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—We present an on-board robotic module which can determine relative positions among miniature robots. The module uses high-frequency modulated infrared emissions to enable nearby robots to determine the range, bearing, and message of the sender with a rapid update rate. A CSMA protocol is employed for scalable operation. We describe a technique for(More)
Using graph theory, this paper investigates how a group of robots, endowed with local positioning (range and bearing from other robots), can be engaged in a leader-following mission whilst keeping a predefined configuration. The possibility to locally change the behaviors of the follower team to accomodate both tasks is explored. In particular, a(More)
In this paper, a consensus-based control strategy is presented to gather formation for a group of differential-wheeled robots. The formation shape and the avoidance of collisions between robots are obtained by exploiting the properties of weighted graphs. Since mobile robots are supposed to move in unknown environments, the presented approach to multi-robot(More)
Using graph theory, this paper investigates how a group of vehicles, endowed with local positioning capabilities (range and bearing to other vehicles), can keep a predefined formation. We propose a longitudinal and lateral controller that stabilizes a system of several vehicles as well as a collision avoidance mechanism. The stability of our approach is(More)
— Formation building and keeping among vehicles has been studied for many years, since 1987 with Reynolds' rules [1]. This paper presents a control algorithm, based on recent work in graph theory, able to reconfigure static formations of non-holonomic vehicles endowed solely with local positioning capabilities. The convergence of our approach is(More)
In this paper, a new Behavioral-based Particle Swarm Optimization algorithm is proposed in order to solve the inverse kinematics problem for a manipulator operating in an environment cluttered with obstacles. The introduced variant of the Particle Swarm Optimization relies on the idea of dividing the population of the particles in subgroups, each of which(More)
When optimization algorithms are applied to non convex functions, they are generally affected by the problem of local minima, i.e. optimal solutions that do not correspond to the global minimum of the cost function. Particle Swarm Optimization algorithms are no exception. In order to overcome this problem and to speed up the convergence of the algorithm, in(More)