Search

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs). [ABSTRACT FROM AUTHOR]

The article various reports published within the issue, including one on the challenges, design principles, and technicalities of industrial wireless sensor networks, on the development of optimization of communication for ad hoc reliable industrial networks, and on the pedestrian localization algorithms based on distributed wireless sensor networks.

The intermittent integration of renewable energy sources and the enhancement of energy-saving awareness on the demand side have posed significant challenges to energy management in smart grids. To address the supply–demand imbalance issues caused by these challenges, this paper proposes a novel multiple time-scale dispatch model. The model utilizes demand response strategy for demand side management (DSM) and employs dynamic pricing strategy for supply-side management. Furthermore, an advanced approach based on federated learning and Twin Delayed Deep Deterministic(TD3) policy gradient algorithm is proposed to construct a federated learning mechanism among multi agents. This approach performs distributed optimization on the multi-agent system to derive the optimal network weights for multiple stakeholders, resulting in the generation of an optimal dynamic pricing strategy that ensures the economic efficiency and secure operation of the smart grid. From the methodological perspective, this approach enhances privacy protection and decision-making autonomy for each stakeholder, while improving the optimization efficiency of the reinforcement learning (RL) algorithm. Based on simulation results, it is evident that the proposed method facilitates economically efficient transactions between supply and demand, and the improved distributed multi-agent federated reinforcement learning (MAFRL) approach holds promise as a feasible and optimal optimization method for economic dispatch in smart grid. [ABSTRACT FROM AUTHOR]