These days, people see more and more applications for drones, including monitoring rainforests to protect plant and animal species. However, drones face challenges when navigating through the dense and cluttered vegetation of the forest. These environments necessitate advanced autonomous detection and navigation to make the drone traverse robustly and fly safely. In addition, the forest brings extra challenges, such as blocked signals for GPS localisation, remote control, and remote supervising.

In this thesis project, a drone is designed, built, and programmed to navigate autonomously in the rainforest with complete onboard computing and no GPS localisation. This 500-gram drone is being extensively tested and optimized in real forest conditions, and a dataset is being created from its autonomous flights to simulate various configurations of the path-planning algorithm. The results of these simulations on this dataset are then used for thorough research on how the algorithm can downscale to smaller systems and how this affects performance.

By using the results of this research on downscaling, a 100-gram drone is built and programmed to fly in forest conditions with complete onboard computation. Challenging on this small-size drone is the use of low-quality lightweight sensors and processor. The processor only weighs 10 grams, and the depth camera weighs 8 grams. Unique on this small drone is the 3D path planning fully computed onboard and the implementation of a new type of depth camera.

Dynamic obstacle avoidance remains a crucial research area for autonomous systems, such as Micro Aerial Vehicles (MAVs) and service robots.
Efforts to develop dynamic collision avoidance techniques in unknown environments have proliferated in recent years. While these methods exhibit impressive and reliable performance in simpler environments, their efficacy in more challenging settings remains an area ripe for enhancement. The difficulty of these environments arises from a multitude of factors, and currently, no standardized approach exists to quantify this complexity. Additionally, to fairly compare different dynamic collision avoidance strategies, it's essential to assess them in environments with a similar degree of difficulty. Therefore, devising a metric capable of accurately gauging the intricacy of dynamic environments becomes imperative.


Building on this context, this master's thesis endeavors to fill this critical gap through three contributions: 1) The establishment and validation of map difficulty metrics that represent the difficulty of dynamic environments, 2) The introduction of a robust benchmarking pipeline to critically validate the representativeness of the proposed metrics and evaluate various collision avoidance strategies, and 3) The provision of a framework for comparative analysis of different planning strategies, utilizing the introduced map difficulty metric.

The proposed survivability metric effectively captures environmental complexity. Its validity is evidenced by a notable correlation with the success rates of typical collision avoidance methods, with over 1.7 million collision avoidance trials on over six hundred maps, securing a Spearman's Rank correlation coefficient (SRCC) of over 0.9. This metric serves as an indispensable tool for facilitating fair comparisons in this dynamic research domain. More importantly, it offers valuable insights for the future refinement and improvement of dynamic collision avoidance strategies, making a contribution to the continuous advancement of autonomous systems.