The Crusher System for Autonomous Navigation

We present the Crusher system for autonomously navigating complex off-road terrain. In this paper, we describe the Crusher system’s three-pronged approach for safely and reliably moving between widely-spaced waypoints. First, the system automatically interprets aerial map data to assess mobility risk and plans trajectories that move from way point to way point. Second, the system uses a ladarand camera-based perception system to detect and avoid hazards that are not discernable in the map data or that appear after the area is mapped. Third, the Crusher vehicle breaches hazards too difficult for on-board sensors to detect. The autonomy software is adaptive for operation on a wide range of terrain types. To date, the Crusher system has been tested on terrain with dense trees, long washes, deep ditches, steep slopes, thick brush, and large rocks. In early 2007, the system was tested at Ft. Bliss in Texas, where it drove over two hundred fifty kilometers autonomously. INTRODUCTION We define autonomous navigation as the task of driving a ground vehicle, equipped with sensors for measuring properties of the terrain, computers for interpreting the data, and actuators for steering, accelerating, and braking the vehicle, from way point to way point across a given terrain, such that the vehicle operates safely and within the correct performance envelope for the operation, but with little or no human involvement or intervention. The vehicle may be assisted by a map of the terrain, if one is available, but it is able to drive successfully without such a map. The way points may be closely spaced or widely separated—the latter implies that the vehicle may need to plan and follow routes, in addition to avoiding obstacles. Ideally, there is no human involvement at all. If an intervention is required, remotely driving out of trouble is considered far less severe than stopping the vehicle to prevent catastrophe. TECHNICAL APPROACH The research described in this paper significantly pushes the state of the art for autonomous navigation in complex terrain. Progress in the field requires a complete system effort, pushing the limits of many component technologies such as obstacle detection and avoidance, route planning, position estimation, vehicle control, sensor fusion, and map interpretation. We list the key elements of our approach below. The Crusher system is the only cross-country navigator to embody all of these elements in a single system: Multi-Sensor, Multi-Feature, Multi-Viewpoint, Multi-Range/Resolution Ground and Aerial Perception: for complex terrains, it is very difficult to distinguish hazards (e.g., rocks) from non-hazards (e.g., bushes). The Crusher system uses multiple sensor modalities, including red/green/blue and near infrared cameras, and ladar range and remission data to maximize the chances that there will be signature data present. Numerous features are extracted from the data, including local shape, range pixel density, normal vector orientation, the difference between visible red and near infrared light, and others to reduce the data bandwidth without reducing the information content. The data is acquired from multiple viewpoints (i.e., air and ground) as well as multiple ranges and resolutions. The system reasons about which sensor data to believe given sensor type, range, resolution, and hazard/non-hazard type. This approach has enabled Crusher to detect hazards such as steep slopes, ditches, ravines, holes, trees, boulders, and rocks, man-made obstacles (e.g., vehicles and other machinery), and areas with poor traction. Learning Algorithms, Risk Assessment, and Data Inferences: given the large variety of both hazards and nonhazards on natural terrain, it is not possible to fully program a system to correctly classify all of them; instead, we advocate a learning approach to assist in the process. Our algorithms learn from human-provided examples as well as logged vehicle data, both off-line and on-line. These algorithms have been instrumental in significantly improving our performance on non-hazards. The Crusher system is capable of distinguishing hazardous vegetation (e.g., stout bushes, bramble, and thick branches) from non-hazardous vegetation (e.g., grass, weeds, sparse bushes). In cases where the level of hazard is uncertain, the system assigns a risk value (i.e., continuous cost) so that the planner is able to trade-off driving over the terrain feature with taking an alternative route. Furthermore, the Crusher system is able to infer the presence of hazards it cannot see, such as those occluded by vegetation cover. The approach includes slowing the vehicle down for shadowed areas and “filling in” missing data to infer holes and estimate the supporting ground plane. Obstacle Avoidance and Maneuvering for Difficult Environments: the Crusher system assumes that the vehicle will need to thread its way around obstacles, squeeze through tight spaces, and turn on surfaces with