Motion planning approach for car-like robots in unstructured scenario

Sun, Xuehao; Deng, Shuchao; Zhao, Tingting; Tong, Baohong

doi:10.1177/0142331221994393

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…Global planning is to calculate the global optimal trajectory on the global map (Niu et al, 2021). The solution of local trajectory planning will be based on the surrounding real-time environmental information perceived by the vehicle, and it pays more attention to environmental information, trajectory smoothness, and safety (Sun et al, 2022; Zhong et al, 2020). In this paper, we focus on the method of local trajectory planning.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method

Qin

Liu

Guo

et al. 2023

Transactions of the Institute of Measurement and Control

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To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads.

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Section: Introductionmentioning

confidence: 99%

Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method

Qin

Liu

Guo

et al. 2023

Transactions of the Institute of Measurement and Control

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“…This has high requirements for information processing and calculation, as well as for the performance of the unmanned ship system. Currently, common path-planning methods include artificial potential field approach (APFA) (Zhao et al, 2022), genetic algorithm (Sun et al, 2022b), ant colony algorithm (Wang et al, 2018), and path planning based on RBF neural network (Sun et al, 2018). Nevertheless, due to the complexity and uncertainty of the operating environment, many route planning methods are cumbersome, inefficient, and prone to a large number of errors, so the route planning of USV is still the focus of research (Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Path planning of unmanned surface vehicle based on artificial potential field approach considering virtual target points

Luan,

Tan,

You

et al. 2023

Transactions of the Institute of Measurement and Control

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Aiming at the local minimum problem and target unreachable problems in the path planning of unmanned surface vehicle (USV), a path planning algorithm of USV considering virtual target point is proposed. For the target unreachable problem, a repulsive force potential field function is created. According to the measured distance between the target and the obstacle, the repulsive force of the target is zero, so that the USV can reach the target point. For the local minimum problem, the local minimum caused by various obstacles is analyzed, simulated annealing (SA) and artificial potential field approach (APFA) are combined to solve the minimum point problem caused by general obstacles. For the local minimum problem caused by special U-shaped obstacles, a virtual target point algorithm (VTPA) is established to solve this problem. The simulation results prove that this algorithm solves the problem of long path caused by too large repulsive force. Compared with Dual-Tree Rapidly exploring Random Tree (DT-RRT) algorithm, the efficiency of this algorithm has been significantly improved. Algorithm running time was reduced by 33.4%. Path length was reduced by 17.8%. Not only the time is saved, but also the path planning is optimized, so that the speed of the algorithm is accelerated.

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“…Mohamed et al 12 processed path-following position error and the required magnitude of input velocities to the car-like robot by digital control. Xuehao et al 13 have developed a hybrid motion planning with a smooth velocity curve for the car-like robots to achieve optimal path and emergency dynamic obstacle avoidance. Narcis et al 14 proposed a solution for planning and controlling a car-like robot in environments cluttered has obstacles with low computational cost.…”

Section: Introductionmentioning

confidence: 99%

Path tracking control for car-like robots by pid controller with time-varying parameters

Trinh

Nguyêñ

2022

Sci.Tech.Dev.J.-Eng.Tech.

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Wheel mobile robots have been widely applied to many areas of life, from serving people in services and industrial logistics to military fields. Applications of wheeled mobile robots such as floor cleaning robots, supermarket robots, logistics robots in hospitals, autonomous robots that guide automatic guidance in intelligent factories, military robots and agricultural robots. Thereby, we can see the great potential of mobile robots in the future. However, among the wheel mobile robots, the differential-drive mobile robot structure is most commonly used because of its simplicity in structure and control. Nevertheless, the navigation and driving are done synchronously through the two drive wheels, which leads to slippage and difficulty in controlling posture errors when navigating, especially on roads curved with a small radius. In order to overcome the disadvantages of the steering and navigation of differential-drive mobile robots, the structure of the automobile has been applied to design a wheel-mobile robot called a car-like robot. Many studies have shown that the advantage of this design option is that the navigation control and robot drive are separated. As a result, the dynamic control loop can ignore the navigation part, while the kinematic model determines the navigation law. Therefore, the work proposes the design of a PID controller with time-varying parameters for a car-like robot to track a given trajectory with minimum error. The nonlinear kinematic model of the robot is linearized along a reference trajectory, and the obtained linear model is used in the controller design process. A PID controller is designed where the controller parameters are tuned to minimize the tracking error. Our model achieves a tracking error value, including the minor position is 5.1 mm, and the maximum postural error is 2o. The simulation results showed the proposed controller's effectiveness in position and posture errors, and it shows we can apply this research to control car-like mobile robots in logistics services.

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Motion planning approach for car-like robots in unstructured scenario

Cited by 11 publications

References 16 publications

Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method

Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method

Path planning of unmanned surface vehicle based on artificial potential field approach considering virtual target points

Path tracking control for car-like robots by pid controller with time-varying parameters

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