Cooperative Filters and Control for Cooperative Exploration

• Published in: IEEE transactions on automatic control Vol. 55; no. 3; pp. 650 - 663
• Main Authors: Fumin Zhang, Leonard, N.E.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptative systems; Adaptive Kalman filtering; Applied sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control systems; Control theory. Systems; cooperative control; cooperative filtering; Error correction; Estimates; Exact sciences and technology; Exploration; Filters; Large-scale systems; Law; Missions; mobile sensing networks; Modelling and identification; Motion control; Motion estimation; Multi-stage noise shaping; Optimal control; Platforms; Robotics; Robots; Sea measurements; Shape control; Simulation; Software; Strategy; Studies; Working environment noise; Error estimation; Kalman filter; Control program; Underwater; Adaptive filtering; Adaptive control; Optimization; Optimal design; Optimal strategy; cooperative control; Cooperation; System identification; Adaptive Kalman filtering; Mobile radiocommunication; Geometrical shape; mobile sensing networks; Multisensor; Cellular radio; Communication network; Multiagent system; Optimal control; cooperative filtering; Distributed control; Wireless network; Climbing behavior; Concurrent engineering; Asymptotic approximation; Large scale; Sensor array; Motion control; Formation; Mobile computing
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2009.2039240

Autonomous mobile sensor networks are employed to measure large-scale environmental fields. Yet an optimal strategy for mission design addressing both the cooperative motion control and the cooperative sensing is still an open problem. We develop strategies for multiple sensor platforms to explore a noisy scalar field in the plane. Our method consists of three parts. First, we design provably convergent cooperative Kalman filters that apply to general cooperative exploration missions. Second, we present a novel method to determine the shape of the platform formation to minimize error in the estimates and design a cooperative formation control law to asymptotically achieve the optimal formation shape. Third, we use the cooperative filter estimates in a provably convergent motion control law that drives the center of the platform formation to move along level curves of the field. This control law can be replaced by control laws enabling other cooperative exploration motion, such as gradient climbing, without changing the cooperative filters and the cooperative formation control laws. Performance is demonstrated on simulated underwater platforms in simulated ocean fields.