Abstract
A predictive guidance obstacle avoidance algorithm (PGOA) in unknown environments is proposed for autonomous underwater vehicle (AUV) that must adapt to multiple complex obstacle environments. Using the environmental information collected by the Forward-looking Sonar (FLS), the obstacle boundary is simplified by the convex algorithm and Bessel interpolation. Combining the predictive control secondary optimization function and the obstacle avoidance weight function, the predicting obstacle avoidance trajectory parameters are obtained. According to different types of obstacle environments, the corresponding obstacle avoidance rules are formulated. Lastly, combining with the obstacle avoidance parameters and rules, the AUV’s predicting obstacle avoidance trajectory point is obtained. Then AUV can successfully achieve obstacle avoidance using the guidance algorithm. The simulation results show that the PGOA algorithm can better predict the trajectory point of the obstacle avoidance path of AUV, and the secondary optimization function can successfully achieve collision avoidance for different complex obstacle environments. Lastly, comparing the execution efficiency and cost of different algorithms, which deal with various complex obstacle environments, simulation experiment results indicate the high efficiency and great adaptability of the proposed algorithm.
Highlights
Autonomous underwater vehicle (AUV) [1] is an important tool for marine resource exploitation and marine scientific research [2,3,4]
(2) If the right boundary of the obstacle is outside the sonar beam range, and the left boundary is in the range, the detected obstacle is called the left bounded obstacle (LB)
If there is an emergency obstacle avoidance setting where a bounded obstacle or an unbounded obstacle exists, the current AUV heading is taken as the dividing line to estimate the boundary point of the obstacle that is closer to the virtual target
Summary
Autonomous underwater vehicle (AUV) [1] is an important tool for marine resource exploitation and marine scientific research [2,3,4]. If an AUV works in a locally known but globally unknown environment with various types of obstacles, nonlinear methods are needed to plan out the AUV trajectory points to ensure the safety of AUV in missions To solve this problem, there is an artificial potential field method (APF) [16,17,18], as well as evolutionary algorithms such as the genetic algorithm (GA) [19,20,21] and the particle swarm optimization algorithm (PSO) [22,23]. Based on the previously mentioned obstacle avoidance problems, and combining target search and tracking in unknown underwater environments with complex irregular obstacles, an obstacle avoidance method for AUV based on PGOA is proposed.
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