TACTILE TERRAIN PREDICTION FOR AUTONOMOUS AND TELE-OPERATED GROUND VEHICLES

Lidar, Sonar, and Vision-based measurements are often used to preview terrain topology for unmanned ground vehicles. Environmental conditions such as wet or snow-covered roads, shadows, superficial ground coverings, and deceptive surface textures can lead to erroneous measurements. Tactile terrain prediction is both an alternative and a supplement to existing measurement systems. Tactile feedback from an array of low-cost sensors on the moving vehicle is used to generate low wave-number terrain profile predictions. This paper presents tactile terrain prediction results evaluated on four unique courses. Prediction error data are presented up to 25m in front of the vehicle. Results indicate 0.02-0.2m RMS error and 0.18-1.0m peak error at a 10m look-ahead distance. As expected, the prediction errors decrease exponentially as the look-ahead distance decreases. The relatively small prediction errors suggest that the proposed tactile terrain prediction method is a viable low-cost option for use in autonomous and teleoperated ground vehicles. INTRODUCTION Unmanned ground vehicles (UGV’s) have demonstrated an ability to support in theater operations including missions such as reconnaissance, surveillance, supply transport, and protection, as well as neutralizing improvised explosive devices [1]. Future operational needs call for increased levels of autonomy and perception-based adaptive tactical behavior. Current autonomous and tele-operated ground vehicles typically employ overly conservative path planning and driving strategies to ensure safe operation over challenging and uncertain terrain. UGV platforms are expected to achieve significant increases in performance through an improved understanding of local terrain characteristics [1], thereby enabling the vehicle to optimally adjust driving strategies and path planning to maximize performance. Lidar or ladar measurement systems have been used on UGV’s to provide preview information of the terrain topology in the immediate path of the vehicle [2-4]. Visionbased measurement systems are being used more frequently to provide similar terrain preview information to either replace or supplement lidar data [5-8]. Unfortunately, both of these measurement solutions often provide erroneous terrain measurements for common environmental conditions such as wet or snow-covered roads, shadows, superficial ground coverings, and deceptive surface textures [9]. Tactile feedback is an emerging solution that can be considered as either an alternative or a supplement to existing terrain measurement systems, depending on the application needs. Although a variety of strategies have been developed, tactile feedback typically utilizes an array of low-cost sensors attached to the moving UGV to predict the macro-scale features of local terrain in the immediate path of the vehicle [10-11]. Tactile feedback uses past history to extrapolate future terrain profile information. The objective of this paper is to document a preliminary evaluation of a proprietary tactile feedback method for terrain profile prediction. This tactile terrain prediction method was implemented on a 6-DOF human-in-the-loop (HIL) driving simulator, and was subsequently evaluated on four unique test courses under a variety of operating conditions. Experimental prediction errors at look-ahead preview distances up to 25m are presented. Proceedings of the 2011 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS) Tactile Terrain Prediction for Autonomous and Tele-Operated Ground Vehicles Page 2 of 6 TACTILE TERRAIN MEASUREMENTS Figure 1 depicts a UGV traveling along a path s through a Cartesian coordinate system defined by coordinates (x, y, z). Path planning algorithms require information about the terrain surface topology immediately in front of the vehicle as indicated by the green triangular patch in figure 1. The extent of the look-ahead preview distance will ultimately depend on the maximum speed that the UGV will be traveling, as well as the computational speed of the pathplanning algorithm; however, this distance is always finite. Figure 1. Unmanned ground vehicle with triangular lookahead terrain preview region The tactile terrain prediction algorithm evaluated in this effort is represented by the block diagram of figure 2. Relatively low-cost on-board sensors such as accelerometers and inclinometers [11] mounted on the UGV provide tactile measurements of the terrain topology at the current location. These measurements are combined with a finite and immediate past history of similar terrain topology data as indicated in figure 2. This historical data is then used to predict the low wave-number features of the terrain in a finite look-ahead preview region using an extrapolation technique. The explicit details of this algorithm are restricted from publication, pending a patent filing. Figure 2. Unmanned ground vehicle with triangular lookahead terrain preview region