This paper discusses a computer vision algorithm and a control law for obstacle avoidance for small unmanned air vehicles using a video camera as the primary sensor. Small UAVs are used for low altitude surveillance flights where unknown obstacles can be encountered. Small UAVs can be given the capability to navigate in uncertain environments if obstacles are identified. This paper presents an obstacle detection methodology using feature tracking in a forward looking, onboard camera. Features are found using the Harris Corner Detector and tracked through multiple video frames which provides three dimensional localization of the salient features. A sparse three dimensional map of features provides a rough estimate of obstacle locations. The features are grouped into potentially problematic areas using agglomerative clustering. The small UAV then employs a sliding mode control law in the autopilot to avoid obstacles.
Small unmanned air vehicles are limited in sensor weight and power such that detection and avoidance of unknown obstacles during flight is difficult. This paper presents a low power low weight method of detection using a laser range finder. In addition, a rapidly-exploring random tree algorithm to generate waypoint paths around obstacles known a priori is presented, and a dynamic geometric algorithm to generate paths around detected obstacles is derived. The algorithms are demonstrated in simulation and in flight tests on a fixed-wing miniature air vehicle (MAV). Index Words-Obstacle avoidance, waypoint path planning, rapidly exploring random tree, unmanned air vehicles, miniature air vehicles.
Robotic Systems with real-time onboard vision processing capabilities are normally bulky or expensive. To process vision locally in real-time requires hardware resources capable of fast and efficient vision processing. Vision processing utilizes a lot of computational power. So onboard vision processing can present a challenge to a designer of robotic systems. In this paper we describe our design using a configurable computing environment for high-speed onboard vision processing to create a truly autonomous small scale robotics system. Using only onboard processing, the robotic system is able to navigate in an enclosed environment with vision cues. The target application in this case was robot soccer, but the concept of using configurable logic in vision systems can be extended to other applications. The functioning of the system was verified in a robot soccer competition which was held in the department. In the competition the robots successfully scored multiple goals using the vision system described in the paper. Video of the competition will be available for the conference presentation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.