A dynamic path planning method based on a gated recurrent unit-recurrent neural network model is proposed for the problem of path planning of a mobile robot in an unknown space. A deep neural network with sensor input is used to generate a new control strategy output to the physical model to control the movement of the robot and thus achieve collision avoidance behavior. Inputs and tags are derived from sample sets generated by an improved artificial potential field and an improved ant colony optimization algorithm. In order to make the ant colony algorithm converge quickly, the pheromone trail and the state transition probability are improved. The field function of the artificial potential field method is modified. Using the end-to-end network model to learn the mapping between input and output in the sample data, the direction and speed of the mobile robot are obtained. The simulation experiments and realistic simulations show that the network model can plan a reasonable path in an unknown environment. Compared with other traditional path planning algorithms, the proposed method is more robust than the traditional path planning algorithms to differences in the robot structure. INDEX TERMS Mobile robot, gated recurrent unit-recurrent neural network, dynamic path planning, ant colony optimization, artificial potential field.
In a complex underwater environment, finding a viable, collision-free path for an autonomous underwater vehicle (AUV) is a challenging task. The purpose of this paper is to establish a safe, real-time, and robust method of collision avoidance that improves the autonomy of AUVs. We propose a method based on active sonar, which utilizes a deep reinforcement learning algorithm to learn the processed sonar information to navigate the AUV in an uncertain environment. We compare the performance of double deep Q-network algorithms with that of a genetic algorithm and deep learning. We propose a line-of-sight guidance method to mitigate abrupt changes in the yaw direction and smooth the heading changes when the AUV switches trajectory. The different experimental results show that the double deep Q-network algorithms ensure excellent collision avoidance performance. The effectiveness of the algorithm proposed in this paper was verified in three environments: random static, mixed static, and complex dynamic. The results show that the proposed algorithm has significant advantages over other algorithms in terms of success rate, collision avoidance performance, and generalization ability. The double deep Q-network algorithm proposed in this paper is superior to the genetic algorithm and deep learning in terms of the running time, total path, performance in avoiding collisions with moving obstacles, and planning time for each step. After the algorithm is trained in a simulated environment, it can still perform online learning according to the information of the environment after deployment and adjust the weight of the network in real-time. These results demonstrate that the proposed approach has significant potential for practical applications.
Path planning is one of the important autonomy abilities for autonomous underwater vehicle (AUV), whose main purpose is to plan an optimized and safety path autonomously during long-range navigation in an unknown environment. This paper proposes two path planners based on quantum ant colony optimization (QACO) and hybrid QACO for AUV in real time based on a sensor detection window. When AUV detects unknown static obstacles, the online path planners are scheduled to plan out a new path to avoid obstacles in the optimization window. To limit the yaw angle, a nonlinear fitness function is defined. In the hybrid QACO, an adaptive quantum gate and improved rules of pheromone updating are proposed according to the movement characteristic of AUV in the process of obstacle avoidance. A local search method is combined with QACO to improve the quality of the path planned by QACO and obtain a smoother path. Finally, the simulation experiments compare the performance of the proposed path planning methods with ant colony optimization.
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