A high speed three dimensional radar scanner capable of providing the high resolution range maps necessary for outdoor mobile robotic vehicles is described in this paper. Unlike traditional laser rangefinder based scanners, this sensor is intended to operate in real world environments plagued with dust, fog, and rain. The 94 GHz pulsed time-of-flight radar system has a peak repetition rate of 250 kHz, a 23 cm beam focused at 8m with an effective 1/spl deg/ divergence, and a 180/spl deg//spl times/360/spl deg/ scanned field of view. DSP processing of the range data provides a reduced data rate while taking full advantage of the higher firing rate to improve accuracy and sensitivity to specular and poorly reflective targets.

Hydraulic machines used in a number of applications are highly non-linear systems. Besides the dynamic coupling between the different links, there are significant actuator nonlinearities due to the inherent properties of the hydraulic system. Automation of such machines requires the robotic machine to be at least as productive as a manually operated machine, which in turn makes the case for performing tasks optimally with respect to an objective function (say) composed of a combination of time and fuel usage. Optimal path computation requires fast machine models in order to be practically usable. This work examines the use of memory-based learning in constructing the model of a 25-ton hydraulic excavator. The learned actuator model is used in conjunction with a linkage dynamic model to construct a complete excavator model which is much faster than a complete analytical model. Test results show that the approach effectively captures the interactions between the different actuators.

Excavators are used for the rapid removal of soil and other materials in mines, quarries, and construction sites. The automation of these machines offers promise for increasing productivity and improving safety. To date, most research in this area has focused on selected parts of the problem. In this paper we present a system that completely automates the truck loading task. The excavator uses two scanning laser rangefinders to recognize and localize the truck, measure the soil face, and detect obstacles. The excavator's software decides where to dig in the soil, where to dump in the truck, and how to quickly move between these points while detecting and stopping for obstacles. The system was fully implemented and was demonstrated to load trucks as fast as human operators.

Dante II is a tethered mobile robot designed for volcano exploration. It has 6 pantographic legs, and is a rappelling robot that uses a tether to support itself on steep terrain, just as a mountaineer uses a climbing rope. The tether is connected to a generator and satellite communication station located at the volcano's rim. The satellite station relays data to and from remotely located operators. This paper describes the issues faced in the design of the tethering system of Dante II, and the approach used to address these issues.

Robotic rappelling is an intriguing concept for exploration of planetary craters and their Earth analogs, volcanoes. Integrating a tensioned tether to a framewalking robot enables a new statically-stable locomotive capability appropriate for rappelling on steep and rugged terrains. Rappelling with a tether-assisted framewalker also allows efficient execution of multi-level control. These ideas are manifested in the locomotion configuration of Dante II. The appropriateness of the Dante II configuration for rappelling was evaluated during a variety of tests and its 1994 exploration of the active volcano of Mount Spurr in Alaska.

The Dante project is developing walking robots to explore inside volcanic craters. These robots face many challenges including generating a walking gait in rough, obstacle-filled terrain. For the walking robot Dante II, we implemented a gait controller to address this situation. Our approach is embodied in a network of asynchronous processes that establish a fundamental gait cycle while maintaining body posture, and reacting to bumps and slips. We describe our implementation, and its relation to similar behavioral approaches, and discuss Dante II's performance during testing and on its descent into Mount Spurr.