The RANGER Navigator
Investigators: Alonzo Kelly, Anthony Stentz
RANGER has navigated over distances of 15 autonomous kilometers, moving continuously, and has at times reached speeds of 15 km/hr. The system has been used successfully on a converted U.S. Army jeep called the NAVLAB II and on a specialized Lunar Rover vehicle that may, one day, explore the moon.
The key to the success of the system is its adaptability. It explicitly computes the vehicle reaction time and required sensory throughput and adapts its perception and planning systems to meet the demands of the moment.
RANGER is an innovative solution to the difficulties of autonomous control of land vehicles based on a tightly-coupled, adaptive feedforward control loop. The system incorporates measurements of both the state of the vehicle and the state of the environment and maintains high fidelity models of both that are updated at very high rates.
Hazards include regions of unknown terrain, hills that would cause a tip-over, holes and cliffs that would cause a fall, and small obstacles that would collide with the vehicle wheels or body.
In the figure, the system issues a left turn command to avoid the hill to its right. The histograms represent the votes for each candidate trajectory, for each hazard. Higher values indicate safer trajectories. The hazards are excessive roll, excessive pitch, collision with the undercarriage, and collision with the wheels. The tactical vote is the overall vote of hazard avoidance. It wants to turn left. The strategic vote is the goal-seeking vote. Here it votes for straight ahead.
At times, goal-seeking may cause collision with obstacles because, for example, the goal may be behind an obstacle. The system incorporates an arbiter which permits obstacle avoidance and goal-seeking to coexist and to simultaneously influence the behavior of the host vehicle. The arbiter can also integrate the commands of a human driver with the autonomous system.
RANGER accommodates both laser rangefinder and stereo perception systems and it incorporates its own integrated stereo correlation algorithm. In either case, the design achieves significant increases in vehicle speeds without sacrificing either safety or robustness.
Perception has long been acknowledged as the bottleneck in autonomous vehicle research. Yet, a moving vehicle generates images which contain much redundant information. Removal of this redundancy is the key to fast moving robot vehicles.
RANGER incorporates a sophisticated Kalman Filter algorithm that merges the indications of all of the navigation sensors into a single consistent estimate of the vehicle position, attitude, and velocity. Any number of sensors in any combination can be accommodated including, wheel or transmission encoders, compasses, gyroscopes, accelerometers, doppler radar, inclinometers, terrain aids such as landmarks and beacons, and inertial and satellite navigation systems.
While the real-time core of the system can be expressed in about 1000 lines of C, RANGER includes a complete simulation and development environment incorporating a data logger and simulators for natural terrain, vehicles, sensors, and pan/tilt mechanisms. Real-time animated graphics provide feedback to the human supervisor. A custom C language interpreter is used to configure and control the system at run-time.
Implemented in C language, the system is composed at the highest level of four objects. The Map Manager integrates all environmental sensor images into a single consistent world model. The Controller implements hazard detection and avoidance, goal-seeking, and arbitrates between them. The Vehicle encapsulates the state of the vehicle and provides dynamics simulation and feedforward. The Kalman Filter implements the position estimation system.
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