Distributed scaling control of rigid formations

Recently it has been reported that biased range-measurements among neighboring agents in the gradient distance-based formation control can lead to predictable collective motion. In this paper we take advantage of this effect and by introducing distributed parameters to the prescribed inter-distances we are able to manipulate the steady-state motion of the formation. This manipulation is in the form of inducing simultaneously the combination of constant translational and angular velocities and a controlled scaling of the rigid formation. While the computation of the distributed parameters for the translational and angular velocities is based on the well-known graph rigidity theory, the parameters responsible for the scaling are based on some recent findings in bearing rigidity theory. We carry out the stability analysis of the modified gradient system and simulations in order to validate the main result.Comment: 6 pages In proceedings 55th Conference on Decision and Control, year 201

Identification of Hessian matrix in distributed gradient-based multi-agent coordination control systems

Multi-agent coordination control usually involves a potential function that encodes information of a global control task, while the control input for individual agents is often designed by a gradient-based control law. The property of Hessian matrix associated with a potential function plays an important role in the stability analysis of equilibrium points in gradient-based coordination control systems. Therefore, the identification of Hessian matrix in gradient-based multi-agent coordination systems becomes a key step in multi-agent equilibrium analysis. However, very often the identification of Hessian matrix via the entry-wise calculation is a very tedious task and can easily introduce calculation errors. In this paper we present some general and fast approaches for the identification of Hessian matrix based on matrix differentials and calculus rules, which can easily derive a compact form of Hessian matrix for multi-agent coordination systems. We also present several examples on Hessian identification for certain typical potential functions involving edge-tension distance functions and triangular-area functions, and illustrate their applications in the context of distributed coordination and formation control

Cooperative distributed pick and place algorithm for mobile manipulators with camera feedback

The main goal of this research is to perform a pick and place task done by several mobile robots equipped with a robotic arm by using a distributed algorithm and a formation control law for keeping the shape. Nowadays, the industry sometimes needs the cooperation of different robots to achieve what cannot be reached by a single robot. Sometimes, the fact of using only a single robot can be either really expensive or not powerful enough and it is worth to implement a system with several agents. The studied case will be with four agents and it will be tested experimentally with the mobile nexus robots which are in the DTPA lab. The fact of being a task performed by several agents means that formation control theory will be taken into account, specifically the formation control Law designed by Garcia de Marina, Jayawardhana, and Cao [1]. Several studies and tests related to formation control have been performed in the PhD Nexus Group. Furthermore, some research about pick and place task has also been studied but only for a single robot. Because of this reason, the aim of this research is to extrapolate the results of the pick and place task done for one robot to a formation of several agents by using the formation control law previously specified. Moreover once the algorithm is tested in a four agent formation is relatively easy to change the number of agents by simply modifying some parameters. Challenges such as recognition of objects (Markers), tracking them (PI control) and keeping the formation of the agents (formation control theory) will be achieved by using the suitable sensors. For instance, a camera mounted on the robotic arm will be used for the recognition of objects, while a RPLidar Laser Scanner will be used for measuring the distances between robots and ensure that they keep the formationOutgoin

Distributed formation control for autonomous robots

This thesis addresses several theoretical and practical problems related to formation-control of autonomous robots. Formation-control aims to simultaneously accomplish the tasks of forming a desired shape by the robots and controlling their coordinated collective motion. This kind of robot performance is a cornerstone in the emerging field of swarm robotics, in particular with applications in precision agriculture, coverage of sport/art events, communication networks, area surveillance or vehicle platooning for energy efficiency and many others. One of the most important outcomes of this thesis is that the provided algorithms are completely distributed. This means that there is no central unit commanding the robots, but they have their own intelligence which allows them to make their own decisions based only on the local information. A distributed scheme entails a striking feature about the scalability and maintenance of a team of robots. Moreover, we also address the scenario of having wrongly calibrated sensors, which has a profound impact in the performance of the robots. The provided algorithms make the robots robust against such a practical and very common problem in real applications