The paper presents a receding horizon planning and control strategy for quadrotor-type micro aerial vehicle (mav)s to navigate reactively and intercept a moving target in a cluttered unknown and dynamic environment. Leveraging a lightweight short-range sensor that generates a point-cloud within a relatively narrow and short field of view (fov), and an ssd-MobileNet based Deep neural network running on board the mav, the proposed motion planning and control strategy produces safe and dynamically feasible mav trajectories within the sensor fov, which the vehicle uses to autonomously navigate, pursue, and intercept its moving target. This task is completed without reliance on a global planner or prior information about the environment or the moving target. The effectiveness of the reported planner is demonstrated numerically and experimentally in cluttered indoor and outdoor environments featuring maximum speeds of up to 4.5–5 m/s.
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