Inspired by nature, swarm robotics aims to increase system robustness while utilizing simple agents. In this work, we present a novel approach to achieve decentralized coordination of forces during collective manipulation tasks resulting in a highly scalable, versatile, and robust solution. In this approach, each robot involved in the collective object manipulation task relies on the behavior of a cooperative ``virtual teammate' in a fully decentralized architecture, regardless of the size and configuration of the real team. By regulating their actions with their corresponding virtual counterparts, robots achieve continuous pose control of the manipulated object, while eliminating the need for inter-agent communication or a leader-follower architecture. To experimentally study the scalability, versatility, and robustness of the proposed collective object manipulation algorithm, a new swarm agent, Δρ is introduced which is able to apply linear forces in any planar direction. Efficiency and effectiveness of the proposed decentralized algorithm are investigated by quantitative performance metrics of settling time, steady-state error, path efficiency, and object velocity profiles in comparison with a force-optimal centralized version that requires complete information. Employing impedance control during manipulation of an object provides a mean to control its dynamic interactions with the environment. The proposed decentralized algorithm is extended to achieve a desired multi-dimensional impedance behavior of the object during a collective manipulation without inter-agent communication. The proposed algorithm extension is built upon the concept of ``virtual coordination' which demands every agent to locally coordinate with one virtual teammate. Since the real population of the team is unknown to the agents, the resultant force applied to the manipulated object would be directly scaled with the team population. Although this scaling effect proves useful during position control of the object, it leads to a deviation from the desired dynamic response when employed in an impedance control scheme. To minimize such deviations, a gradient descent algorithm is implemented to determine a scaling parameter defined on the control action. The simulation results of a multi-robot system with different populations and formations verify the effectiveness of the proposed method in both generating the desired impedance response and estimating the population of the group. Eventually, as two case studies, the introduced algorithm is used in robotic collective manipulation and human- assistance scenarios. Simulation and experimental results indicate that the proposed decentralized communication- free algorithm successfully performs collective manipulation in all tested scenarios, and matches the performance of the centralized controller for increasing number of agents, demonstrating its utility in communication- limited systems, remote environments, and access-limited objects.

In nature animal species often exist in groups. We talk of insect swarms, flocks of birds, packs of lions, herds of wildebeest etc. These are characterised by individuals interacting by following their own rules, privy only to local information. Robotic swarms or simulations can be used explore such interactions. Mathematical formulations can be constructed that encode similar ideas and allow us to explore the emergent group behaviours. Some behaviours show characteristics reminiscent of the phenomena of criticality. A bird flock may show near instantaneous collective shifts in direction: velocity changes that appear to correlated over distances much larger individual separations. Here we examine swarm systems inspired by flocks of birds and the role played by criticality. The first system, Particle Swarm Optimisation (PSO), is shown to behave optimally when operating close to criticality. The presence of a critical point in the algorithm’s operation is shown to derive from the swarm’s properties as a random dynamical system. Empirical results demonstrate that the optimality lies on or near this point. A modified PSO algorithm is presented which uses measures of the swarm’s diversity as a feedback signal to adjust the behaviour of the swarm. This achieves a statistically balanced mixture of exploration and exploitation behaviours in the resultant swarm. The problems of stagnation and parameter tuning often encountered in PSO are automatically avoided. The second system, Swarm Chemistry, consists of heterogeneous particles combined with kinetic update rules. It is known that, depending upon the parametric configuration, numerous structures visually reminiscent of biological forms are found in this system. The parameter set discovered here results in a cell-division-like behaviour (in the sense of prokaryotic fission). Extensions to the swarm system produces a swarm that shows repeated cell division. As such, this model demonstrates a behaviour of interest to theories regarding the origin of life.