Cognitive response navigation algorithm for mobile robots using biological antennas

Jiang, Jiliang; Tu, Dawei; Xu, Shiyi; Zhao, Qijie

doi:10.1017/s0263574713000970

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…It can be understood by comparing Equations (10) and (12) that the average energy of the single-robot system will be lesser if and only if Equation 13is satisfied. It is assumed that AD is zero in the case of a single-robot system to account for the worst-case scenario.…”

Section: Energy Expenditurementioning

confidence: 99%

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Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

2020

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This paper addresses the development and implementation of an obstacle avoidance strategy for a multi-robot system operating in an unknown environment. This novel strategy is based on the conventional Bug-1 obstacle avoidance algorithm, which is a non-heuristic method for obstacle avoidance in an unknown environment. In the Bug-1 algorithm, a robot circumnavigates the obstacle to find the coordinates of the point, having minimum distance to the goal. In the case of the new strategy, two robots will circumnavigate the obstacle in such a manner that it will reduce both the total travel time and the distance traveled. Information acquired by the individual robots during the circumnavigation is shared across other robots to accomplish the obstacle avoidance efficiently. A theoretical analysis is carried out to show the improvement in travel time and energy expenditure of the robots in implementing the new strategy. Different test scenarios for comparing the performance of the obstacle avoidance strategies using simulations is also identified. The simulation studies using these scenarios suggest that the new algorithm is a better algorithm with respect to multi-robot obstacle avoidance. The experimental study conducted also shows that robots using this new algorithm have a better travel time and less energy expenditure than the conventional Bug-1 algorithm.

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Section: Energy Expenditurementioning

confidence: 99%

“…Several other variants of the Bug algorithm were developed later with improvements in function. IBA, 9 K-Bug, 10 DH-Bug, 11 Bio-Bug, 12 etc., are some of the other variants of the Bug algorithm. A study regarding the working of different Bug algorithms and their comparisons is provided.…”

Section: Introductionmentioning

confidence: 99%

Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

2020

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“…Zhu et al [24] proposed the Distance Histogram Bug (DH-Bug), which designed a new pattern and conversion condition so that it could deal with unknown mobile obstacles in a dynamic environment. Jiang et al [25] put forward a BioBug algorithm based on the principle of neuroethological behavior, using the biological antenna model to describe the interested detected area, thereby reducing the burden of calculation and path length. Athanasios et al [26] proposed a Ladybug algorithm based on intensified localization that, using the received signal strength indication (RSSI) of the electromagnetic signal, can accurately calculate the label location of the signal, thereby improving the smoothness of the path.…”

Section: Introductionmentioning

confidence: 99%

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

Wang,

Yin,

Jing

2023

JMSE

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Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In this paper, the global static path is planned using a parallel line scan search. When encountering unknown obstacles, the improved Bug algorithm is used for local dynamic path planning according to the sensor detection information. This paper first sets up the safe expansion area to ensure the safety of the USV during the obstacle avoidance process and optimizes the movement direction considering the operation and behavior characteristics of the USV. To meet the requirement of USV steering, the Bezier curve is used to smooth the path points, which greatly improves the smoothness of the path. In this paper, the multi-mode switching strategy of the Bug algorithm based on obstacle boundary width obtained by the sensor is proposed, which ensures no area omissions and meets the requirement of search area coverage during the process of bypassing obstacles. The simulation results show that the improved Bug algorithm can maintain a safe distance along the obstacle boundary to bypass the obstacle. Moreover, the improved Bug algorithm effectively improves the path oscillation phenomenon of traditional Bug and shortens the path length and operating time. Finally, through the global search path planning simulation and comparison experiments, the effectiveness of the proposed method is verified.

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Cognitive response navigation algorithm for mobile robots using biological antennas

Cited by 2 publications

References 21 publications

Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

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