JIA C H , HUANG M H , SUI L Y . An autonomous vehicle motion planning method based on dynamic programming [C ] //Proceedings of the 2023 17th International Computer Conference on Wavelet Active Media Technology and Information Processing. Chengdu, China:IEEE , 2020 : 394 - 398 .

JIA H B , ZHANG L , WANG Z P . A dynamic lane-changing trajectory planning scheme for autonomous vehicles on structured road [C ] // Proceedings of the 2020 IEEE 9th International Power Electronics and Motion Control Conference. Nanjing, China:IEEE , 2020 : 2222 - 2227 .

BETZ J , ZHENG H R , LINIGER A , et al. Autonomous vehicles on the edge: A survey on autonomous vehicle racing [J ] . IEEE Open Journal of Intelligent Transportation Systems , 2022 , 3 : 458 - 488 . DOI: 10.1109/OJITS.2022.3181510 http://doi.org/10.1109/OJITS.2022.3181510 https://ieeexplore.ieee.org/document/9790832/ https://ieeexplore.ieee.org/document/9790832/

LIU B X , LIU C . Path planning of mobile robots based on improved RRT algorithm [J ] . Journal of Physics: Conference Series , 2022 , 2216 : 012020 . DOI: 10.1088/1742-6596/2216/1/012020 http://doi.org/10.1088/1742-6596/2216/1/012020 Aiming at the shortcomings of RRT (Rapidly-exploring Research Tree) algorithm such as long time cost and low utilization of sampling points, an improved RRT algorithm is proposed. By adopting the sampling strategy based on dynamic probability, the robot is prevented from falling into a local minimum during the sampling process. At the same time, the adopt of variable step strategy as the random tree expands reduces the number of sampling points. Finally, the initial path is optimized to make it more suitable for the robot to walk. The improved RRT algorithm is compared with the RRT algorithm and the Goal-bias RRT algorithm in both simulations on MATLB and experiments on robot based on ROS (Robot Operating System). And the results show that improved RRT algorithm increases the speed of path planning, reduces the length of path, and the planned path is smoother and more suitable for the real robot to move.

SONG P C , PAN J S , CHU S C . A parallel compact cuckoo search algorithm for three-dimensional path planning [J ] . Applied Soft Computing , 2020 , 94 : 106443 . DOI: 10.1016/j.asoc.2020.106443 http://doi.org/10.1016/j.asoc.2020.106443 https://linkinghub.elsevier.com/retrieve/pii/S1568494620303835 https://linkinghub.elsevier.com/retrieve/pii/S1568494620303835

SUN Y H , FANG M , SU Y X . AGV path planning based on improved Dijkstra algorithm [J ] . Journal of Physics: Conference Series , 2021 , 1746 : 012052 . DOI: 10.1088/1742-6596/1746/1/012052 http://doi.org/10.1088/1742-6596/1746/1/012052 Aiming at the path planning problem of Automated Guided Vehicle (AGV) in intelligent storage, an improved Dijkstra algorithm that combines eight-angle search method and Dijkstra algorithm for path optimization is proposed. The grid method is used to model the storage environment, and the improved Dijkstra algorithm is used to optimize the route of the AGV. The simulation test of the AGV path planning process with Matlab shows that the AGV can effectively avoid obstacles by using the traditional Dijkstra algorithm and the improved Dijkstra algorithm, and then search for a collision-free optimized path from the start point to the end point; and the traditional Dijkstra algorithm In comparison, the path length planned by the improved Dijkstra algorithm is shorter and the turning angle is less, indicating that the improved algorithm is correct, feasible and effective, and has a strong global search ability.

LI B C , DONG C Y , CHEN Q M , et al. Path planning of mobile robots based on an improved A * algorithm [C ] // Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence. Qingdao , China : ACM , 2020 : 49 - 53 .

SUN J X , XIN L . Research on path planning of AGV based on improved ant colony optimization algorithm [C ] // Proceedings of the 2021 33rd Chinese Control and Decision Conference. Kunming , China : IEEE , 2021 : 7567 - 7572 .

GUO P , JIANG B . Research on path planning of three-dimensional UAV based on levy flight strategy and improved particle swarm optimization algorithm [C ] // Proceedings of the 2020 7th International Conference on Information Science and Control Engineering. Changsha , China : IEEE , 2020 : 1199 - 1203 .

ANDRIY D , SERGII R , DARYA V . Simulation tool for the drone trajectory planning based on genetic algorithm approach [C ] // Proceedings of the 2020 IEEE KhPI Week on Advanced Technology. Kharkiv , Ukraine : IEEE , 2020 : 387 - 390 .

BAO B L . Research on real-time path planning and obstacle avoiding for mobile robot swarms based on an advanced artificial potential field method [C ] // Proceedings of the 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms. Changchun , China : IEEE , 2023 : 1747 - 1752 .

YUAN X Y . Research on the limitations of UAV path planning based on artificial potential field method [C ] // Proceedings of the 2022 9th International Forum on Electrical Engineering and Automation. Zhuhai , China : IEEE , 2022 : 619 - 622 .

