Abstract
Autonomous underwater vehicles (AUVs) are underwater robots which are able to perform certain tasks without the help of a human operator. The key skill of each AUV is the capability to avoid collisions. To this end, appropriate devices and software are necessary with the potential to detect obstacles and to take proper decisions from the point of view of both the task and safety of the vehicle. The paper presents a neural collision avoidance system (NCAS) designed for the biomimetic autonomous underwater vehicle (BAUV). The NCAS is a component of the path following and collision avoidance system (PFCAS), which as the name implies is responsible for safely leading the vehicle along a desired path with collision avoidance. The task of NCAS is to make decisions regarding vehicle maneuvers in the horizontal plane, but only in the close proximity of the obstacles. It is implemented as an evolutionary artificial neural network designed by means of a neuro-evolutionary technique called assembler encoding with evolvable operations (AEEO). The paper outlines operation and construction of the BAUV as well as the PFCAS, the role of the NCAS in the entire system, and briefly presents AEEO as well as reporting on the experiments performed in simulation.
Highlights
The underwater environment is increasingly being explored by humans
The rest of the paper is organized as follows: Sect. 2 outlines the real biomimetic autonomous underwater vehicle (BAUV) and its simulation model used in the experiments, Sect. 3 specifies algorithmic PFCAS (APFCAS), path following and collision avoidance system (PFCAS)–neural collision avoidance system (NCAS), and other BAUV components that are involved in obstacle avoidance, Sect. 4 describes assembler encoding with evolvable operations (AEEO), that is, the neuro-evolutionary method applied to design the NCAS, Sect. 5 reports the experiments, and the final section summarizes the paper
If the distance is greater than an assumed threshold, the speed is set to a desired value attached to a description of a goal waypoint, otherwise it is reduced to a small value equal to SPEED_CLOSE_OBSTACLES which, as with other parameters of the APFCAS, was optimized in the evolutionary way during experiments reported in Praczyk (2015a)
Summary
The underwater environment is increasingly being explored by humans. Previously, only submarines, submersibles, and divers in heavy, uncomfortable diving suits visited the marine depths. The current paper is focused only on the latter task, and in general, three approaches can be used for that purpose (Campbell et al 2012; Holtung Eriksen 2015; Tan 2006) The first of these is the so-called deliberative or sense-plan-act approach which is, the global path planning method applied locally. Neural collision avoidance system (NCAS), presented in the current paper, is the representative of the neural reactive approach for underwater vehicles. The NCAS and PFCAS were designed for the AUV as presented in Fig. 1 which, due to its biomimetic drive (fins instead of a traditional screw propeller), is called biomimetic AUV, in short, BAUV The task of this vehicle is generally to follow a predefined path with collision avoidance and pro-. The rest of the paper is organized as follows: Sect. 2 outlines the real BAUV and its simulation model used in the experiments, Sect. 3 specifies APFCAS, PFCAS–NCAS, and other BAUV components that are involved in obstacle avoidance, Sect. 4 describes AEEO, that is, the neuro-evolutionary method applied to design the NCAS, Sect. 5 reports the experiments, and the final section summarizes the paper
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