Abstract
Autonomous navigation of micro aerial vehicles in unknown environments not only requires exploring their time-varying surroundings, but also ensuring the complete safety of flights at all times. The current research addresses estimation of the potential exploration value neglect of safety issues, especially in situations with a cluttered environment and no prior knowledge. To address this issue, we propose a vision object-oriented autonomous navigation method for environment exploration, which develops a B-spline function-based local trajectory re-planning algorithm by extracting spatial-structure information and selecting temporary target points. The proposed method is evaluated in a variety of cluttered environments, such as forests, building areas, and mines. The experimental results show that the proposed autonomous navigation system can effectively complete the global trajectory, during which an appropriate safe distance could always be maintained from multiple obstacles in the environment.
Highlights
Due to their good flexibility, strong maneuverability, easy operation, and unique viewpoint, micro aerial vehicles (MAVs) are widely used in aerial photography, searchand-rescue missions [1], delivery of goods, and mine exploration [2], etc
Our path planning method is an extension of intermediate goal strategy [3], which belongs to the sampling method
We provided six kinds of simulation maps to test the performance of the planning algorithm and autonomous navigation system
Summary
Due to their good flexibility, strong maneuverability, easy operation, and unique viewpoint, micro aerial vehicles (MAVs) are widely used in aerial photography, searchand-rescue missions [1], delivery of goods, and mine exploration [2], etc. In search-and-rescue missions, an autonomous navigation system can autonomously control the MAV to avoid obstacles in real time and provide image information with a unique perspective for rescue operations. The research of planning based on target searches in unknown environments mainly deals with two problems: the first is obstacle avoidance, while the second is solving the problem of getting stuck at local minima. This poses a special problem in unexplored or partially unexplored environments, where only locally optimal or reactive planners will frequently fail to find a path [3]. The planning policy should gradually complete the task through exploration, and have the capability of guaranteeing the safety of the MAV in unknown surroundings
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