jokić A. A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm. Applied Soft Computing 81. 2019;105520.
• 18. Piegl L,Tiller W. The NURBS book. Springer Science & Business Media; 1996.
• 19. Salt L, Howard D, Indiveri G, Sandamirskaya Y. Parameter optimiza-tion and learning in a spiking neural network for uav obstacle avoid-ance targeting neuromorphic processors. IEEE transactions on neu-ral networks and learning systems. 2019; 31(9): 3305–3318.
• 20. Shi J, Liu C, Xi H. Improved d* path planning algorithm based on CA model. Journal of Electronic Measurement & Instrumentation; 2016.
• 21. Singla A, Padakandla S, Bhatnagar S. Memory-based deep rein-forcement learning for obstacle avoidance in uav with limited envi-ronment knowledge. IEEE Transactions on Intelligent Transportation Systems. 2019; 22(1): 107–118.
• 22. Wang Z, Zeng G, Huang B, Fang Z. Global optimal path planning for robots with improved A* algorithm. Journal of Computer Applications. 2019;39(9): 2517.
• 23. Yi Z, Yanan Z, Xiangde L. Path planning of multiple industrial mobile robots based on ant colony algorithm. In 2019 16th International Computer Conference on Wavelet Active Media Technology and In-formation Processing. 2019; 406–409.
• 24. Zhang T, Xu J, Wu B. Hybrid path planning model for multiple robots considering obstacle avoidance. IEEE Access 10. 2022;71914-71935.
• 25. Zhu H, Ouyang H, Xi H. Neural network-based time optimal trajectory planning method for rotary cranes with obstacle avoidance. Mechani-cal Systems and Signal Processing 185. 2023;109777.