Abstract
Applying swarm intelligence to actual swarm robotic systems is a challenging task especially with adequately consideration of corresponding practical constraints. Under the restrictions of the field-of-view limited relative positioning, local sensing and communication, kinematic limitations as well as anti-collision issues, this paper presents a constrained particle swarm optimization (PSO) based collaborative searching method for robotic swarms. Besides, the proposed method follows the concept of evolution speed and a modified aggregation degree to determine the adaptive weights in the robotic PSO model. The modified aggregation degree is associated with the number of members in one's field-of-view. Unlike the traditional position update method, the proposed method updates the forward speed and angular velocity of the robot using the non-holonomic model to realize the motion control of each robot. The simulation results show that the proposed method has the potential for the practical implementation of collaborative searching tasks for robotic swarms in different types of environments.
Highlights
Swarm robotics originates from bionics of natural social creatures such as birds, fishes, or insects [1]
What presented in this paper is an extended particle swarm optimization (PSO) based collaborative searching scheme for robotic swarms which take account of the field of view limited relative positioning, local sensing, and restricted communication
There are some similarities between the PSO algorithm and the collaborative searching of robotic swarms
Summary
Swarm robotics originates from bionics of natural social creatures such as birds, fishes, or insects [1]. What presented in this paper is an extended PSO based collaborative searching scheme for robotic swarms which take account of the field of view limited relative positioning, local sensing, and restricted communication. Due to the nonlinearity and complexity of optimation problems, the linear decreasing strategy cannot match the actual optimization process Another adaptive weight acquisition method is based on a truth that the change of inertia weight is affected by the swarm’s evolutionary situation, which is determined by the combination of the ‘‘evolution speed’’ and ‘‘aggregation degree’’ of the particles [9]–[11]. If the aggregation degree is high, the group is likely to fall into the local optimal, and it is necessary to increase w to increase the search space to keep the diversity of the swarm and improve the global optimization ability
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