SBIR-STTR Award

The innovative use of variational methods to dynamically segment scenes, which leads to a fast, natural approach to estimating the location of unknown 3-D obstacles is proposed. The previously developed algorithm has been shown in simulation to be suitable for real-time processing in flight using current generation processors, and to be robust in the presence of transient sensor data, distortion, and obscuration. It is employed to rapidly construct a 3-D database in the flight path ahead of the vehicle, and is combined with custom-developed guidance laws to produce a real-time in-flight obstacle detection and avoidance capability using only a sequence of 2-D images. This technology is to be tailored to application on the Block II production Wasp micro air vehicle and evaluated in near-real-time simulation. Simulation results are to be validated using flight video collected on the Wasp. Sensitivity studies will be conducted to develop a set of design requirements, and a preliminary design for hardware and software implementation completed. Detailed design, development, flight test on the Wasp, algorithm refinement, and demonstration at an MOUT test site will be carried out in phase II.

Keywords:
Micro Air Vehicle, Collision Avoidance, Obstacle Avoidance, Guidance, Control, Image Processing

It is proposed to further develop, flight demonstrate and transition a robust capability to autonomously detect and avoid obstacles typically encountered in flight by fixed or rotary wing micro air vehicles during low-altitude urban operations. There are two basic elements: (1) continually detecting and modeling the observable 3-D obstacle field in real-time, and (2) autonomously guiding the vehicle to accomplish a given set of mission objectives while ensuring there are no collisions with obstacles. A key innovation of the proposed approach is maturation of a technique to accomplish the stated objective that requires only a single 2-D image stream (a stream available on essentially every unmanned air vehicle in the inventory today). A key technical challenge is to realize the system in hardware that simultaneously meets demand for real-time execution of computationally intense algorithms and severe constraints on subsystem mass, volume and power consumption. Demonstrated phase I algorithms will immediately move into practical implementation and evaluation on an existing low-cost surrogate flight test vehicle using commercial-off-the-shelf FPGA hardware. Hardware and software design specific to integration on the Wasp micro air vehicle will proceed in parallel. Flight demonstration of the developed technology on the production Wasp vehicle is proposed as a program option.

Keywords:
Obstacle Avoidance, Collision Avoidance, Vision-Based Guidance, Image Processing, Micro Air Vehicles, Unmanned Air Vehicles, Application Of Field Prog