The realm of UAVs is expanding, moving from high altitudes and clear skies to fly amongst buildings and in forests. Flying close to the Earth presents many challenges, from cluttered terrain to adverse weather conditions such as fog, rain, or dust. The UAV must be outfitted with sensors to map the environment and control algorithms to navigate it from point to point while compensating for wind gusts and dodging obstacles. The Drexel Autonomous Systems Lab (DASL) at Drexel University has set out to solve these problems.
Flight tests are the cornerstone of UAV research. The tests ensure that hardware and software systems work in concert, bridging the gap between lab development and real-world application. But the cost of performing flights drives most researchers to focus on computer simulations, leaving the issues of implementation to someone else. There was no existing method to test computer code, flight control systems, sensors, and other hardware without actually flying the UAV. To address this issue, the researchers at DASL envisioned the Systems Integrated Sensor Test Rig (SISTR).
SISTR is a hardware-in-the-loop test rig that can be used to characterize and design sensor suites, test control algorithms, and emulate flight tests. The facility is designed to virtually fly the UAV sensors through a realistic environment. Sensor data feeds into a high-fidelity math model of the aircraft, which generates the aerial robot’s motion with a six-degrees-of-freedom gantry. This allows the UAV to be rapidly developed in a controlled, measured setting.
The heart of the rig is a three-degree-of-freedom gantry from Techno Inc. that was custom built to provide the speeds and accelerations needed to simulate UAV flight. The gantry has to have a large envelope of approximately 15 ft wide by 20 ft long by 10 ft tall to enable the UAV to fly completely through the environment. In addition, it needs very fine motion control and high levels of speed and acceleration to simulate UAVs flying through the space.
The remaining three degrees of freedom are provided by a Drexel-made pan/tilt/roll unit attached to the end of the gantry beam. The axes of rotation intersect at the center of the sensor, mimicking how most aircraft rotate at their center. This approach decouples rotations, allowing independent control over each axis.
The environment is represented by a scaled mockup of a near-earth environment. By scaling down the environment, a much larger area can be re-created inside the confined lab space. The environment was created at 1/87 scale so HO railroad accessories can be used to dress up the model.
A nonflying mockup of the UAV equipped with collision-avoidance sensors is attached to the gantry. The sensor data feeds into a high-fidelity math model of the real-world aircraft. The math model is used to control the motion of the gantry. The test rig also has a rain machine, dust machine, fog system, fans, and lamps to reproduce rain, dust, and fog. Twelve 750-watt lamps on the top of the rig are used to simulate day and night conditions.
The gantry is able to duplicate a large portion of the operating range of the UAV. All translational axes can be controlled within ±0.5 cm. This scales up to a resolution of ±0.43 m, well within the ±2 m accuracy of off-the-shelf GPS systems. Sensors are mounted on the gantry and virtually flown through the environment. Real-time data are collected by the same software that will be used in flight. Control commands are fed into a mathematical model of the aircraft, which generates aircraft positions that are used to drive the gantry.
Aircraft were flown in the test rig at unscaled speeds ranging from 1 to 40 m/s. The velocity was gradually varied to determine if there is an upper or lower bound on the boundaries that the test rig could reproduce. When the aircraft was moving at velocities greater than 30 m/s, the motion was erratic because the control was not updating fast enough. When the aircraft was moving slowly at around 1 m/s, it was difficult to overcome static friction. Acceptable results were seen in the range between these extremes, and the best results were obtained in the neighborhood of 5 m/s.
The scaled tests were verified against baseline computer simulations. A standard
UAV mission was selected in which the UAV uses a camera to guide itself to the center of a window while remaining a fixed distance from the building. Results showed that the tests at 1/87 scale were similar to simulations.