Abstract
This paper presents a new approach for cooperative search of a robot swarm. After modeling the robot, the mechanical Particle Swarm Optimization method is conducted based on physical robot properties. Benefiting from the effective localization and navigation by sensor data fusion, a mixed robot swarm which contains both simulated and real robots is then successfully used for searching a target cooperatively. With the promising results from experiments based on different scenarios, the feasibility, the interaction of real and simulated robots, the fault tolerance, and also the scalability of the proposed method are investigated.
Highlights
Nowadays robots are used more and more since they can do various tasks instead of humans
This paper presents a new approach for cooperative search of a robot swarm
Readers may notice from these applications that cooperative search is quite representative for the swarm robotics research since it covers most of the technical points
Summary
Nowadays robots are used more and more since they can do various tasks instead of humans. For cooperative search of robot swarms, key points are performing collaboration, showing fault tolerance, sharing information, distributing the work, and realizing scalability. These are the advantages of a mobile robot swarm in contrast to a single mobile robot. Many research groups use PSO and its variants for swarm mobile robots search; see, for example, [9,10,11] These publications have shown shortcomings for cooperative search. This study develops a systematic cooperative search method which contains sufficient search ability, scalability, and fault tolerance, considers robot physical properties, and qualifies for real-simulated robots interaction. Flexible connection board Figure 2: Robotino controller head and its components
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