Evaluation of Safe Landing Area Determination Algorithms for Autonomous Rotorcraft Using Site Benchmarking

An important element of rotorcraft UAV operations is Safe Landing Area Determination (SLAD) to select desirable sites for landing or load placement. Effectively and reliably accomplishing this task would greatly enhance high-level autonomous capabilities in many operations such as search and rescue and re-supply. This paper presents the results of quantitatively evaluating two SLAD algorithms using a new test method that incorporates a detailed survey of the test sites. These survey sites act as benchmarks against which the SLAD methods are compared. One SLAD algorithm is a new approach that uses laser range data to detect a set of potential landing points and uses fuzzy logic to rank them based on surface roughness, size, and terrain slope metrics. The second SLAD method was developed previously under the Precision Autonomous Landing Adaptive Control Experiment (PALACE), Army Technology Objective. Flight test data were collected at seven sites ranging from simple to complex with multiple runs at each site. Both methods are evaluated based on their true positive and false positive rates and the consistency of their landing site selection.