Formations of Robotic Swarm: An Artificial Force Based Approach

Abstract

Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm) into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding intermember collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

“…155,225 Furthermore, recently proposed control methods can achieve collision avoidance for Lagrangian systems with bounded inputs. 225 Some other methods provide good practical results, though without focusing on mathematical analysis of collision avoidance, see, e.g., [45,72,73]. Many of these methods can be extended to static obstacles, and these combined systems are achieved by the same avoidance functions as the single vehicle case, see, e.g., [155].…”

Void Reduction in Self-Healing Swarms

“…Swarm-based algorithms use a number of agents which behave according to local rules (locality often being defined in terms of spatial proximity), but whichcollectively -are capable of synergistically cooperative behaviour. Problems to which such methods have been applied include path finding (Hou et al, 2009), distribution across a space (Ekanayake and Pathirana, 2010;Passino, 2002, 2004a), or foraging as a colony (Gurfil and Kivelevitch, 2007;Hereford, 2011). In order to model inter-agent interactions, many algorithms use field effects, which capture attractive and repulsive forces between agents (Andreou et al, 2009;Barnes et al, 2006a,b;Bennet and McInnes, 2009;Gazi and Passino, 2002, 2004b, 2005, 2011Mohan and Ponnambalam, 2009).…”

Multi-agent formation control based on artificial force with exponential form

“…Ref. [8] presents a distributed potential control algorithm, which can guide a multi-agent system into a predefined two-dimensional boundary and spread them inside the shape uniformly. This algorithm has good effects on swarm control but is difficult to ensure several agents within the region uniformly.…”

“…However, obstacle avoidance is not included which is essential in practical applications. Inspired by [8] [9], Ref. [10] proposes a model using bounded artificial force and demonstrates its stability and convergence.…”