Edward Fiorelli

Virtual leaders, artificial potentials and coordinated control of groups

We present a framework for coordinated and distributed control of multiple autonomous vehicles using artificial potentials and virtual leaders. Artificial potentials define interaction control forces between neighboring vehicles and are designed to enforce a desired inter-vehicle spacing. A virtual leader is a moving reference point that influences vehicles in its neighborhood by means of additional artificial potentials. Virtual leaders can be used to manipulate group geometry and direct the motion of the group. The approach provides a construction for a Lyapunov function to prove closed-loop stability using the system kinetic energy and the artificial potential energy. Dissipative control terms are included to achieve asymptotic stability. The framework allows for a homogeneous group with no ordering of vehicles; this adds robustness of the group to a single vehicle failure.

Cooperative control of mobile sensor networks:Adaptive gradient climbing in a distributed environment

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.

Multi-AUV Control and Adaptive Sampling in Monterey Bay

Operations with multiple autonomous underwater vehicles (AUVs) have a variety of underwater applications. For example, a coordinated group of vehicles with environmental sensors can perform adaptive ocean sampling at the appropriate spatial and temporal scales. We describe a methodology for cooperative control of multiple vehicles based on virtual bodies and artificial potentials (VBAP). This methodology allows for adaptable formation control and can be used for missions such as gradient climbing and feature tracking in an uncertain environment. We discuss our implementation on a fleet of autonomous underwater gliders and present results from sea trials in Monterey Bay in August, 2003. These at-sea demonstrations were performed as part of the Autonomous Ocean Sampling Network (AOSN) II project

Multi-AUV control and adaptive sampling in Monterey Bay

Multi-AUV operations have much to offer a variety of underwater applications. With sensors to measure the environment and coordination that is appropriate to critical spatial and temporal scales, the group can perform important tasks such as adaptive ocean sampling. We describe a methodology for cooperative control of multiple vehicles based on virtual bodies and artificial potentials (VBAP). This methodology allows for adaptable formation control and can be used for missions such as gradient climbing and feature tracking in an uncertain environment. We discuss our implementation on a fleet of autonomous underwater gliders and present results from sea trials in Monterey Bay in August 2003. These at-sea demonstrations were performed as part of the Autonomous Ocean Sampling Network (AOSN) II project.

Underwater gliders: recent developments and future applications

Autonomous underwater vehicles, and in particular autonomous underwater gliders, represent a rapidly maturing technology with a large cost-saving potential over current ocean sampling technologies for sustained (month at a time) real-time measurements. We give an overview of the main building blocks of an underwater glider system for propulsion, control, communication and sensing. A typical glider operation, consisting of deployment, planning, monitoring and recovery are described using the 2003 AOSN-II field experiment in Monterey Bay, California. We briefly describe the recent developments at NRC-IOT, in particular, the development of a laboratory-scale glider for dynamics and control research and the concept of a regional ocean observation system using underwater gliders.

Exploring scalar fields using multiple sensor platforms: Tracking level curves

Autonomous mobile sensor networks are employed to measure large scale environmental scalar fields. Yet an optimal strategy for mission design addressing both the cooperative motion control and the collaborative sensing is still under investigation. We develop one strategy which uses four moving sensor platforms to explore a noisy scalar field defined in the plane; each platform can only take one measurement at a time. We derive a Kalman filter in conjunction with a nonlinear filter to produce estimates for the field value, the gradient and the Hessian along the averaged trajectories of the moving platforms. The shape of the platform formation is designed to minimize error in the estimates, and a cooperative control law is designed to asymptotically achieve the optimal formation. We develop a motion control law to allow the center of the platform formation to move along level curves of the averaged field. Convergence of the control laws are proved, and performance of both the filters and the control laws are demonstrated in simulated ocean fields.