We discuss the implementation of Spectral Multiscale Coverage (SMC) based multi-vehicle control and coordination for coverage and search missions by autonomous UAVs. The SMC algorithm gives rise to multiscale vehicle trajectories leading to efficient coverage of a given area and thereby making it useful for search algorithms that are robust to sensor errors and terrain uncertainties. We provide a functional summary of the SMC framework and address its practical implementation. The practical feasibility of the SMC approach is demonstrated via several coverage problems using high fidelity software-in-the-loop (SIL) simulations and experimental flight tests conducted using an electric helicopter.

Abstract-In this paper, we study multiple target detection using Bayesian learning. The main aim of the paper is to present a computationally efficient way to compute the belief map update exactly and efficiently using results from the theory of symmetric polynomials. In order to illustrate the idea, we consider a simple search scenario with multiple search agents and an unknown but fixed number of stationary targets in a given region that is divided into cells. To estimate the number of targets, a belief map for number of targets is also propagated. The belief map is updated using Bayes' theorem and an optimal reassignment of vehicles based on the values of the current belief map is adopted. Exact computation of the belief map update is combinatorial in nature and often an approximation is needed. We show that the Bayesian update can be exactly computed in an efficient manner using Newton's identities and the detection history in each cell.