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Aiming at the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, Deep Deterministic Policy Gradient (DDPG) and vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to equivalent virtual abstract scene by transfer model, furthermore, the control action and trajectory sequences are calculated according to trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model generalization performance. Compared with the traditional trajectory planning, the proposed method output continuous rotation angle control sequence, meanwhile, the lateral control error is also reduced.

The fusion of the heterogeneous sensors can greatly improve the environmental perception ability of mobile robots. And that the primary difficulty of heterogeneous sensors fusion is the calibration of depth scan information and plane image information for a laser rangefinder and a camera. Firstly, a coordinate transformation method from a laser rangefinder coordinates system to an optical image plane is given, and then the calibration of the camera's intrinsic parameters is achieved by “Camera Calibration Toolbox‘. Secondly, the intrinsic and extrinsic parameters are separated for calibration are proposed and compared, in which the characteristic parameters' identification is according to some characteristic points on the intersection line. Then Gaussian elimination is utilized for the initial value. Furthermore, the parameters' optimization using the non-linear least square and non-linear Gauss-Newton methods is devised for different constraints. Finally, the simulated and real experimental results demonstrate the reliability and effectiveness of extrinsic and intrinsic parameters' separated calibration, meanwhile, the real-time analysis is achieved for robotic multi-sensor fusion.

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Summary Distributed generation (DG) has attracted significant attention due to its great potential for enhancing economical and technical performance of power systems and reducing dependence on fossil fuels. Optimal sizing and placement are critical for stimulating such potential, about which a considerable number of models and algorithms have been proposed in past literature. This paper attempts to undertake a comprehensive review on optimal sizing and placement of DG via a systematic methodology procedure, including definition and classifications of DG, modelling and problem formulation with different technical and economic criteria, and summary of optimization algorithms. Common features and distinctive characteristics of both models and methods are identified, followed by evaluations and comparisons based on their practical performance in various test systems. Selection of DG techniques with respect to application scenarios, indispensable and optional considerations in DG planning models, and pros and cons of algorithms are listed in tables for a clearer understanding. Lastly, a total of 107 algorithms are addressed, which are classified into five categories. Particular, hybrid methods can deal with complex engineering problems with multiple objective functions and constraints most effectively and robustly. Future research trends are also highlighted with the aim of providing a comprehensive and state‐of‐the‐art survey for researchers, engineers, and other stakeholders.