  Swarm robotic search is concerned with searching targets in unknown
environments (e.g., for search and rescue or hazard localization), using a
large number of collaborating simple mobile robots. In such applications,
decentralized swarm systems are touted for their task/coverage scalability,
time efficiency, and fault tolerance. To guide the behavior of such swarm
systems, two broad classes of approaches are available, namely nature-inspired
swarm heuristics and multi-robotic search methods. However, simultaneously
offering computationally-efficient scalability and fundamental insights into
the exhibited behavior (instead of a black-box behavior model), remains
challenging under either of these two class of approaches. In this paper, we
develop an important extension of the batch Bayesian search method for
application to embodied swarm systems, searching in a physical 2D space. Key
contributions lie in: 1) designing an acquisition function that not only
balances exploration and exploitation across the swarm, but also allows
modeling knowledge extraction over trajectories; and 2) developing its
distributed implementation to allow asynchronous task inference and path
planning by the swarm robots. The resulting collective informative path
planning approach is tested on target search case studies of varying
complexity, where the target produces a spatially varying (measurable) signal.
Significantly superior performance, in terms of mission completion efficiency,
is observed compared to exhaustive search and random walk baselines, along with
favorable performance scalability with increasing swarm size.
