Stochastic path optimization for robotic bees using cloud computing

We study the problem of dynamic routing of robotic bees towards the hive, with the intended purpose of minimizing the time it takes for all the bees to arrive at the destination. Due to uncertainty in position measurements, the stochastic problem cannot ensure collision-free paths. We study the effects that the algorithm parameters have in reducing the computational complexity and expected number of collisions. The dynamic path allocation assumes signals are received every ε units of time. We provide a weak convergence proof that the stochastic dynamic allocation converges to the optimal deterministic path when ε → 0. Next we explore via experimentation how the various algorithm parameters affect the overall performance. A k-nearest neighbors strategy is implemented to lessen the need for small step size ε and safety parameter. Δ. In this manner we achieve a faster completion time, reduce collisions and computational complexity.