In this paper, a possible solution to track a mobile underwater source in a closed environment with N Autonomous Underwater Vehicles (AUV) in a swarm formation is adressed. The source tracking algorithm is defined as successful when the range between the source and the swarm is sufficiently low during a given duration, short enough to perform a specified action (for example a source localization). A source is defined as an entity that releases a scalar information affected by transport and diffusion in the environment. We use a generic time-varying information f (pi(t)), where pi at time t is the m-dimensional position of a tracker i and function f (.) is a function that represents sensor information. In this paper, we propose an innovative tracking method inspired by the Particle Swarm Optimization (PSO) algorithm that we call the Local Charged Particle Swarm Optimization (LCPSO). The proposed algorithm is adapted to range-dependant communication that characterizes the underwater context and includes flocking parameters. Comparison of the LCPSO against state of the art methods demonstrate the interest of our approach in an underwater scenario.

We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the Particle Swarm Optimization (PSO) algorithm for function optimization. Four parameters drive LCPSO—the number of agents; the inertia weight; the attraction/repulsion weight; and the inter-agent distance. Using APF (Artificial Potential Field), we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target.

Estimating the distance traveled while navigating in a GPS-deprived environment is key for aerial robotic applications. For drones, this issue is often coupled with weight and computational power constraints, from which stems the importance of minimalistic equipment. In this study, we present a visual odometry strategy based solely on two optic flow magnitudes perceived by two optic flow sensors oriented at ±30 • on either side of a drone's vertical axis. As results, (i) we measured the local optic flow divergence and the local translational optic flow respectively as the subtraction and the sum of the two optic flow magnitudes perceived (ii) we validated experimentally the visual odometer on a hexarotor oscillating upand-down while following a 50m-long circular trajectory under three illuminance conditions (117lux, 814lux and 1518lux). The measured optic flow divergence was used to estimate the flight height by means of an Extended Kalman Filter. The estimated flight height scaled the measured translational optic flow, which was integrated to perform minimalistic visual odometry.