poor traction. The system is able to plan and execute the trajectories necessary to perform these actions, including coupling forward and backward motion and making use of a calibrated vehicle model to minimize tracking error. Continuous Route Re-planning: at every point in its traverse, the Crusher system selects the best route given the latest and most accurate information available. Thus, the system re-plans continuously as new information is received. It makes use of sketchy obstacle information acquired at a long range and then updates its route as the vehicle draws near and acquires more accurate information. The system represents map data at the highest resolution available and fuses information from multiple passes over the same area to ensure the vehicle has an accurate picture of the options available to it. This approach is essential for driving in cluttered environments where few safe options may be available. Furthermore, it has also enabled the Crusher system to navigate with any amount of terrain uncertainty, since the system does not require a prior map but makes the best use of the information available to it. The Crusher system uses a three-pronged approach to perform a “mission”, consisting of a sequence of widely spaced way points on a given terrain. First, if a map is available, the system analyzes it for navigable areas and hazards and selects a route that moves the vehicle safely from one way point to the next. Second, the system scans the terrain with its sensors as the vehicle drives to detect hazards too small to be resolved from map data or which appeared after the terrain was mapped. The system avoids the hazards or re-plans an alternative route as needed. Third, the Crusher vehicle is designed to breach/survive some hazards that were missed by the perception system and would have been avoided otherwise. CRUSHER VEHICLE High Mobility Platform Crusher is a 6,800 kg, six-wheeled, hybrid-powered robot which is extremely capable in the most severe terrain. Figure 1 lists the vehicle’s performance specifications. Figure 1: Crusher performance specifications. High-strength aluminum tubes and titanium nodes make up Crusher’s space frame hull design. Crusher can comfortably carry over 8000 lbs. of payload and armor. The hybrid electric system allows the vehicle to move silently on one battery charge over miles of extreme terrain. A 60 kw turbo diesel engine maintains charge on a high-performance lithium ion battery module. Engine and batteries work intelligently to deliver power to Crusher's six-wheel motor-in-hub drive system. Crusher’s advanced suspension supports 30 inches of travel with selectable stiffness and reconfigurable ride height. This suspension system provides a smooth ride for the navigation sensors over rough surfaces even at speeds up to 12 meters per second and enables the vehicle to climb obstacles and cross gaps (see Figure 2). Figure 2: Crusher breaching an obstacle (left) and crossing a ditch (right). A suspended and shock-mounted skid plate made from high-strength steel enables Crusher to shrug off massive, below-hull strikes from boulders and tree stumps. This skid plate enables Crusher to survive driving over rocks that are hidden in vegetation—an obstacle that is hard for an autonomy system to detect without a foliage penetrating sensor. Crusher’s bumper was designed to absorb the impact energy from major frontal collisions with trees and rocks. This bumper allows the autonomy system to confidently interact with the terrain to determine if an object is crushable or not (e.g., vegetation, boulder, cactus, tree, etc.). In short, the Crusher vehicle can survive an autonomy system that makes an occasionally mistake. Furthermore, the autonomy system can learn from such mistakes without sacrificing the vehicle. Navigation Sensors Crusher uses laser rangefinders to measure the geometry of the terrain. The geometry is important for determining the supporting surface, positive obstacles such as trees, and negative obstacles such as ditches. Crusher uses cameras in the red/green/blue/near infrared range to measure the appearance of the terrain. The appearance provides important clues about the material properties of the terrain, for example, which portions are vegetation and which portions are solid objects. In order for the cameras to be usable outdoors, we developed a high-dynamic range (HDR) system that fuses images with multiple exposure times to compensate for a wide range of illumination. Crusher operates in complex off-road terrain and thus requires a wide sensor field of view, since the vehicle must see in many directions to negotiate a cluttered, confined area. Figure 3 shows the sensor pod configuration on Crusher. The sensor pod is made up of laser rangefinders and cameras that give a total field of view of 180 degrees. The sensor pod laser rangefinders consist of six SICK LMS ladar sensors, each with a 90 degree field of view, 0.5 degree beam spacing, 181 points per scan, and 75 Hz u