Autonomous Navigation Field Results of a Planetary Analog Robot in Antarctica
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AUTONOMOUS NAVIGATION FIELD RESULTS OF A PLANETARY ANALOG ROBOT IN ANTARCTICA Stewart Moorehead, Reid Simmons, Dimitrios Apostolopoulos and William "Red" Whittaker The Robotics Institute Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA. 15213 U.S.A. phone: 1-412-268-7086, fax: 1-412-268-5895, email: [email protected] phone: 1-412-268-262 1, fax: 1-412-268-5576, email: [email protected] phone: 1-412-268-7224, fax: 1-412-268- 1488, email: [email protected] phone: 1-412-268-6556, fax: 1-412-682-1 793, email: [email protected] ABSTRACT tion in polar terrain and meteorite detection/classification 191. Experiments were also performed on characterizing The Robotic Antarctic Meteorite Search at Curnegie Mel- laser and stereo sensors [14], systematic patterned search lon is developing robotic technologies to allow for auton- [lo], ice and snow mobility, landmark based navigation omous seart.h and class$cation of meteorites in and millimeter wave radar [I]. Foot search by the expedi- Antarctica. In November 1998, the robot Nomad was tion found two meteorites [5]. r1eployc.d in the Patriot Hills region of Antarctica to per- ,form several demonstrations and experiments of these technologies in a polar environment. Nomad drove 10.3km autonomously in Antarctica under a variety of weather and terrain conditions. This paperpre- sents the results of this traverse, the ability of stereo ~lisionand laser scanner to perceive polar terrain and the rlutonomous navigation system used. 1 INTRODUCTION From the Lunakhods on the Moon to Sojourner on Mars 161, mobile robots have demonstrated their usefulness to planetary exploration. As future missions become more ambitious, mobile robots will be required to do more tasks in shorter periods of time necessitating an increased level of autonomy. In particular, mobile robots will be called upon to drive long distances with little or no super- Figure I : Nomad at the Patriot Hills vision to achieve the goals of planetary science. As one of the harshest environments on Earth, Antarctica Very few robots have been deployed to Antarctica. TROV is a unique place to test planetary robotic technologies. 1131 and SARA 171 explored the underwater coastal The low temperatures, lack of communications and regions and Dante I [IS] the volcano Mt. Erebus. How- remoteness make it an interesting terrestrial analog of the ever, to the authors' knowledge no robot for cross country Moon and Mars. In November of 1998, the robot Nomad navigation in polar terrain has been demonstrated. This (Figure 1) was deployed to the Patriot Hills region (80S, meant that many factors were unknown before the expe- XIW) of Antarctica. This deployment was part of Carn- dition such as the ability of stereo and laser sensors to see egie Mellon's Robotic Antarctic Meteorite Search pro- obstacles on snow and ice fields. This uncertainty neces- gram 131 which is developing robotic capabilities to sitated the development of a robust autonomy system. perform Antarctic meteorite searches from a mobile robot. The expedition demonstrated autonomous naviga- ~ - -- - -- --.~ .---- ['roc. Fltih International Symposium on Artificial Intelligence, Robotics and Automation in Space, 1-3 June 1999 (ESA SP-440) This paper presents a description of the autonomy system lution grid based maps (Nomad currently uses a 5Ocm implemented on Nomad in Antarctica and presents the grid resolution) where each cell contains two numbers: a results of its autonomy tests. goodness score indicating the desirability of the robot occupying that cell and a certainty score which indicates 2 NAVIGATIONAL AUTONOMY SYSTEM the reliability of the goodness score. Each of these num- bers are normalized between O and I. Additionally, any The autonomy system drives Nomad through a series of cell with a certainty less than a lower threshold is consid- waypoints while avoiding any obstacles too large for the ered unknown and certainty and goodness values are set robot to drive over. It is descended from that found on explicitly to zero. Ratler [ 121 and Nomad in the Atacama [I61 but differs in Multiple goodness maps can be combined by taking an several ways. An error recovery module has been added average of the goodness values in corresponding cells, which lets Nomad backup and turn when it is blocked by weighted by their certainties. If the maps are created by obstacles or exceeds its roll and pitch specifications. The different sources, then a weight for the confidence in that representation of terrain has been changed to indicate source is also used in the average. For example, on how good it is to occupy a cell and the certainty of that Nomad both the stereo and laser modules create goodness goodness. Finally, the