Autonomous man-portable robots have the potential to provide a wide range of new capabilities for both military and civilian applications. Previous research in autonomy for small robots has focused on vision, LIDAR, and sonar sensors. While vision and LIDAR work well in clear weather, they are seriously impaired by rain, snow, fog, and smoke. Sonar can penetrate adverse weather, but has limited range outdoors, and suffers from specular reflections indoors. For the Daredevil Project, we have investigated the use of ultra-wideband (UWB) radar to provide obstacle detection capabilities for man-portable robots. Our research shows that UWB radar can effectively penetrate adverse weather, including dense fog, and detect obstacles that would be undetectable by vision or LIDAR under the same conditions. We have developed filtering algorithms that process the raw radar returns to eliminate reflections from ground clutter and make obstacles easier to detect. We have tested this system on an iRobot PackBot equipped with both UWB radar and LIDAR, and we have demonstrated how UWB radar can be used for obstacle detection in obscured environments.

The major themes of our research include the creation of mobile robot systems that are robust and adaptive in rapidly changing environments and the view of integration as a basic research issue. Where reasonable, we try to use the same representations to allow different components to work more readily together and to allow better and more natural integration of and communication between these components. In this paper, we describe our most recent work in integrating mobile robot exploration, localization, navigation, and planning through the use of a common representation, evidence grids.

Our research addresses how to integrate exploration and localization for mobile robots. A robot exploring and mapping an unknown environment needs to know its own location, but it may need a map in order to determine that location. In order to solve this problem, we have developed ARIEL, a mobile robot system that combines frontier based exploration with continuous localization. ARIEL explores by navigating to frontiers, regions on the boundary between unexplored space and space that is known to be open. ARIEL finds these regions in the occupancy grid map that it builds as it explores the world. ARIEL localizes by matching its recent perceptions with the information stored in the occupancy grid. We have implemented ARIEL on a real mobile robot and tested ARIEL in a real-world office environment. We present quantitative results that demonstrate that ARIEL can localize accurately while exploring, and thereby build accurate maps of its environment.

Dead reckoning provides a simple way to keep track of a mobile robot's location. However, due to slippage between the robot's wheels and the underlying surface, this position estimate accumulates errors over time. In this paper, we introduce a method for correcting dead reckoning errors by matching evidence grids constructed at different times. A hill-climbing algorithm is used to search the space of possible translations and rotations used to transform one grid into the other. The transformation resulting in the best match is used to correct the robot's position estimate. This technique has been tested on a real mobile robot and has demonstrated robustness to transient changes (moving people) and lasting changes (rearranged obstacles) in dynamic environments.

This article describes techniques that have been developed for spatial learning in dynamic environments and a mobile robot system, ELDEN, that integrates these techniques for exploration and navigation. In this research, we introduce the concept of adaptive place networks, incrementally-constructed spatial representations that incorporate variable-confidence links to model uncertainty about topological adjacency. These networks guide the robot's navigation while constantly adapting to any topological changes that are encountered. ELDEN integrates these networks with a reactive controller that is robust to transient changes in the environment and a relocalization system that uses evidence grids to recalibrate dead reckoning.

A description is given of the Independent Agents Architecture, designed for building behavior-based robots which make use of sophisticated, computationally expensive sensory modalities. Systems built using this architecture have three layers corresponding to perception, behavior, and motor control. The use of a shared perception layer prevents unnecessary redundancy in the processing of sensor data. Robotic tasks are decomposed into independent stimulus/response behaviors, each of which is implemented by a separate agent. The formalism of stimulus/response spaces is introduced to model these sensorimotor behaviors. This formalism provides a method to analyze when behavioral systems are free from potential action conflicts. In addition, it is demonstrated that it is possible to build a robotic system which performs dynamic tasks through the use of qualitative sensorimotor behaviors instead of mathematical models of the physical world. Juggler, a behavior-based balloon-bouncing robot, is an example of such a system.>