  We consider a pursuit-evasion problem with a heterogeneous team of multiple
pursuers and multiple evaders. Although both the pursuers (robots) and the
evaders are aware of each others' control and assignment strategies, they do
not have exact information about the other type of agents' location or action.
Using only noisy on-board sensors the pursuers (or evaders) make probabilistic
estimation of positions of the evaders (or pursuers). Each type of agent use
Markov localization to update the probability distribution of the other type. A
search-based control strategy is developed for the pursuers that intrinsically
takes the probability distribution of the evaders into account. Pursuers are
assigned using an assignment algorithm that takes redundancy (i.e., an excess
in the number of pursuers than the number of evaders) into account, such that
the total or maximum estimated time to capture the evaders is minimized. In
this respect we assume the pursuers to have clear advantage over the evaders.
However, the objective of this work is to use assignment strategies that
minimize the capture time. This assignment strategy is based on a modified
Hungarian algorithm as well as a novel algorithm for determining assignment of
redundant pursuers. The evaders, in order to effectively avoid the pursuers,
predict the assignment based on their probabilistic knowledge of the pursuers
and use a control strategy to actively move away from those pursues. Our
experimental evaluation shows that the redundant assignment algorithm performs
better than an alternative nearest-neighbor based assignment algorithm.
