Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.
Proactive caching at the base station (BS) is a promising way to leverage the user-behaviorrelated information to boost network throughput and improve user experience. However, the gain of caching at the mobile edge highly depends on random user behavior and is largely compromised by the uncertainty in predicting behavior-related information. First, the local file popularity in each cell may not be skewed. Second, the local file popularity varies quickly due to user mobility even if the lifetime of each file is long. Furthermore, considering the small population of users that initiate requests in each cell, the local popularity in the next cache update period is not easy to predict accurately, because users may not request their interested files in this period, despite that the popularity can be indirectly obtained by predicting the mobility and preference of each individual user in a cell. To address such issue, in this paper, we integrate recommendation with caching at BS, aiming at improving cache efficiency whereas not violating user preference. In particular, we propose a temporal-spatial recommendation policy, which can guide mobile users to request their preferred files in proper time and place, so as to make local popularity peakier. We do not assume that the user preference, the impact of the recommendation on request probability, and the mobility pattern are known. Hence, we resort to deep reinforcement learning to optimize recommendation and caching policy. To deal with the difficulty in predicting local popularity in the next cache replacement period, we model the user preference and request probability with Bernoulli mixture distribution and hence can estimate them separately. The simulation results demonstrate that the proposed policy can reduce the cache miss number, compared to the policies without any recommendation and without temporal-spatial recommendation. INDEX TERMS Caching, recommendation, user preference, user mobility.
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