As should be expected, the tactile feedback approach does not have the ability to predict obstacles, and the prediction error is expected to increase as the look-ahead preview distance increases. Even with this known limitation, predictions of terrain topology based on tactile feedback can be fused with lidar or vision-based data to overcome the measurement deficiencies, and ultimately improve the overall accuracy for path-planning and autonomous navigation. Ideally for UGVs on rough terrain, tactile terrain prediction algorithms will enable the processing of lidar and/or vision data to focus on obstacle detection through data fusion with the predicted terrain profile. HIL EXPERIMENTAL TEST PLATFORM In order to evaluate the performance of the proprietary tactile terrain prediction method over a range of terrain courses, vehicles, drivers, and operational conditions, the prediction system was integrated with a 6-DOF human-inthe-loop (HIL) driving simulator as shown in figure 3. In this HIL driving simulator, the steering, throttle, brake, and gear selection commands from the human driver are processed through a natural cockpit interface. These commands are applied as inputs to one of many possible vehicle dynamics simulation models, which interact with one of many possible terrain maps to provide visual, audio, 6-DOF motion, and tactile feedback to the driver. Within the virtual environment, all pertinent signals of interest were available for predicting the look-ahead terrain profile. In order to more accurately represent a real vehicle on actual terrain, apriori knowledge of the terrain profile was not used in this study. Figure 3. 6-DOF Driving Simulator used for experimental HIL testing of tactile terrain prediction algorithm EVALUATION CASE STUDIES Four test courses were selected to evaluate the tactile terrain prediction method. The first three courses were paved tracks: (A) Road America, in Elkhart Lake, Wisconsin; (B) Virginia International Raceway, in Alton, Virginia; and (C) Zandvoort, in the Netherlands. The fourth course was a mixture of off-road terrain and rough pavement: (D) Pikes Peak, in Colorado Springs, Colorado. These courses and the corresponding test conditions are summarized in table 1. Proceedings of the 2011 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS) Tactile Terrain Prediction for Autonomous and Tele-Operated Ground Vehicles Page 3 of 6 Table 1. Summary of courses and drivers used in case study. Case Course Driver Course Description A Road America Human 6604 m course length 48 m elevation change B Virginia Int’l Raceway AI 6359 m course length 46 m elevation change C Zandvoort AI 4427 m course length 9 m elevation change D Pikes Peak Human 11630 m course length 914 m elevation change Figure 4 is a plot of overhead views for each of the four test courses where the x–axis represents the easting direction in meters, and the y–axis represents the northing direction in meters. Waypoint markers have been included on each of the subplots in figure 4. For courses A, B, and C, the waypoint spacing is 1000 meters, and for course D, the waypoint spacing is 2000 meters. The waypoint markers with subscript-1 represent the starting point for each test. Figure 4. Test courses: (A) Road America, (B) Virginia International Raceway, (C) Zandvoort, (D) Pikes Peak. From table 1, notice that courses A and B are similar in length and elevation change. Course C was the shortest with the least amount of elevation change, and course D was the longest with the largest elevation change. Courses A and D were driven with a human driver in the loop to simulate a tele-operated ground vehicle, and courses B and C were driven with an artificial intelligence (AI) driver to simulate a fully autonomous ground vehicle. During the testing, both a human and an AI driver were independently allowed to drive on the same test course. No significant differences were observed between the prediction errors for the fully autonomous and the tele-operated cases. This is a strong testament to the robustness of the proprietary tactile terrain prediction algorithm to driver style and speed. In all test cases, the ground vehicle speeds ranged from zero to 80 kilometers/hour. TERRAIN PREDICTION RESULTS The results of each of the four test cases are presented in figures 5, 6, 7, and 8 respectively. Each figure contains four subplots, and each subplot shares a common horizontal axis, which is the path position s in meters. The topmost subplot in each figure is the elevation profile z in meters, as a function of path position. The corresponding waypoint markers are included on the elevation profiles to enable cross-referencing with figure 4. The second subplot from the top of each results figure is the localized Grade of the terrain profile expressed with units of degrees. The grade is a measure of the spatial rate of change of elevation along the path. The third subplot from the top of each figure is the localized Grade Rate, which is the spatial r