This work addresses the problem of radially segregating heterogeneous robotic swarms. Such swarms are those composed of different groups of robots. Unlike other works on segregation in the literature, we propose a controller for Dubins-like robots, motivated by autonomous aerial, wheeled, and underwater vehicles. Our controller can drive the robots individually to converge to circles that are shared only by robots of the same group. We present a heuristic and a collision avoidance scheme in which the information required is locally acquired. We present several simulations widely varying the number of robots per group and the number of groups in which segregation is always reached and collisions between robots are always avoided.

In this work, we present an Integrated Guidance and Controller (IGC) scheme to drive quadcopters in path-following tasks with obstacle avoidance and constant uncertainty rejection. This scheme is based on the combination of a time-varying artificial vector field and Backstepping with integral action control. The vector field switches between two behaviors: (i) path-following; and (ii) obstacle circumnavigation to allow collision avoidance. This vector field is then integrated into a nonlinear controller designed via Backstepping with Integral Action to deal with the quadcopter vehicle dynamics and reject constant uncertainties. The considered vehicle model is based on quaternion algebra. The control inputs are considered to be the total thrust and torques. Stability is proved by using Lyapunov's Theory and Matrosov's Theorem.

The present work focuses on the development of an efficient path controller to guide a fixed-wing UAV (Unmanned Aerial Vehicle) to follow a closed curve and avoid unknown dynamic obstacles. Our strategy is composed of two layers: a top level layer responsible for guidance and a lower level layer responsible for tracking the references given by the top level. To solve the guidance problem, we propose a vector field strategy that switches between two forms: a vector field to converge and circulate the target curve and a vector field to avoid obstacles by circulating the closest one. To make the fixed-wing UAV follow the velocity provided by the guidance vector field we consider a Model Predictive Control scheme. The feedback linearization allows efficient computation of control commands as a linear MPC controller can be employed. Our results are validated in simulations that take into account the 6DOF (Degrees of Freedom) model with constraints of the aircraft, wind disturbance and uncertainties on the measurements.

This article presents a path tracking control strategy for a quadcopter to follow a time varying curve. The control is based on artificial vector fields. The construction of the field is based on a well known technique in the literature. Next, control laws are developed to impose the behavior of the vector field to a second order integrator model. Finally, control laws are developed to impose the dynamics of the controlled second order integrator to a quadcopter model, which assumes the thrust and the angular rates as input commands. Asymptotic convergence of the whole system is proved by showing that the individual systems in cascade connection are input-to-state stable. We also analyze the influence of norm-bounded disturbances in the control inputs to evaluate the robustness of the controller. We show that bounded disturbances originate limited deviations from the target curve. Simulations and a real robot experiment exemplify and validate the developed theory.