Abstract
Existing resource-intensive obstacle avoidance techniques are hard to be applied on the small-size Unmanned Aerial Vehicles (UAVs) that have limited sensing and computation capacity. Therefore, it is necessary to develop an obstacle recognition and avoidance scheme which works under resource-constrained environments. To this backdrop, this paper first presents an obstacle recognition model based on monocular vision feature points. Afterwards, an obstacle recognition algorithm is put forward, whose computational complexity is low. Then, an obstacle avoidance method is proposed to regulate the obstacle-avoidance path of a UAV until it arrives its destination. To evaluate the effectiveness of the presented algorithms, we design and implement a simulation platform on Objective Modular Network Testbed in C++ (OMNeT++), and conduct a series of experiments. Experimental results show that the proposed model and algorithms can effectively guide a micro UAV to its destination using only an embedded processor and 0.5kg extra load. Even in poor communication conditions, the UAV can independently avoid obstacles and reach the destination only by acquiring the destination coordinates from the ground station.
Highlights
Unmanned Aerial Vehicles (UAVs) have been widely used in many mission-critical applications, such as reconnaissance [1], search and rescue [2], land and resources monitoring [3], bridge crack detection [4], and computation enhancement in Internet of Things scenarios [5], due to their inherent advantages including flexibility, low-cost, and mobility
Several obstacles are placed on the planned route of a UAV; second, when the UAV flies along the route, the obstacle are detected using DOMD algorithm, and the route is re-planned based on DOMD and Planning Lane based on DOMD (PLDOMD) algorithms until the UAV arrives at its destination
Based on a low-cost monocular camera and a small-sized laser range finder, this paper proposed an obstacle recognition algorithm and an obstacle avoidance method for resource-constrained UAVs
Summary
Unmanned Aerial Vehicles (UAVs) have been widely used in many mission-critical applications, such as reconnaissance [1], search and rescue [2], land and resources monitoring [3], bridge crack detection [4], and computation enhancement in Internet of Things scenarios [5], due to their inherent advantages including flexibility, low-cost, and mobility. Flight control is used to control and adjust a UAV’s flight speed and direction based on its planned obstacle avoidance path All these functions should be implemented under the limited load-carrying capability and power supply severely of the UAVs. Different types of sensors, including optical, infrared, ultrasonic, millimeter wave, laser, and etc., can be used to detect obstacles. Combining the actual size of the obstacles obtained by DOMD algorithm and the constraints of the UAV’s own flight conditions, our method can quickly calculate the shortest obstacle avoidance path of the UAV to bypass the obstacle and reach the destination. It has unique advantages, including low cost and light weight, and can better adapt to resource-constrained UAVs. its computational complexity is low VOLUME 8, 2020 and can be supported by the UAV’s on-board processor.
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