Controlling a triangular flexible formation of autonomous agents

In formation control, triangular formations consisting of three autonomous agents serve as a class of benchmarks that can be used to test and compare the performances of different controllers. We present an algorithm that combines the advantages of both position- and distance-based gradient descent control laws. For example, only two pairs of neighboring agents need to be controlled, agents can work in their own local frame of coordinates and the orientation of the formation with respect to a global frame of coordinates is not prescribed. We first present a novel technique based on adding artificial biases to neighboring agents' range sensors such that their eventual positions correspond to a collinear configuration. Right after, a small modification in the bias terms by introducing a prescribed rotation matrix will allow the control of the bearing of the neighboring agents.Comment: 7 pages, accepted in the 20th World Congress of the International Federation of Automatic Control (IFAC

Formation Pattern Based on Modified Cell Decomposition Algorithm

The purpose of this paper is to present the shortest path algorithm for Quadrotor to make a formation quickly and avoid obstacles in an unknown area. There are three algorithms proposed in this paper namely fuzzy, cell decomposition, and potential field algorithms. Cell decomposition algorithm is an algorithm derived from graph theory used to create maps of robot formations. Fuzzy algorithm is an artificial intelligence control algorithm used for robot navigation. The merger of these two algorithms are not able to form an optimum formation because some Quadrotors which have been hovering should wait for the other Quadrotors which are unable to find the shortest distance to reach the formation quickly. The problem is that the longer time the multi Quadrotors take to make a formation, the more energy they use. It can be overcome by adding potential field algorithm. The algorithm is used to give values of weight to the path planning taken by the Quadrotors. The proposed algorithms have shown that multi Quadrotors can quickly make a formation because they are able to avoid various obstacles and find the shortest path so that the time required to get to the goal position is fast