Recent research has seen the advancement of drone depot models as a promising way to allocate drones for large-scale task completion. Applications of these drone depot models include data collection, environmental monitoring, package delivery, and more. This paper focuses on sharing agents between static depots for task allocation based on expected demand. We model the problem as a Binary Nonlinear Program, then derive an iterative neighborhood search based on solving a series of Binary Linear Programs to drive towards the optimal configuration of agents for each depot. We show that our method is more tractable than a Branch and Bound approach for this model as problem complexity grows. We also show through simulations that with near optimal allocation between local depots, the overall system performance will outperform greedy and non-sharing approaches.

This paper presents a method to reduce computations for stochastic dynamic games with game-theoretic belief space planning through partially propagating beliefs. Complex interactions in scenarios such as surveillance, herding, and racing can be modeled using game-theoretic frameworks in the belief space. Stochastic dynamic games can be solved to a local Nash Equilibrium using a game-theoretic belief space variant of an iterative Linear Quadratic Gaussian (iLQG). However, the scalability of this method suffers due to the large dimensionality of beliefs which the iLQG must propagate. We examine the utility of partial belief space propagation, which allows polynomial runtime to decrease. We validate our findings through simulations and hardware implementation.