Biologically Inspired Intelligence with Applications on Robot Navigation

Luo, Chaomin; Jan, Gene En; Chu, Zhenzhong; Li, Xinde

doi:10.5772/intechopen.75692

Cited by 5 publications

(3 citation statements)

References 59 publications

(129 reference statements)

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“…Many cases used this method and showed effective results to enable the robots to cover an area. The computations of the model are easy, and the path of the robot is created without searching over the global free workspace or any global cost functions [56].…”

Section: A Bio-inspired Neural Network Approachmentioning

confidence: 99%

A Survey on Swarm Robotics for Area Coverage Problem

Muhsen,

Sadiq,

Raheem

2023

Algorithms

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The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

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Section: A Bio-inspired Neural Network Approachmentioning

confidence: 99%

A Survey on Swarm Robotics for Area Coverage Problem

Muhsen,

Sadiq,

Raheem

2023

Algorithms

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“…Комбіновані алгоритми поєднують різні підходи, наприклад, нейронні мережі та алгоритми ройового інтелекту [27] або відстеження хімічного сліду та алгоритми ройового інтелекту [28].…”

Section: рис 2 класифікація біонатхненних алгоритмівunclassified

Bio-inspired methods for planning the path of mobile robots

Gurko¹,

Hurko²

2022

Bulletin of KhNAHU

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Problem. The issue of path planning for a mobile robot is one of the most important ones of mobile robotics. Proper path planning ensures the safety of the robot and its environment, the efficiency of the tasks carried out by a robot, saves time and energy consumption for these tasks, etc. Therefore, research is constantly conducted on the implementation of new and improving existing optimization methods for the path planning for a mobile robot. The utilization of classical optimization methods is limited by their significant drawbacks, such as computational complexity and long time for searching the optimal path. To eliminate these issues, heuristic and then metaheuristic methods have been developed. Among metaheuristic methods, bio-inspired optimization methods, which are based on evolutionary processes in nature, as well as the behaviour of living organisms, are becoming increasingly popular. Goal. This paper aims to analyse the most popular bio-inspired algorithms used for mobile robot path planning. Methodology. The paper briefly reviews the bio-inspired optimization methods that are applicable to the path planning of a mobile robot. Particular emphasis is given to swarm intelligence algorithms, in which the relatively simple behaviour of individual agents interacting with each other and with the environment allows a swarm of these agents to achieve a given goal. Results. A classification of bio-inspired optimization methods used for mobile robot path planning is proposed. Pseudocodes for swarm optimization algorithms that are most frequently applied in mobile robotics are presented. Originality. This paper is one of the first in Ukraine to offer a comprehensive overview of bio-inspired methods of optimization used for mobile robot path planning. Practical value. The implementation of the considered algorithms in mobile robot control systems will improve the efficiency of robots in performing their assigned tasks. The given pseudocodes will simplify the development of software to implement the mentioned algorithms.

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“…The search strategies exhibited by animals can nonetheless be thought of as coverage algorithms since animals tend to cover the entire environment in a constant effort to search for their potential food source or prey. Several recent works which propose coverage algorithms inspired by animal behavior are given in [11][12][13][14]. Statistical analysis for search strategies observed in animals is given in [15], where the authors compare two variants of the random walk model, correlated random walks (CRWs) and Levy walks (LWs).…”

Section: Introductionmentioning

confidence: 99%

A bio-inspired localization-free stochastic coverage algorithm with verified reachability

et al. 2021

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Dependence on constant availability to an external localization service is often unreliable and infeasible in mobile robots. In this paper, we take inspiration from a continuous fish motion model, the persistent turning Walker (PTW), to devise a strategy which is able to achieve 2D and 3D coverage in an unknown environment in the absence of a localization service, such as a global positioning system (GPS). This is achieved by converting the continuous-time dynamical system into a discrete-time Markov chain which is then shown to exhibit strongly connected properties that are verifiable through numerical methods. The aforementioned proposed framework can also be used to study the continuous-time dynamics of other biological systems and evaluate their properties. The performance of the PTW model is also compared with two existing random search strategies, simple random walks (SRW) and correlated random walks (CRW) by using analytical bounds, simulation results, and statistical tests. The simulation results show that the proposed PTW algorithm covers a given search-space at a faster rate compared to the CRW and SRW models. Hence, the PTW may be effectively used as a coverage strategy by mobile robots in underwater or underground environments where the availability of a GPS cannot be guaranteed at all times.

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Biologically Inspired Intelligence with Applications on Robot Navigation

Cited by 5 publications

References 59 publications

A Survey on Swarm Robotics for Area Coverage Problem

A Survey on Swarm Robotics for Area Coverage Problem

Bio-inspired methods for planning the path of mobile robots

A bio-inspired localization-free stochastic coverage algorithm with verified reachability

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