References
- 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.
- Alymani M, Alsolai H, Maashi M, Alhebri A, Alshahrani H, Al-Wesabi FN, Mohamed A, Hamza MA. Dispersal foraging strategy with cuckoo search optimization based path planning in unmanned aerial vehicle networks. IEEE Access 11. 2023; 31365–31372.
- Bozek P, Karavaev YL, Ardentov AA, Yefremov KS. Neural network control of a wheeled mobile robot based on optimal trajectories. International journal of advanced robotic systems 2020;17: 2.
- Cao X, Zuo F. A fuzzy-based potential field hierarchical reinforcement learning approach for target hunting by multi-auv in 3-d underwater environments. International Journal of Control 2021;94(5):1334–1343.
- Duan S, Wang Q, Han X. Improved a-star algorithm for safety insured optimal path with smoothed corner turns. Journal of Mechanical Engineering. 2020;56(18): 205–215.
- 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.
- Gosiewski Z, Kwaśniewski K. Time minimization of rescue action realized by an autonomous vehicle. Electronics 9. 2020;12: 2099.
- Halliday D, Resnick R, Walker J. Fundamentals of physics. John Wiley & Sons; 2013.
- 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.
- Kwaśniewski KK, Gosiewski Z. Genetic algorithm for mobile robot route planning with obstacle avoidance. acta mechanica et automatica. 2018; 12(2): 151–159.
- 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.
- 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 Environments. Sensors. 2023;23(5):2560.
- Li L, Shi D, Jin S, Yang S, Lian Y, Liu H. SP2E: Online spiral coverage with proactive prevention extremum for unknown environments. Journal of Intelligent & Robotic Systems; 2023; 108(2): 30.
- 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.
- Norhafezah K, Nurfadzliana A, Megawati O. Simulation of municipal solid waste route optimization by dijkstra’s algorithm. Journal of Fundamental and Applied Sciences 9. 2017; 732–747.
- Pawlowski A, Romaniuk S, Kulesza Z, Petrović M. Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm. IAES International Journal of Robotics and Automation (IJRA). 2022; 11(4): 263–277.
- 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.
- Piegl L,Tiller W. The NURBS book. Springer Science & Business Media; 1996.
- Salt L, Howard D, Indiveri G, Sandamirskaya Y. Parameter optimization and learning in a spiking neural network for uav obstacle avoidance targeting neuromorphic processors. IEEE transactions on neural networks and learning systems. 2019; 31(9): 3305–3318.
- Shi J, Liu C, Xi H. Improved d* path planning algorithm based on CA model. Journal of Electronic Measurement & Instrumentation; 2016.
- Singla A, Padakandla S, Bhatnagar S. Memory-based deep reinforcement learning for obstacle avoidance in uav with limited environment knowledge. IEEE Transactions on Intelligent Transportation Systems. 2019; 22(1): 107–118.
- 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.
- 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 Information Processing. 2019; 406–409.
- Zhang T, Xu J, Wu B. Hybrid path planning model for multiple robots considering obstacle avoidance. IEEE Access 10. 2022;71914–71935.
- Zhu H, Ouyang H, Xi H. Neural network-based time optimal trajectory planning method for rotary cranes with obstacle avoidance. Mechanical Systems and Signal Processing 185. 2023;109777.