TAN X , CORTEZ W S , DIMAROGONAS D V . High-order barrier functions:Robustness, safety, and performance-critical control [J ] . IEEE Transactions on Automatic Control , 2021 , 67 ( 6 ): 3021 - 3028 . DOI: 10.1109/TAC.2021.3089639 http://doi.org/10.1109/TAC.2021.3089639 https://ieeexplore.ieee.org/document/9456981/ https://ieeexplore.ieee.org/document/9456981/

AMES A D , COOGAN S , EGERSTEDT M , et al. Control barrier functions: theory and applications [C ] // Proceedings of the 2019 18th EuropeanControl Conference. Naples , Italy : IEEE , 2019 : 3420 - 3431 .

TAYLOR A J , AMES A D . Adaptive safety with control barrier functions [C ] // Proceedings of the 2020 American Control Conference. Denver, CO , US : IEEE , 2020 : 1399 - 1405 .

KOSURU V S R , VENKITARAMAN A K . Developing a deep Q-learning and neural network framework for trajectory planning [J ] . European Journal of Engineering and Technology Research , 2022 , 7 ( 6 ): 148 - 157 . DOI: 10.24018/ejeng.2022.7.6.2944 http://doi.org/10.24018/ejeng.2022.7.6.2944 https://ej-eng.org/index.php/ejeng/article/view/2944 https://ej-eng.org/index.php/ejeng/article/view/2944 With the recent expansion in Self-Driving and Autonomy field, every vehicle is occupied with some kind or alter driver assist features in order to compensate driver comfort. Expansion further to fully Autonomy is extremely complicated since it requires planning safe paths in unstable and dynamic environments. Impression learning and other path learning techniques lack generalization and safety assurances.  Selecting the model and avoiding obstacles are two difficult issues in the research of autonomous vehicles. Q-learning has evolved into a potent learning framework that can now acquire complicated strategies in high-dimensional contexts to the advent of deep feature representation.  A deep Q-learning approach is proposed in this study by using experienced replay and contextual expertise to address these issues. A path planning strategy utilizing deep Q-learning on the network edge node is proposed to enhance the driving performance of autonomous vehicles in terms of energy consumption. When linked vehicles maintain the recommended speed, the suggested approach simulates the trajectory using a proportional-integral-derivative (PID) concept controller. Smooth trajectory and reduced jerk are ensured when employing the PID controller to monitor the terminals. The computational findings demonstrate that, in contrast to traditional techniques, the approach could investigate a path in an unknown situation with small iterations and a higher average payoff. It can also more quickly converge to an ideal strategic plan.

YANG Y X , CHEN Z X . Optimization of dynamic obstacle avoidance path of multirotor UAV based on ant colony algorithm [J ] . Wireless Communications and Mobile Computing , 2022 ( 3 ): 1 - 9 .

SWDDAOUI A , SAAJ C M . Collision-free optimal trajectory generation for a space robot using genetic algorithm [J ] . Acta Astronautica , 2021 , 179 : 311 - 321 . DOI: 10.1016/j.actaastro.2020.11.001 http://doi.org/10.1016/j.actaastro.2020.11.001 https://linkinghub.elsevier.com/retrieve/pii/S0094576520306457 https://linkinghub.elsevier.com/retrieve/pii/S0094576520306457

WANG D , LI C H , GUO N , et al. Local path planning of mobile robot based on artificial potential field [C ] // Proceedings of the 2020 39th Chinese Control Conference. Shenyang , China : IEEE , 2020 : 3677 - 3682 .

杜宏宝 , 王正杰 , 唐礼喜 , 等 . 基于控制障碍函数的飞行器避障与制导控制 [J ] . 兵工学报 , 2023 , 44 ( 9 ): 2814 - 2823 . DOI: 10.12382/bgxb.2022.1002 http://doi.org/10.12382/bgxb.2022.1002 为实现动态障碍物环境下飞行器对目标的打击以提高其工程适用性,建立飞行器与障碍物以及飞行器与目标的运动学模型。在控制障碍函数的基础上进行拓展,结合速度障碍法对飞行器速度施加约束,与现有的比例制导律构建二次优化问题,得到闭式解形式的避障制导律。数值仿真并分析轨迹和过载曲线的特性。研究结果表明:所提出的避障制导律可以实时地避开运动障碍物并打击目标,有较好的全局特性;与已有避障制导律相比,过载曲线更加平缓且饱和过载占比小。

DU H B , WANG Z J , TANG L X , et al. Control barrier function-based control for aircraft avoidance and guidance with a dynamic obstacle [J ] . Acta Armamentarii , 2023 , 44 ( 9 ): 2814 - 2823 . (in Chinese)

SRINIVASAN M , DABHOLKAR A , COOGAN S , et al. Synthesis of control barrier functions using a supervised machine learning approach [C ] // Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas, ND , US : IEEE , 2020 : 7139 - 7145 .

TAYLOR A , SINGLETARY A , YUE Y , et al. Learning for safety-critical control with control barrier functions [C ] // Proceedings of the 2nd Learning for Dynamics and Control. Cambridge, MA , US : PMLR , 2020 : 708 - 717 .

龙离军 , 胡腾飞 . CLF-CBF-QP新形式下非线性系统的安全攸关控制与优化 [J ] . 控制理论与应用 , 2022 , 39 ( 8 ): 1387 - 1396 .

LONG L J , HU T F . Safety-critical control and optimization of nonlinear systems based on new forms of CLF-CBF-QP [J ] . Control Theory & Applications , 2022 , 39 ( 8 ): 1387 - 1396 . (in Chinese)