Abstract
Chemical plume tracing based on autonomous underwater vehicle uses chemical as a guidance to navigate and search in the unknown environments. To solve the key issue of tracing and locating the source, this article proposes a path-planning strategy based on partially observable Markov decision process algorithm and artificial potential field algorithm. The partially observable Markov decision process algorithm is used to construct a source likelihood map and update it in real time with environmental information from the sensors on autonomous underwater vehicle in search area. The artificial potential field algorithm uses the source likelihood map for accurately planning tracing path and guiding the autonomous underwater vehicle to track along the path until the source is detected. This article carries out simulation experiments on the proposed algorithm. The experimental results show that the algorithms have good performance, which is suitable for chemical plume tracing via autonomous underwater vehicle. Compared with the bionic method, the simulation results show that the proposed method has higher success rate and better stability than the bionic method.
Highlights
Olfactory is a long-distance perceptual behavior, which is widely used by animals in search of food and finding mates,[1,2,3] such as Pacific salmon returning to their habitat,[4] Antarctic seabirds foraging,[5] and some insects mating and feeding.[6,7,8] In nature, olfactory plays a vital role in most animals
This article presents a partially observable Markov decision process (POMDP) method used on the probability distribution map of the chemical source and is based on the artificial potential field (APF) method of the autonomous underwater vehicle (AUV) olfaction search positioning path-planning method
When the target is near the obstacle in the traditional APF method, the AUV cannot reach the target location, and the previous method we proposed can avoid this situation
Summary
Olfactory is a long-distance perceptual behavior, which is widely used by animals in search of food and finding mates,[1,2,3] such as Pacific salmon returning to their habitat,[4] Antarctic seabirds foraging,[5] and some insects mating and feeding.[6,7,8] In nature, olfactory plays a vital role in most animals. This article presents a partially observable Markov decision process (POMDP) method used on the probability distribution map of the chemical source and is based on the artificial potential field (APF) method of the AUV olfaction search positioning path-planning method. Establishment of probability distribution map model of chemical source location Tracing and locating chemical plume accords with the characteristics of POMDP method: Chemical source position is the hidden state which cannot be observed, but the AUV’s position is the explicit state which can be observed by localization sensors. Only hidden state c can have the initial belief state b, where b represents whether there is a probability value of the source in the cell, so that we can get the plume source likelihood map in the entire search area by traversing all cells. The probability in both directions is independent of each other, because of the orthogonality of x and y directions, and after some simplification
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