In this paper, we present an approach to transfer human expertise for learning off-road navigation behavior to an autonomous mobile robot. The methodology uses the concept of humanized intelligence to combine principal component analysis and neural network learning to embed human driving expertise onto mobile robots. The algorithms are tested in the field using a commercial Pioneer 2AT robot to demonstrate autonomous traversal over rough natural terrain.

Presents a fuzzy terrain-based path planning method for planetary rovers operating on rough natural terrain. The focus of this approach is on planning an optimally safe path of minimum traversal cost, which is calculated from linguistic descriptors of terrain traversability. The method incorporates the traversability map, a fuzzy map representation of traversal difficulty of the terrain, into the path planning logic. The search methodology uses a traversal cost function that is derived directly from this traversability map. The path planning method is developed in detail and experimental results are presented.

We present an approach to the design of a comprehensive set of behaviours for locally-controlled robots which must engage in inter-group competition. Such a "comprehensive" set of behaviours includes the ability to make progress towards team goals and obstruct progress of opponents by interacting constructively with teammates, and safely with all environmental objects. Our design principles guide us to maintain complexity of desired behaviour while minimizing complexity of control and requirements of accurate sensing, precise action, and communication. The resulting systems are small, simple, robust, and easily portable between platforms. These methods are discussed in the context of the design of the "Spirit of Bolivia", a soccer team of five autonomous robots which was undefeated in the RoboCup '97 championship, and which is currently training for RoboCup '98.