Publications by Author: Bardienus P. Duisterhof

Fully autonomous navigation using nano drones has numerous applications in the real world, ranging from search and rescue to source seeking. Nano drones are wellsuited for source seeking because of their agility, low price, and ubiquitous character. Unfortunately, their constrained form factor limits flight time, sensor payload, and compute capability. These challenges are a crucial limitation for the use of source-seeking nano drones in GPS-denied and highly cluttered environments. Hereby, we introduce a fully autonomous deep reinforcement learning-based light-seeking nano drone. The 33-gram nano drone performs all computation on-board the ultra-low-power microcontroller (MCU). We present the method for efficiently training, converting, and utilizing deep reinforcement learning policies. Our training methodology and novel quantization scheme allow fitting the trained policy in 3 kB of memory. The quantization scheme uses representative input data and input scaling to arrive at a full 8-bit model. Finally, we evaluate the approach in simulation and flight tests using a Bitcraze CrazyFlie, achieving 80% success rate on average in a highly cluttered and randomized test environment. Even more, the drone finds the light source in 29% fewer steps compared to a baseline simulation (obstacle avoidance without source information). To our knowledge, this is the first deep reinforcement learning method that enables source seeking within a highly constrained nano drone demonstrating robust flight behavior. Our general methodology is suitable for any (source seeking) highly constrained platform using deep reinforcement learning. Code & video: https://github. com/harvard-edge/source-seeking

Nano quadcopters are ideal for gas source localization (GSL) as they are safe, agile and inexpensive. However, their extremely restricted sensors and computational resources make GSL a daunting challenge. We propose a novel bug algorithm named ‘Sniffy Bug’, which allows a fully autonomous swarm of gas-seeking nano quadcopters to localize a gas source in unknown, cluttered, and GPS-denied environments. The computationally efficient, mapless algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are first set for exploration, and, when a single swarm member has sensed the gas, by a particle swarm optimization-based (PSO) procedure. We evolve all the parameters of the bug (and PSO) algorithm using our novel simulation pipeline, ‘AutoGDM’. It builds on and expands open source tools in order to enable fully automated end-to-end environment generation and gas dispersion modeling, allowing for learning in simulation. Flight tests show that Sniffy Bug with evolved parameters outperforms manually selected parameters in cluttered, real-world environments. Videos: https://bit.ly/37MmtdL