Mission Planning for Low-Flying Unmanned Rotorcraft in Unknown Environments

Abstract

Small Uninhabited Aerial Vehicles that are to be operated at low altitude in obstacle prone environments require efficient mission planning capabilities. A fully autonomous miniature helicopter is being employed as a testbed to explore the development of new subsystems and algorithms for autonomous intelligent functions. An overview of the concepts related to mission planning and management for this research platform is presented. The underlying problem of mission planning for a helicopter differs from high-altitude systems by the operation close to the ground; among known or unknown obstacles. For the global path planning in a three-dimensional space, previously known information about the environment is used. The path planning is based on the visibility graph concept, which returns length-optimal paths in a plane. To account for the whole thee-dimensional space, this algorithm is run in several fixed-height layers, and then connected to the start and finish points, generating near-optimal paths in a very short time. Subsequently, the path planner is completed by a real-time collision avoidance planner. Upon detection of unknown, static or moving obstacles, the global path can be adapted in little time. The arithmetic techniques of this approach are further explained through examples. These mission planning components are integrated into the unmanned helicopter’s ground control station software and thus can be used to plan and monitor missions in obstructed environments. Experience from simulations and flight tests has shown, that the combination of those techniques results in a powerful, computationally efficient mission planning method that could be applied to unmanned vehicles with payload limitations.