Abstract
Small Unmanned Aircraft Systems (UAS) have diverse commercial applications. Risk mitigation techniques must be developed to minimize the probability of harm to persons and property in the vicinity of the aircraft. This paper presents an emergency flight planner combining sensor-based and map-based elements to collectively plan a landing path for a UAS that experiences an unexpected low energy condition while flying over a populated area. Focus is placed in this work on the use of public databases of population distribution, structure locations, and terrain to create an efficient-to-access cost map of the data. Safe landing plans are generated with an A* search algorithm shown to be feasible for real-time use with the cost map. Simulation-based case studies are presented of a quadrotor UAS operating within New York City to illustrate how different cost terms impact optimal path characteristics.
Highlights
Small Unmanned Aircraft Systems (UAS) are expected to experience a period of significant growth as regulatory policy supports them in the coming years [2, 12, 19, 25]
It is a combination of a curve-fit energy equation based on discharge curves of lithium polymer batteries and experimental data taken from the Michigan Autonomous Aerial Vehicles (MAAV) team, a UAS student team at the University of Michigan1, and a simple equation validated with benchtop testing that translates motor force into approximate current draw from the onboard battery
This paper has presented a new method for emergency landing planning when a UAS unexpectedly encounters a low-energy situation
Summary
Small Unmanned Aircraft Systems (UAS) are expected to experience a period of significant growth as regulatory policy supports them in the coming years [2, 12, 19, 25]. A meta-level emergency landing planner, shown, is proposed to calculate a safe path for a small UAS that senses unexpectedly low energy reserves while flying over or within a populated urban environment. The map-based planner uses a pre-computed cost map to determine the safest transit to a landing site area that may be beyond sensor range or visibility. Landing due to imminent motor shutdown may occur in an unsafe area In this case, the sensor-based planner, using one of a variety of techniques [1, 11, 23, 24], is responsible for executing the lowest-risk landing plan. From this data, a cost map generation module chooses optimal landing sites offline, based on the assigned cost of locations on the ground, as well as some key building rooftops. This paper is organized as follows: Section 2 reviews related work; Section 3 gives an overview of the vehicle model; Section 4 describes the environment model; Section 5 describes planner cost and constraint terms; Section 6 details the flight path planning algorithm; Section 7 presents case studies for a quadrotor operating in Manhattan; Section 8 gives conclusions and considerations for future work
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