High level behavior for an autopilot enabled model aircraft

Path planning for small unmanned aerial vehicle (SUAV) has been researched for its high potential in urban deployment. There are two kinds of path planning for SUAV, namely deliberative path planning and reactive path planning. Deliberative path planning can only be implemented based on the presupposed knowledge of the navigation environment prior to performing a flight mission and thus, is deemed as inadequate for urban missions. On the other hand, reactive path planning allows interactive in-flight amendments made on the fight mission of SUAV. After all, the prerequisite for urban deployment lies in the capability of avoiding obstacle and preventing collision in reactive path planning. The existing commercially off-the-shelf (COTS) autopilot system has been able to perform deliberative path planning. As such, this project aims to develop high level behavior (i.e. obstacle avoidance behavior) for an existing autopilot enabled SUAV system. The project was kick-started with conducting a comprehensive review on the existing autopilot control architecture integrated in a rotary-wing SUAV. Through exploring the PID feedback loops that come with the autopilot module, it was conceived that one of the PID feedback loops can be adopted to develop obstacle avoidance behavior for the rotary-wing SUAV. Validation on the feasibility of this PID feedback loop for reactive path planning was accomplished by conducting flight tests. The result of the flight tests showed that the hover location of the navigating rotary-wing SUAV can be controlled by a high level behavior control layer, Navigational Reactive Behavior Controller (NRBC) by virtue of the PID feedback loop. Specifically, the path planning of a navigating SUAV can be generated based on the control inputs from its payload sensors. This hover location control mechanism is known as Imaginary Waypoint Generation (IWG) in this report. Based on this result, a simple obstacle avoidance behavior was proposed to be incorporated into the rotary-wing SUAV. This behavior was implemented using the IWG algorithm to generate feasible subsequent movement of the SUAV towards a desired location upon the activation of high level behavior by a laser sensor (payload sensor) mounted on the SUAV for frontal obstacle detection. Intensive flight tests had been conducted to validate the proposed obstacle avoidance algorithm. The results showed that the rotary-wing SUAV had successfully performed the intended commands generated by the obstacle avoidance algorithm using the laser sensor. The precision of distance readings captured by the laser sensor is greatly affected by flight vibration. A digital filtering algorithm resembling a band-pass filtering mechanism was included in the obstacle avoidance behavior algorithm. This algorithm rejects distance readings that fall out of a preset distance range. The transmission of control inputs from the laser sensor to the autopilot system would only be triggered by the distance that stays within the preset range. This algorithm succeeded to improve the precision of the laser sensor. Flight test results showed that invalid distance readings would not trigger the obstacle avoidance behavior. In conjunction with the capability of performing autonomous deliberative path planning, the rotary-wing SUAV is now equipped with the capability of avoiding obstacle and preventing collision.Bachelor of Engineering (Mechanical Engineering