Target following and dynamic obstacle avoidance control method for speed difference slip steering vehicle

Abstract

The invention belongs to the technical field of unmanned driving, and discloses a target following and dynamic obstacle avoidance control method for a speed difference slip steering vehicle, and the method comprises the steps: building four neural networks through employing a depth determinacy strategy in reinforcement learning; constructing a cost range of the obstacle so as to determine a single-step reward function of the action; determining continuous action output through an actor-critic strategy, and updating network parameters continuously through gradient transmission; and training a network model for following and obstacle avoidance according to the current state. According to the method, the intelligence of vehicle following and obstacle avoidance is improved, and the method canbetter adapt to an unknown environment and well cope with other emergencies. the complexity of establishing a simulation environment in the reinforcement learning training process is reduced. By utilizing a neural network prediction model trained in advance, the position and posture of each step of the target vehicle and the obstacle can be obtained according to the initial position and posture ofthe target and the obstacle and the action value of each step, so that the simulation accuracy and efficiency are improved.

Description

Technical field [0001] The invention belongs to the technical field of unmanned driving, and in particular relates to a target following and dynamic obstacle avoidance control method of a speed difference slip steering vehicle. Background technique [0002] At present, the closest existing technology: traditional methods for target following and obstacle avoidance mainly adopt path planning and path tracking control methods. That is, a path is planned first based on the current target state and obstacle information, and then the trajectory tracking control method is used to control the vehicle to travel along the planned path, and the local path planning method is adopted for dynamic obstacle avoidance. When trajectory tracking is usually carried out in a constant speed mode, a combination of path planning and trajectory tracking is adopted, and due to the complexity of the control of the speed difference slip steering vehicle, it cannot be well adjusted in real time according to...

AI Technical Summary

Problems solved by technology

[0005] (1) When performing trajectory tracking, it is usually a constant speed mode, which adopts a combination of path planning and trajectory tracking, and due to the complexity of the control of the speed difference slip steering vehicle, it cannot be well timed according to the dynamic changes of targets and obstacles. Adjust the charged vehicle

[0006] (2) DDPG algorithm in reinforcement learning When the agent and the environment it interacts with are complex, the design of the reward function will also be difficult

At the same time, improper reward function settings will also make the actions output by the learning model unable to effectively solve the problem, greatly reducing the training effect

[0007] Difficulties in solving the above technical problems: (1) For wheeled differential slip steer vehicles, based on traditional path planning and trajectory tracking methods, there will be great uncertainty, and there is little room for improvement in control accuracy, making it difficult to cope with various an emergency

(2) When the DDPG algorithm in reinforcement learning solves complex environmental problems, it is difficult to design a reward function, which will lead to a large deviation between the training effect and the ideal situation

(3) During the training process, in order to obtain the real trajectory information, the target vehicle and obstacles must be loaded, which makes the calculation efficiency of the system slow down

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