This paper presents control and coordination algorithms for groups of autonomous vehicles performing distributed sensing tasks. The focus is on developing gradient descent algorithms for a class of utility functions that encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct. The paper reviews locational optimization problems and their solutions, specifically centroidal Voronoi partitions. It proposes continuous-time and discrete-time versions of the Lloyd algorithm, which are gradient descent flows. The algorithms are designed to be implemented in multi-vehicle networks with limited sensing and communication capabilities. The paper also discusses two asynchronous distributed implementations of the Lloyd algorithm and presents numerical simulations to illustrate the performance of the algorithms. Additionally, it explores extensions to vehicles with passive dynamics and local motion planners, and provides examples of density functions that lead the network to predetermined geometric patterns.This paper presents control and coordination algorithms for groups of autonomous vehicles performing distributed sensing tasks. The focus is on developing gradient descent algorithms for a class of utility functions that encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct. The paper reviews locational optimization problems and their solutions, specifically centroidal Voronoi partitions. It proposes continuous-time and discrete-time versions of the Lloyd algorithm, which are gradient descent flows. The algorithms are designed to be implemented in multi-vehicle networks with limited sensing and communication capabilities. The paper also discusses two asynchronous distributed implementations of the Lloyd algorithm and presents numerical simulations to illustrate the performance of the algorithms. Additionally, it explores extensions to vehicles with passive dynamics and local motion planners, and provides examples of density functions that lead the network to predetermined geometric patterns.