laser has been fully integrated into maps from their sensor readings. The obstacle avoidance the autonomy system. These changes have made the sys- module maintains a local terrain map by combining these tem more reliable and robust. sensor produced goodness maps, we~ghtedby our confi- Figure 2 shows the structure of the autonomy system. dence in that sensor, with its own map. Except for the controller each box is a separate Linux Nomad only uses the perceived roughness of the terrain process running on a single Pentium Pro 133 located on or traversability to determine the goodness of a cell. Nomad. The arrows indicate interprocess communica- However, the goodness map representation is general tions using the Task Control Architecture's (TCA) mes- enough to incorporate other measures in the determina- sage passing capability [ll] and the arrow labels indicate tion of cell goodness. By using multiple criterion when the type of information passed. Messages can also be determining cell goodness, a goodness map provides a passed with TCA over a wireless ethernet link to user unified format to balance competing goals. For example, interface processes running on an external computer. The a goodness map which combines the terrainability, sci- controller is implemented on a 68060 running VxWorks ence interest and the potential for solar power of a cell and performs the low level motor control. would help the robot make trade offs between the three Laser criterion and choose paths which satisfy all of the con- Module straints. Goodness 2.2 STEREO MODULE Nomad has four Sony XC-77 640x480 B&W CCD cam- 1 eras mounted on a sensor yard 1.67m above the ground at Navigation Avoidance Recovery the front of the robot (Figure 3). Each camera has a Com- putar HAS3616APC auto iris, 3.6mm focal length lens. To operate in the cold temperatures of Antarctica the Arcs cameras are enclosed in insulated, heated boxes. Steering Since Nomad is quite wide and able to turn relatively 1 Arbiter I sharply the four cameras are set up as two stereo pairs - one pair looking right the other looking left. They are Command strongly calibrated using the procedure in [8]. The raw images are first dewarped to remove radial lens distortion .I?ilI Controller and then rectified so that the epipolar lines lie on the scan lines. Figure 2: Autonomy System To reduce the cycle time only a small number of rows in 2.1 GOODNESS MAPS the image arc examined by the stereo module. These rows correspond to distances of 4.5m to 8.5m in front of the To model the environment it passes through, Nomad uses robot. The stereo module computes the disparity map in a map structure called a goodness map (an example map this region and takes the (x,y,z) pixel coordinates to create can be found in Figure 4). Goodness maps are fixed reso- a goodness map by using a plane fitting technique. For each cell in the goodness map, stereo fits a plane to the inversely proportional to the average deviation of all the data in a region equal to the size of the robot (a 5x5 grid laser measurements in the cell. Cell certainty is propor- cell area) centered at the active cell. Smaller planes are tional to the number of measurements present in the cell. also fit to each cell in this 5x5 submap. The goodness Cells with goodness values below 0.5 are expanded to fill score of the center cell is then determined by the roll and the 5x5 cell area around them, providing configuration- pitch of the planes as well as the residual from fitting the space obstacles in the map [4]. A large change in the level planes. The certainty is derived from the number of data of the ground line from the previous scan indicates a step points used to create these planes. This process produces feature - such as a cliff - so in this case all map cells with a goodness map where the goodness of a cell is the lowest laser measurements are marked with low goodness. Other goodness of all cells in a 5x5 area. Therefore obstacles than the previous ground level, the goodness map pro- are expanded into configuration space format allowing duced is based entirely on the current scan. planning to consider Nomad as a point robot [4]. The 2.4 OBSTACLE AVOIDANCE goodness map created depends only on the current stereo image. The obstacle avoidance module, named Morphin, is the heart of the navigation system. It maintains a goodness map of the environment around the robot. This map is generated by merging the goodness maps created by the stereo and laser modules. Unlike the sensor goodness maps, Morphin's map contains data from previous sensor module maps. When a new sensor module map arrives Morphin ages its current map by multiplying the certain- ties of each cell by a number less than 1. It then merges in the new data using the cell certainties and sensor type to weight each goodness value.