Optimized Autonomous UAV Design with Obstacle Avoidance Capability

Obstacle Avoidance and Navigation (OAN) algorithms are an active research field dominated by either offline or online methods. The former method is fast but requires a prior known map while the latter method can function without a prior known map at the expense of high computational requirements. To bring OAN algorithm to mass produced mobile robots, more precisely multirotor Unmanned Aerial Vehicles (UAVs), the computational requirement of robust algorithms must be brought low enough such that the computation can be done on an onboard companion computer, while being able to operate without prior knowledge of the map. In this project, we propose a novel OAN algorithm – dubbed the Closest Obstacle Avoidance and A* Algorithm (COAA*) – to bridge the capabilities of current offline and online OAN algorithms. The proposed algorithm takes into account the UAV performance limits and is simple to calibrate and incorporate for many other classes of mobile robots. The main contributions of this research work are that COAA* has guaranteed convergence to a global minimum for the navigational trajectory, while being very computationally lightweight due to its first principles formulation. This algorithm has been implemented on an actual low-cost UAV, with multiple successful flight trials to validate its obstacle avoidance capability while navigate from one point to another.