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.

Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However, real-life applications of this SNN are currently still limited by the fact that the exact choice of neuron parameters depends on the spatiotemporal properties of the input. Furthermore, tuning the network is a challenging task due to the high degree of coupling between the various parameters. Inspired by neurons in biological brains that modify their intrinsic parameters through a process called intrinsic plasticity, this research proposes update rules which adapt the voltage threshold and maximum synaptic delay during inference. This allows applying the already trained network to a wider range of operating conditions and simplifies the tuning process. Starting with a detailed parameter analysis, primary functions and undesired side effects are assigned to each parameter. The update rules are then designed in such a way as to eliminate these side effects. Unlike existing update rules for the voltage threshold, this work does not attempt to keep the firing activity of output neurons within a specific range, but instead aims to adjust the threshold such that only the correct output maps spike. In particular, the voltage threshold is adapted such that output spikes occur in no more than two maps per retinotopic location. The maximum synaptic delay is adapted such that the resulting apparent pixel velocities of the input match those of the data used during training. A sensitivity analysis is presented which illustrates the effects of newly introduced parameters on the network performance. Furthermore, the adapted network is tested on real event data recorded onboard a drone avoiding obstacles. Due to the difficulties in matching the output of the adapted SNN to the ground truth data, quantitative results are inconclusive. However, qualitative results show a clear improvement in both the density and correctness of optic flow estimates.