Major delivery companies such as DHL, UPS or Amazon have been developing small drones to deliver packages. This alternative to truck delivery is expected to start operating in the near future. The advantages of it are its speed, price, safety and sustainability: parcels would not be subjected to traffic and they would be delivered within an hour, it is 10% less expensive and it means a 73% reduction in CO2 emissions when compared to truck delivery, as well as a relieve in the road traffic network. The only drawback is that the noise produced by current drones is deemed by humans as more annoying than car noise due to its high frequency. The mission of the Silent Delivery Drone project is to provide a drone delivery system that is faster, less expensive and has lower emissions than truck delivery while complying with Dutch noise regulations. The presented innovative configuration is a combination of a quadcopter, suitable for Vertically Taking-Off and Landing (VTOL) in densely populated regions, and a flying wing, optimized for the cruise phase. It consists of a horizontal propeller used during cruise and four vertical propellers for VTOL. The drone can carry a payload of up to 2.5 kg, which corresponds to 89% of the packages delivered yearly worldwide. Four packages can be delivered while flying the maximum range of 30 km. Thanks to the low required revolutions per minute, the absolute maximum noise caused by the drone is 58 dBA at take-off from 7.5 m and 25 dBA during cruise from a distance of 120 m. This meets the Dutch night noise regulations which stablish a peak noise level of 65 dBA and average noise level of 40 dBA. We believe that a fleet of Silentium drones would revolutionize the way we perceive package delivery and it would mark the next step towards a greener, smarter and more connected future.

Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challenging to compare the performance between algorithms. In this paper, we present AvoidBench, a benchmarking suite capable of evaluating the performance of vision-based obstacle avoidance algorithms for multi-rotors in simulation. Utilising a set of performance metrics, AvoidBench assigns performance scores to obstacle avoidance algorithms by subjecting them to a series of tasks. Using both Airsim and Unreal engine under the hood, we are able to provide high-fidelity visuals and dynamics, leading to a relatively small gap between simulation and reality. AvoidBench comes included with a simple, but powerful C++ and Python API which provides functionality for procedural environment generation, custom benchmark design, and an easy-to-use framework for users to implement their own vision-based obstacle avoidance methods. Implementing an obstacle avoidance method can be done entirely in a single file, allowing anyone to share and compare their obstacle detection and avoidance algorithms with others.