Barry Werger

Global and regional path planners for integrated planning and navigation

This paper presents a hierarchical strategy for field mobile robots that incorporates path planning at different ranges. At the top layer is a global path planner that utilizes gross terrain characteristics, such as hills and valleys, to determine globally safe paths through the rough terrain. This information is then passed via waypoints to a regional layer that plans appropriate navigation paths using regional terrain characteristics. The global and regional path planners share the same map information, but at different ranges. The motion recommendations from the regional layer are then combined with those of the reactive navigation layer to provide reactive control for the mobile robot. Details of the global and regional path planners are discussed, and simulation and experimental results are presented.

Fuzzy terrain-based path planning for planetary rovers

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.

Integrating terrain maps into a reactive navigation strategy

This paper presents a new method for integrating terrain maps into a reactive navigation strategy of field mobile robots operating on rough terrain. The method incorporates the Regional Traversability Map, a fuzzy map representation of traversal difficulty of the regional terrain, into the navigation logic. A map-based regional navigation behavior provides speed and direction recommendations based on the current status of the robot. In addition, recommendations from two sensor-based reactive behaviors, local avoid-obstacle and regional traverse-terrain, are fused with the map-based regional behavior to construct a comprehensive navigation system. The algorithms are tested both in graphical simulations and in the field using a commercial Pioneer 2AT robot to demonstrate traversal over rough natural terrain.

Theory and experiments in SmartNav rover navigation

This paper describes theoretical and experimental results using the SmartNav rule-free fuzzy rover navigation system. SmartNav divides the terrain perceived by the rover into a number of circular sectors, and evaluates each sector using goal and safety preference factors to differentiate between preferred and unpreferred terrain sectors. The goal-preference factor is used to make sector evaluation based on the sector orientation relative to the designated goal position. The safety-preference factors are used to make sector evaluations on the basis of the sector local and regional terrain hazards. Three methods are developed to blend the three sector evaluations in order to find the effective preference factor for each sector. Two sector selection methods are then described in which the sector preference factors are used to find the heading command for the rover. The rover speed command is also computed based on the goal distance and safety-preference factor of the chosen sector. The above navigation steps are continuously repeated throughout the rover motion. Experimental results are presented to demonstrate the navigational capabilities of SmartNav using a commercial Pioneer 2AT rover traversing a simulated Martian terrain at the JPL Mini Mars Yard.

A human-robot mentor-protege relationship to learn off-road navigation behavior

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.