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Amazon's Prime Air drone. The "wingspan" diameter is approximately 2 m. (Image used with permission from Amazon.com, Inc.).
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It is anticipated that future skies over urban areas will be busy with drones flying back and forth delivering packages. Taking New York City as an extreme example, it is estimated that by 2026, 2600 delivery drones could simultaneously populate the city's airspace. The drone–drone collision rate of “dumb” drones can be calculated by treating them...
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... Equipping each drone with a "sense and avoid" capability to detect an imminent collision and then swerve to avoid it. Amazon's Prime Air delivery drone 4,9 (see Fig. 1) utilizes this strategy. (2) Creating an Unmanned Aircraft System Traffic Management (UTM) system to ensure a safe separation between drones, using intelligence resident outside the drones themselves. A UTM is currently under development by the FAA and NASA, working in collaboration with other federal agencies and industry, but will ...
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... Fig. 2 shows an example mission with a 12km start to goal distance. This is based off the max round trip distance (24km) for the Amazon Prime Air delivery drone (2019 model) [8]. The example mission shown in Fig. 2 has a cloud server and 3 edge servers (at basestations 4km apart) available for computation. ...
As autonomous Unmanned Aerial Vehicles (UAVs) are becoming more and more prevalent in everyday life, it is paramount that UAVs are equipped with effective obstacle avoidance capabilities. Edge AI, which runs AI on-device (e.g., on-UAV) or on edge servers, offers many advantages to traditional cloud-based AI when applied to the problem of UAV obstacle avoidance. Literature shows that deep reinforcement learning (DRL) applied to robots (e.g., UAVs) is an effective method of obstacle avoidance. One key issue associated with DRL applied to robotics is the time required to train when the environment is complicated. In this paper, we propose a DRL-based UAV obstacle avoidance system that leverages edge AI. Our system distributes the training and inferencing processes of DRL by splitting large environments into multiple smaller environments. Our main goal is to make DRL training faster and more feasible under relatively large and complex environments. We demonstrate the effectiveness of our system in 3D simulation and all our code is open-sourced on GitHub.
