In this paper an omnidirectional mobile robot that possesses high mobility in rough terrain is presented. The omnidirectional robot has four active split offset caster (ASOC) modules, enabling the robot to move in any planar direction. It also possesses passive suspension articulation, allowing the robot to conform to uneven terrain. The agility of the robot is experimentally evaluated in various configurations. In addition, an odometry method that mitigates position estimation error due to wheel slippage is proposed. A key aspect of the proposed method is to utilize sensory data of wheel velocity, and turning rate around each ASOC pivot shaft, along with kinematic constraints of the robot configuration. Experimental odometry tests with different maneuvers in rough terrain are presented that confirm the utility of the proposed method.

A local positioning system (LPS) for tracking mobile robots using a newly developed ultra-wideband (UWB) ranging radio technology is presented in this paper. However, measured ranges from these radios often have uncertain biases and large sporadic errors due to multipath and attenuation effect. A fuzzy neighborhood tracking filtering technique was developed to deal with the range outlier problems. A progressive update trilateration filter technique is introduced. The paper then describes a UWB LPS based on triangulation or trilateration of the ranges, which is fused with other vehicle kinematics/dynamics sensors including a compass, rate gyros and wheel speed sensors. The UWB LPS was applied to navigation and guidance of an experimental autonomous mobile robot. Simulation theoretical and real-time experimental results were validated against the true results of the experiment recorded by video.

This paper presents a tactical path planning algorithm for following ridges or valleys across a 3D terrain. The intent is to generate a path that enables an unmanned vehicle to surveil with maximum observability by traversing the ridges of a terrain or to operate with maximum covertness by navigating the valleys. The input to the algorithm is a 3D triangle mesh model for the terrain of interest. This mesh may be non-uniform and non-regular. Thus, the algorithm leverages research from computer graphics and computer vision to identify ridge-valley features on the terrain. These features serve as "obstacles" for an artificial potential field algorithm. The valleys are obstacles for a surveillance path, or the ridges are obstacles for a covert path. We incorporate geodesic-rather than Euclidean-distances into the potential field formulation to extend path planning to 3D surfaces. We present the theory of our proposed algorithm and provide experimental results

This paper describes a behavior-based method of controlling an autonomous skid-steer robot operating in an unknown environment. We introduce a new modular, neural-fuzzy system called a threshold fuzzy system (TFS). Supervised training, using error backpropagation, is used to find optimal parameters of the TFS. In this paper, a TFS controller is developed for a skid-steer autonomous vehicle system (AVS). Several hundred simulations are conducted and results for the AVS are compared (favorably) with a traditional neural network approach.