High-Speed Obstacle Avoidance in Unstructured Environments

Abstract

Micro aerial vehicles (MAVs) have many applications in various environments, some of which include site surveying, mapping, search and rescue, inspection, delivery, and photography. Even though MAVs have an incredible amount of application space, most industry applications do not make use of autonomous flight. Instead, a human pilot flies the MAV, especially where navigation in complex environments is required. While autonomous navigation systems do exist in industry, the complexity of the environment can inhibit the system’s ability to traverse the environment. High-speed autonomous navigation of MAVs, in unstructured environments has received more attention in recent years, which has resulted in new more robust algorithms which can navigate complex unstructured environments quickly and successfully. In this research report, a simulation environment is created in a photo-realistic simulation engine in which high-speed obstacle avoidance algorithms can be evaluated. This is done so that an assessment can be made regarding how well these algorithms generalise as the complexity of the environment increases. Obstacle avoidance research often overlooks the degree of environmental complexity when presenting performance metrics. As a result, the performance metrics for obstacle avoidance algorithms can be vague due to the lack of detailed metrics that adequately capture environmental complexity. To this end, an environmental complexity scoring framework for unstructured environments is proposed. Environments are divided into ten levels which increase in environmental complexity score. The experiment allows the MAV to move through the simulation environment at various target speeds. As the MAV moves through each level, evaluation metrics such as average speed and collisions are logged. Evaluation of these metrics indicates that if the environmental complexity is constant, speed increases cause collision avoidance performance to degrade. Conversely, as the environmental complexity increases, lower speeds are required for successful collision avoidance. It is further shown that object complexity has an impact on environmental complexity and subsequently successful obstacle avoidance. The greater the object complexity the more obstacle avoidance performance is reduced. The project repository can be found here. Results indicate that obstacle avoidance algorithms have maximum speed and environmental complexity threshold scores, below which successful obstacle avoidance can occur.