Multi-Robot Coverage: A Bee Pheromone Signalling Approach

Caliskanelli, Ipek; Broecker, Bastian; Tuyls, Karl

doi:10.1007/978-3-319-18084-7_10

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…The BCO is another swarm intelligence based on a bioinspired machine learning algorithm similar to ACO and PSO. Caliskanelli et al [248] introduced a pheromone signaling algorithm based on BCO [249] for multi-robot coverage as well as a hybrid BCO-ACO pheromone signaling technique to solve the loss of communication network problems in multiple robots [250]. Firefly algorithm (FA) is a nature-inspired optimization algorithm [251] that has been widely used in coverage and exploration of the unknown area, especially mine disarming tasks [252,253].…”

Section: ) Swarm Intelligencementioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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Section: ) Swarm Intelligencementioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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“…If a robot receives pheromone, it makes a decision to move and selects a target destination in the opposite direction of the pheromone received. The movement decision is based on vector addition; further description can be found in [4].…”

Section: Beepco Principlementioning

confidence: 99%

“…-BeePCo: The robots execute the bee-pheromone signalling principle [4] for coordination, as described in Section 3.2. -BeePCo-with-rotation: The BeePCo algorithm is extended with a rotational move.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Hybrid Insect-Inspired Multi-Robot Coverage in Complex Environments

Broecker

Caliskanelli

Tuyls

et al. 2015

Towards Autonomous Robotic Systems

Self Cite

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Abstract. Coordination is one of the most challenging research issues in distributed multi-robot systems (MRS), aiming to improve performance, energy consumption, robustness and reliability of a robotic system in accomplishing complex tasks. Social insect-inspired coordination techniques achieve these goals by applying simple but effective heuristics from which elegant solutions emerge. In our previous research, we demonstrated the effectiveness of a hybrid ant-and-bee inspired approach, HybaCo, designed to provide coordinated multi-robot solutions to area coverage problems in simple environments. In this paper, we extend this work and illustrate the effectiveness of our hybrid ant-and-bee inspired approach (HybaCo) in complex environments with static obstacles. We evaluate both the ant-inspired (StiCo) and bee-inspired (BeePCo) approaches separately, and then compare them according to a number of performance criteria using a high-level simulator. Experimental results indicate that HybaCo improves the area coverage uniformly in complex environments as well as simple environments.

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“…In terms of multi-agent coverage control, based on honeybee colony behavior, a bee pheromone signaling approach has been applied to the multi-agent coverage issues [28], which had realized the robots to maximise cover the total working area. By analyzing the problems of Voronoi coverage with non-convex environments, Breitenmoser et al [29] designed a navigation algorithm based on the Lloyd and the Tangent Bug algorithm, and guaranteed a team of robots to converge to a local area.…”

Section: Introductionmentioning

confidence: 99%

The Application of the Multi-Agent Coverage and Self-Healing Control Based on a Swarm Intelligence SONM and Potential Function Approach

et al. 2020

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Self-organizing network will probably be a priority choice for the multi-agent systems because of its good features of configuration, management, optimization and self-healing. In this paper, a swarm intelligence coverage and self-healing method based on the Self-Organizing Network Model (SONM) and potential function is proposed for our multi-agent system. The SNOM is presented with a hierarchical network structure, which mainly includes the link layer design, route establishment and route maintenance. Based on the SNOM, the multi-agent coverage and self-healing control law is devised, and the potential function is designed to realize the collision avoidance, coverage and self-healing control for the multi-agent systems. The proposed approaches are implemented and evaluated by extensive simulations and experiments, and the results confirm that it is feasibility and efficiency in multi-agent coverage and self-healing control.INDEX TERMS Multi-agent system, self-organizing network model, potential function, self-healing control.

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Multi-Robot Coverage: A Bee Pheromone Signalling Approach

Cited by 9 publications

References 24 publications

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Hybrid Insect-Inspired Multi-Robot Coverage in Complex Environments

The Application of the Multi-Agent Coverage and Self-Healing Control Based on a Swarm Intelligence SONM and Potential Function Approach

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