Abstract
Flocking is a social animals’ common behaviour observed in nature. It has a great potential for real-world applications such as exploration in agri-robotics using low-cost robotic solutions. In this paper, an extended model of a self-organised flocking mechanism using heterogeneous swarm system is proposed. The proposed model for swarm robotic systems is a combination of a collective motion mechanism with obstacle avoidance functions, which ensures a collision-free flocking trajectory for the followers. An optimal control model for the leader is also developed to steer the swarm to a desired goal location. Compared to the conventional methods, by using the proposed model, the swarm network has less requirement for power and storage. The feasibility of the proposed self-organised flocking algorithm is validated by realistic robotic simulation software.
Highlights
Flocking, a collective motion of individuals with a limited communication ability, is a social animals’ common behaviour observed in nature [1]
There are many types of collective motions in nature mainly found in living organisms and social animals, such as shoals of fish [2], flocks of birds [3] and swarms of wildebeest [4]
Inspired by the collective behaviours, swarm robotics has been emerged as a research topic that provides collective strategies for a large number of simple robots in order to achieve fascinating collective behaviours [6, 7]
Summary
A collective motion of individuals with a limited communication ability, is a social animals’ common behaviour observed in nature [1]. Inspired by the collective behaviours, swarm robotics has been emerged as a research topic that provides collective strategies for a large number of simple robots in order to achieve fascinating collective behaviours [6, 7]. The performed study by Thrun et al [19] proposed a cluster analysis that used the projection method based on the topographic map. These strategies can be divided, on the basis of its framework, into two main categories: homogeneous and heterogeneous [20]. A heterogeneous model is used in this research study to achieve centralised decision making for a swarm of robots
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