Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of
decision-making strategies for domain search and object classification using multiple
autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a
first discussion of the problem of effective resource allocation using MAV with sensing
limitations i.e. for search and classification missions over large-scale domains or when
there are far more objects to be found and classified than there are autonomous vehicles
available. Under such scenarios search and classification compete for limited sensing
resources. This is because search requires vehicle mobility while classification restricts the
vehicles to the vicinity of any objects found. The authors develop decision-making strategies
to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed
management scheme. Deterministic Lyapunov-based probabilistic Bayesian-based and risk-based
decision-making strategies and sensor-management schemes are created in sequence. Modeling and
analysis include rigorous mathematical proofs of the proposed theorems and the practical
consideration of limited sensing resources and observation costs. A survey of the
well-developed coverage control problem is also provided as a foundation of search algorithms
within the overall decision-making strategies. Applications in both underwater sampling and
space-situational awareness are investigated in detail. The control strategies proposed in each
chapter are followed by illustrative simulation results and analysis. Academic researchers and
graduate students from aerospace robotics mechanical or electrical engineering backgrounds
interested in multi-agent coordination and control in detection and estimation or in Bayes
filtration will find this text of interest.
