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Constructing a cohesive pattern for collective navigation based on a swarm of robotics.

ABSTRACT: Swarm robotics carries out complex tasks beyond the power of simple individual robots. Limited capabilities of sensing and communication by simple mobile robots have been essential inspirations for aggregation tasks. Aggregation is crucial behavior when performing complex tasks in swarm robotics systems. Many difficulties are facing the aggregation algorithm. These difficulties are as such: this algorithm has to work under the restrictions of no information about positions, no central control, and only local information interaction among robots. This paper proposed a new aggregation algorithm. This algorithm combined with the wave algorithm to achieve collective navigation and the recruitment strategy. In this work, the aggregation algorithm consists of two main phases: the searching phase, and the surrounding phase. The execution time of the proposed algorithm was analyzed. The experimental results showed that the aggregation time in the proposed algorithm was significantly reduced by 41% compared to other algorithms in the literature. Moreover, we analyzed our results using a one-way analysis of variance. Also, our results showed that the increasing swarm size significantly improved the performance of the group.

SUBMITTER: Soliman YA

PROVIDER: S-EPMC8323727 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Constructing a cohesive pattern for collective navigation based on a swarm of robotics.

Soliman Yehia A YA Abdulkader Sarah N SN Mohamed Taha M TM

PeerJ. Computer science 20210727

Swarm robotics carries out complex tasks beyond the power of simple individual robots. Limited capabilities of sensing and communication by simple mobile robots have been essential inspirations for aggregation tasks. Aggregation is crucial behavior when performing complex tasks in swarm robotics systems. Many difficulties are facing the aggregation algorithm. These difficulties are as such: this algorithm has to work under the restrictions of no information about positions, no central control, a ...[more]

PMID: 34395863

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Project description:BackgroundThe combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI).MethodsIn this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents.ResultsThe entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface.ConclusionsThe integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.

Dataset Information

Constructing a cohesive pattern for collective navigation based on a swarm of robotics.

Publications

Constructing a cohesive pattern for collective navigation based on a swarm of robotics.

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