Abstract—We present a stable control strategy for groups of vehicles to move and reconfigure cooperatively in response to a sensed, distributed environment. Each vehicle in the group serves as a mobile sensor and the vehicle network as a mobile and reconfigurable sensor array. Our control strategy decouples, in part, the cooperative management of the network formation from the network maneuvers. The underlying coordination framework uses virtual bodies and artificial potentials. We focus on gradient climbing missions in which the mobile sensor network seeks out local maxima or minima in the environmental field. The network can adapt its configuration in response to the sensed environment in order to optimize its gradient climb. Index Terms—Adaptive systems, cooperative control, gradient methods, mobile robots, multiagent systems, sensor networks. I.

Fish in a school efficiently find the densest source of food by individually responding not only to local environmental stimuli but also to the behavior of nearest neighbors. It is of great interest to enable a network of autonomous vehicles to function similarly as an intelligent sensor array capable of climbing or descending gradients of some spatially distributed signal. We formulate and study a coordinated control strategy for a group of autonomous vehicles to descend or climb an environmental gradient using measurements of the environment together with relative position measurements of nearest neighbors. Each vehicle is driven by an estimate of the local environmental gradient together with control forces, derived from artificial potentials, that maintain uniformity in group geometry.

We consider the problem of seeking the maximum of a scalar signal using a swarm of autonomous vehicles equipped with sensors that can take point measurements of the signal. Vehicles are not able to measure their current position or to communicate with each other. Our approach induces the vehicles to perform a biased random walk inspired by bacterial chemotaxis and controlled by a stochastic hybrid automaton. With such a controller, it is shown that the positions of the vehicles evolve towards a probability density that is a specified function of the spatial profile of the measured signal, granting higher vehicle densities near the signal maxima.

This dissertation concerns the development of a feedback control framework for coordinat-ing multiple, sensor-equipped, autonomous vehicles into mobile sensing arrays to perform adaptive sampling of observed fields. The use of feedback is central; it maintains the array, i.e. regulates formation position, orientation, and shape, and directs the array to perform its sampling mission in response to measurements taken by each vehicle. Specifically, we address how to perform autonomous gradient tracking and feature detection in an unknown field such as temperature or salinity in the ocean. Artificial potentials and virtual bodies are used to coordinate the autonomous vehicles, modelled as point masses (with unit mass). The virtual bodies consist of linked, moving reference points called virtual leaders. Artificial potentials couple the dynamics of the vehicles and the virtual bodies. The dynamics of the virtual body are then prescribed allowing the virtual body, and thus the vehicle group, to perform maneuvers that include translation, rotation and contraction/expansion, while ensuring that the formation error remains bounded. This methodology is called the Virtual Body and Artificial Potential

A multi-vehicle search strategy based on the simplex algorithm is proposed. The strategy is decomposed into a hierarchical scheme with three layers.

A layered control architecture for executing multi-vehicle team coordination algorithms is presented along with the specifications for team behavior. The control architecture consists of three layers: team control, vehicle supervision and maneuver control. It is shown that the controller implementation is consistent with the system specification on the desired team behavior. Computer simulations with accurate models of autonomous underwater vehicles illustrate the overall approach in the coordinated search for the minimum of a scalar field. The coordinated search is based on the simplex optimization algorithm.