This article presents a family of Stochastic Cartographic Occupancy Prediction Engines (SCOPEs) that enable mobile robots to predict the future states of complex dynamic environments. They do this by accounting for the motion of the robot itself, the motion of dynamic objects, and the geometry of static objects in the scene, and they generate a range of possible future states of the environment. These prediction algorithms are software-optimized for real-time performance for navigation in crowded dynamic scenes, achieving 10 times faster inference speed and 3 times less memory usage than the original engines. Three simulated and real-world datasets collected by different robot models are used to demonstrate that these proposed prediction algorithms are able to achieve more accurate and robust stochastic prediction performance than other algorithms. Furthermore, a series of simulation and hardware navigation experiments demonstrate that the proposed predictive uncertaintyaware navigation framework with these stochastic prediction engines is able to improve the safe navigation performance of current state-of-the-art model-and learning-based control policies.

Abstract. Because of modern air combat is fierce, the air combat situation vary from minute to minute, air combat opportunity transient, and the biggest threat to air combat comes from the tail, so the air combat both are considering how to directly attack the rear target missile, OTSL(over the shoulder launch) is a new way to meet this need and to develop. Study on OTSL plays a key role in the future war. Through the research of the fire control system of missile launching, how to expand the capture zone, implementation of gesture control requirement, guidance rate, the trajectory optimization problem of missile horizontal turning, It enables us to have a further understanding to the OTSL.