【摘 要】
：
Robots need more intelligence to complete cognitive tasks in home environments. In this paper, we present a new cloud-assisted cognition adaptation mechanism for home service robots, which learns new knowledge from other robots. In this mechanism, a chang
【机 构】
：
College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;School of Electrical and
论文部分内容阅读
Robots need more intelligence to complete cognitive tasks in home environments. In this paper, we present a new cloud-assisted cognition adaptation mechanism for home service robots, which learns new knowledge from other robots. In this mechanism, a change detection approach is implemented in the robot to detect changes in the user\'s home environment and trigger the adaptation procedure that adapts the robot\'s local customized model to the environmental changes, while the adaptation is achieved by transferring knowledge from the global cloud model to the local model through model fusion. First, three different model fusion methods are proposed to carry out the adaptation procedure, and two key factors of the fusion methods are emphasized. Second, the most suitable model fusion method and its settings for the cloud–robot knowledge transfer are determined. Third, we carry out a case study of learning in a changing home environment, and the experimental results verify the efficiency and effectiveness of our solutions. The experimental results lead us to propose an empirical guideline of model fusion for the cloud–robot knowledge transfer.
其他文献
The optical rotation technique arose in the 1990s. Optical tweezer brought an ideal platform for research on the angular momentum of laser beams. For decades, the optical rotation technique has been widely applied in laboratory optical manipulation and th
Active queue management (AQM) is essential to prevent the degradation of quality of service in TCP/AQM systems with round-trip time (RTT) delay. RTT delays are primarily caused by packet-propagation delays, but they can also be caused by the processing ti
A fundamental task for mobile robots is simultaneous localization and mapping (SLAM). Moreover, long-term robustness is an important property for SLAM. When vehicles or robots steer fast or steer in certain scenarios, such as low-texture environments, lon
A gallium nitride (GaN) power amplifier mono-lithic microwave integrated circuit (MMIC) with a wide band and high efficiency in the microwave fre-quency band is proposed in this study. The power am-plifier MMIC uses a 0.15 μm GaN high electron mo-bility t
In this paper, an intelligent fractional-order integral sliding mode control (FOISMC) strategy based on an improved cascade observer is proposed. First, an FOISMC strategy is designed to control a permanent magnet synchronous motor. It has good tracking p
Because it is magnet-free and can achieve a high integration level, the switched-capacitor (SC) converter acting as a direct current transformer has many promising applications in modern electronics. However, designing an SC converter with large current c
In constructing a smart court, to provide intelligent assistance for achieving more efficient, fair, and explainable trial proceedings, we propose a full-process intelligent trial system (FITS). In the proposed FITS, we introduce essential tasks for const
To avoid Doppler ambiguity, pulse Doppler radar may operate on a high pulse repetition frequency (PRF). The use of a high PRF can, however, lead to range ambiguity in many cases. At present, the major efficient solution to solve range ambiguity is based o
Deep learning has proven to be an effective mechanism for computer vision tasks, especially for image denoising and burst image denoising. In this paper, we focus on solving the burst image denoising problem and aim to generate a single clean image from a
Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems, the skewed distribution of multiclass data poses a major challenge to machine learning algorithms. To