Optimal Transport-based Coverage Control for Swarm Robot Systems: Generalization of the Voronoi Tessellation-based Method*

ABSTRACTThe persistent coverage control problem is formulated based on cell discretisation of two-dimensional mission space and time-increasing cell ages. A new performance function is defined to represent the coverage level of the mission space, and time behaviour is evaluated by the probabilistic method based on the detection model of agents. For comparison, persistent coverage controllers are designed by a target-based approach and a reactive approach. Both controllers are designed in a distributed manner using Voronoi tessellation and Delaunay graph-based local information sharing. Numerical simulation is performed to analyse the evaluated mean age of cells and evaluated coverage level over time for the designed persistent coverage controllers. The differences between the evaluation model and simulation situation are discussed.

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Sharing Experience for Behavior Generation of Real Swarm Robot Systems Using Deep Reinforcement Learning

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2019.p0520 ◽

2019 ◽

Vol 31 (4) ◽

pp. 520-525 ◽

Cited By ~ 1

Author(s):

Toshiyuki Yasuda ◽

Kazuhiro Ohkura ◽

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Keyword(s):

Reinforcement Learning ◽

Collective Behavior ◽

Design Methodology ◽

Learning Performance ◽

Centralized Control ◽

Robot System ◽

Swarm Robots ◽

Robot Systems ◽

Swarm Robot ◽

Typical Design

Swarm robotic systems (SRSs) are a type of multi-robot system in which robots operate without any form of centralized control. The typical design methodology for SRSs comprises a behavior-based approach, where the desired collective behavior is obtained manually by designing the behavior of individual robots in advance. In contrast, in an automatic design approach, a certain general methodology is adopted. This paper presents a deep reinforcement learning approach for collective behavior acquisition of SRSs. The swarm robots are expected to collect information in parallel and share their experience for accelerating their learning. We conducted real swarm robot experiments and evaluated the learning performance of the swarm in a scenario where the robots consecutively traveled between two landmarks.

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Behavior-Based Formation Control of Swarm Robots

Mathematical Problems in Engineering ◽

10.1155/2014/205759 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 18

Author(s):

Dongdong Xu ◽

Xingnan Zhang ◽

Zhangqing Zhu ◽

Chunlin Chen ◽

Pei Yang

Keyword(s):

Formation Control ◽

Large Scale ◽

Research Field ◽

Initial Formation ◽

Swarm Robots ◽

Robot Systems ◽

Potential Applications ◽

Behavior Based ◽

Swarm Robot ◽

Searching Method

Swarm robotics is a specific research field of multirobotics where a large number of mobile robots are controlled in a coordinated way. Formation control is one of the most challenging goals for the coordination control of swarm robots. In this paper, a behavior-based control design approach is proposed for two kinds of important formation control problems: efficient initial formation and formation control while avoiding obstacles. In this approach, a classification-based searching method for generating large-scale robot formation is presented to reduce the computational complexity and speed up the initial formation process for any desired formation. The behavior-based method is applied for the formation control of swarm robot systems while navigating in an unknown environment with obstacles. Several groups of experimental results demonstrate the success of the proposed approach. These methods have potential applications for various swarm robot systems in both the simulation and the practical environments.

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