Recent progress in multiple micro aerial vehicle (MAV) systems has demonstrated autonomous navigation in static environments. Yet, there are limited works regarding the autonomous navigation of multiple MAVs in dynamic and unknown environments. The challenge arises from the complexity of the motion planning problem, which requires the MAVs to coordinate with each other while avoiding dynamic obstacles. This thesis presents a novel risk-aware decentralized multi-agent motion planning framework to address this issue. For perception, we rely on a particle-based dynamic map, which utilizes particles to represent dynamic obstacles and predict future states of dynamic obstacles. Leveraging this map representation and the shared trajectory from other agents, we evaluate the future collision risk with dynamic obstacles and other agents in a coupled manner. During the planning phase, a risk-aware kino-dynamic A* algorithm tailored to the particle-based map representation is developed, ensuring dynamically feasible paths with risks under a given safe level. Subsequently, spatio-temporal safety corridors with maximum volume are optimized by inflating from path segments, taking map particles as constraints. These corridors act as constraints for the trajectory optimization problem, which is simplified to a convex optimization problem by using Bézier splines. The proposed method is thoroughly evaluated in simulation environments featuring various quantities and shapes of dynamic obstacles. Comparative results with state-of-the-art multi-agent planners that rely on precise obstacle observations demonstrate the efficiency and safety of the proposed method. Furthermore, the effectiveness of the proposed method is validated in a more realistic simulation environment with pedestrians, using depth cameras onboard for perception.

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.