Baseball Avoidance Multirotor Simulation

Autonomous collision avoidance (ACA) for aerospace systems is an area of active study within the autonomy research community. This problem consists of three consecutive subtasks: perception, prediction, and planning. Each of these sub-tasks are large, active fields of research on their own. For this reason, this team developed an open-source simulation framework that enables the autonomy community to target ACA using a multirotor vehicle which must avoid moving baseballs. The Baseball Avoidance Multirotor (BAM) simulation framework uses MATLAB and Simulink with a multirotor dynamics model in combination with the high-fidelity photo-realistic and sensor simulation capabilities of Microsoft AirSim or Colosseum with Unreal Engine. The multirotor was selected as the aerospace vehicle due to its simple dynamics and control methodologies that are accessible, high precision, and well known within academia and industry. These features allow researchers to focus on one or more ACA subtasks without managing the more challenging aspects of vehicle control. This paper presents the BAM simulation and details a series of collision avoidance challenges provided with accompanying datasets. Results from BAM simulation experiments demonstrate collision avoidance with both an algorithm that combines Optimal Reciprocal Collision Avoidance (ORCA) with Bernstein polynomials, and a transformer-based replanning method.