Small and Miniature Air Vehicles (MAVs) have the potential to perform tasks that are too difficult or dangerous for human pilots. For example, they can monitor critical infrastructure and real-time disasters, perform search and rescue, and measure weather in-storms. For many of these applications, MAVs are required to navigate in urban or unknown terrain where obstacles of various types and sizes may hinder the success of the mission. MAVs must have the capability to autonomously plan paths that do not collide with buildings, trees or other obstacles. Therefore, the path planning and obstacle avoidance problems for MAVs have received significant attention.
Observability-based Local Path Planning and Collision Avoidance Using Bearing-only Measurements
2012
34 pages
Report
Keine Angabe
Englisch
Sampling-Based Path Planning for UAV Collision Avoidance
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