Neural anti-collision system for Autonomous Surface Vehicle

Praczyk, Tomasz

doi:10.1016/j.neucom.2014.08.018

Cited by 64 publications

(36 citation statements)

References 17 publications

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“…It reflects the state of the vehicle and its readiness to changing the maneuver, and the BAUV simply has certain inertia which has to be taken into account when controlling the vehicle. 5 Parameters of evolutionary process given in Table 2 and applied in the experiments are the most successful parameters applied in experiments reported in Praczyk (2015b).…”

Section: Methodsmentioning

confidence: 99%

“…The consequence was the decision to modify the PFCAS by changing the method for determining collision-free maneuvers in the horizontal plane. Due to the successful application of ANNs in a collision avoidance system designed for an autonomous surface vehicle (ASV) (Praczyk 2015b), the decision was made to apply ANNs in the BAUV. In the experiments reported in Praczyk (2015b), it appeared that ANNs used to control the ASV successfully coped with avoiding collisions with both stationary and moving obstacles.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the successful application of ANNs in a collision avoidance system designed for an autonomous surface vehicle (ASV) (Praczyk 2015b), the decision was made to apply ANNs in the BAUV. In the experiments reported in Praczyk (2015b), it appeared that ANNs used to control the ASV successfully coped with avoiding collisions with both stationary and moving obstacles. In addition to the effectiveness itself, a key feature of ANNs which decided about their use in ASV was their high processing speed compared to planning methods.…”

Section: Introductionmentioning

confidence: 99%

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Neural collision avoidance system for biomimetic autonomous underwater vehicle

Praczyk

2019

Soft Comput

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural collision avoidance system for biomimetic autonomous underwater vehicle

Praczyk

2019

Soft Comput

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“…Brought to you by | MIT Libraries Authenticated Download Date | 5/11/18 10:22 PM Neural Network Definition Matrix cNDM [12], while the coding network or networks encode the FSNA (i.e. coding networks fills elements of matrices representing this system).…”

Section: General Idea Of Ccga-inementioning

confidence: 99%

“…Fig. 5 shows a method for generating a coding neural network (defined by cNDM) [12] using the information stored in the chromosome, consisting of four components. Each component is composed of 7 bits, i.e.…”

Section: General Idea Of Ccga-inementioning

confidence: 99%