No abstract

Future generation of landing craft will autonomously look at the surface during the terminal phase of powered descent and then, in real-time, choose and divert to a safe landing site in order to avoid hazards. Enabling technologies for such capability have been under development in recent years in the Autonomous Landing Hazard Avoidance Technology (ALHAT) project funded by NASA's Exploration Technology Development Program.ALHAT is a comprehensive system that spans the approach and landing events -from de-orbit coasting to touchdown. In this paper, we focus on ALHAT's perception task of detecting hazards in the sensed terrain and of selecting candidate safe sites for landing. This task, named Hazard Detection and Avoidance (HDA), occurs in the middle of the landing sequence. Our approach to HDA employs a probabilistic model in order to better manage the ubiquitous uncertainties associated with noisy sensor measurements and navigation. Also, we explicitly take into account the geometry of the lander and its interaction with the surface when assessing hazards. Experimental results on synthetic Lunar-like terrain show that our HDA algorithm can designate safe landing locations for a variety of terrain types and density and abundance of hazards. The complete ALHAT system is undergoing ground field-testing, and is scheduled for additional field tests on a one-hectare, lunar-like, hazard field recently constructed at NASA's Kennedy Space Center (KSC). Although the focus of ALHAT is on autonomous planetary landings, a number of terrestrial applications can also benefit from out HDA system.

In May 2008, the Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project conducted a helicopter field test of a commercial flash lidar to assess its applicability to safe lunar landing. The helicopter flew several flights, which covered a variety of slant ranges and viewing angles, over man-made and natural lunar-like terrains. The collected data were analyzed to assess the performance of the sensor and the performance of two algorithms: Hazard Detection (HD) and Hazard Relative Navigation (HRN). The collected flash lidar data were also used to validate a high fidelity flash lidar software model used in ALHAT Monte Carlo simulations. The field test results, combined with prior simulation results, advanced the technology readiness level of the HD algorithm to TRL 5 and the HRN algorithm to TRL 4. 12

To increase safety and land near pre-deployed resources, future NASA missions to the moon will require precision landing. A LIDAR-based terrain relative navigation (TRN) approach can achieve precision landing under any lighting conditions. This paper presents results from processing flash lidar and laser altimeter field test data that show LIDAR TRN can obtain position estimates less than 90m while automatically detecting and eliminating incorrect measurements using internal metrics on terrain relief and data correlation. Sensitivity studies show that the algorithm has no degradation in matching performance with initial position uncertainties up to 1.6 km.

Water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation over cross-country terrain. Under the Robotics Collaborative Technology Alliances (RCTA) program, the Jet Propulsion Laboratory (JPL) developed a set of water detection algorithms that are used to detect, localize, and avoid water bodies large enough to be a hazard to a UGV. The JPL water detection software performs the detection and localization stages using a forward-looking stereo pair of color cameras. The 3D coordinates of water body surface points are then output to a UGV's autonomous mobility system, which is responsible for planning and executing safe paths. There are three primary methods for evaluating the performance of the water detection software. Evaluations can be performed in image space on the intermediate detection product, in map space on the final localized product, or during autonomous navigation to characterize the avoidance of a variety of water bodies. This paper describes a methodology for performing the first two types of water detection performance evaluations.