Three-dimensional navigation will be an essential component of low-altitude unmanned rotorcraft operations in urban environments. Successful navigation will require that the vehicle sense the surrounding obstacles, incorporate the data into its world model, and react to new obstacles to ensure both vehicle survivability and satisfactory completion of the mission objectives. A complete navigation solution built on heuristic planning concepts is presented. A fast A*-based 3D route planner is compared with one that constructs 3D routes by executing a 2D planner on plane slices of the terrain. Monte Carlo simulation evaluation and flight test validation results are presented.

A critical element of rotorcraft autonomy is a flight control system that can operate harmoniously with the various autonomy components that depend on it. This is particularly true for highly interactive components, such as obstacle field navigation (OFN), where the vehicle navigation course is constantly being altered as more terrain information is gathered. This paper describes the development, integration, and flight-testing of an autonomous flight control system (AFCS) on a JUH-60A research helicopter. Flight-test results include control law validation using frequency domain analysis and performance characteristics using both ADS-33E mission task elements and path-error measurements. These performance data are then used to configure a risk minimizing OFN algorithm with the AFCS. The integrated OFN algorithm and AFCS are demonstrated in flight by navigating autonomously through 23 mi of mountainous terrain.

An important element of rotorcraft UAV operations is safe landing area determination (SLAD), which is the ability to select desirable landing or load placement areas at unprepared sites. Effectively and reliably accomplishing this task would greatly enhance high-level autonomous capabilities in many operations such as search and rescue and resupply. 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 algorithm uses laser range data to optimize a performance index, based on sliding window statistics of surface slope and roughness over the landing zone, to select potential landing points. Flight-test data were collected at six 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.

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