This paper builds on prior research into the application of particle swarm optimisation to autonomous vehicle control in search roles. It examines the use of naturally inspired search strategies to enhance the performance of groups of sensor-based vehicles in applications where there is no knowledge a priori regarding target presence, location, distribution or behaviour (movement). This paper first briefly reviews existing ethological research into search strategies in the natural world, identifying three types of random walk, two multi-phase strategies and two species-specific strategies for further investigation. Experiments are then performed within a simulation environment to compare the performance of naturally inspired strategies with deterministic patterns and random movement, when searching for both static and dynamic targets. Results indicate that performance improvements can be realised, provided that critical relationships within the application domain broadly match those existing in the underlying natural metaphor.
