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This article investigates the trajectory tracking control problem for wheeled mobile robot (WMR) subject to both velocity and torque constraints. Firstly, a more general model is established by considering the displacement between the centre of mass and the midpoint of the driving wheels of the WMR, based on which, a new bounded kinematic controller is proposed by combining the first-order filtering approach to keep the trajectory tracking error asymptotically stable. Then the dynamic controller is designed and the stability of the overall closed-loop system is proved by the Lyapunov stability theory and Barbalat's lemma. Some conditions are given to ensure that both the velocity constraints and torque constraints are satisfied. Finally, some numerical examples are given to show the effectiveness and advantages of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

[Objectives] An energy management optimization strategy is proposed which considers the limitation of a ship's power system operation and greenhouse gas(GHG) emissions.[Methods] This strategy uses the differential evolution algorithm and real number code to optimize the dispatching speed of the ship,start and stop state of the generators and power distribution of the generator set with minimum consumption as the goal. These three plans can ensure compliance with environmental limits while reducing fuel consumption and ensuring efficiency.[Results] Taking the actual measured data of a cruise ferry as an example,the operating cost of the initial plan(plan 1)is 36 960.5 m.u while the operating costs of plan 2 and plan 3 are 36 938.1 m.u and 35 888.3 m.u respectively,showing a decrease of 0.06% and 2.90% when compared with the initial plan.[Conclusions] On the precondition that the optimal design method meets the limitations of the ship's generator,diesel engine operation,GHG limit and arrival distance,it can be seen that the ship's speed and propulsion curve become relatively stable,the figure tends to be trapezoidal and the efficiency of the ship's power system and fuel economy are significantly improved,while the amount of greenhouse gas emissions is simultaneously reduced.

Continuous implicit representations can flexibly describe complex 3D geometry and offer excellent potential for 3D point cloud analysis. However, it remains challenging for existing point-based deep learning architectures to leverage the implicit representations due to the discrepancy in data structures between implicit fields and point clouds. In this work, we propose a new point cloud representation by integrating the 3D Cartesian coordinates with the intrinsic geometric information encapsulated in its implicit field. Specifically, we parameterize the continuous unsigned distance field around each point into a low-dimensional feature vector that captures the local geometry. Then we concatenate the 3D Cartesian coordinates of each point with its encoded implicit feature vector as the network input. The proposed method can be plugged into an existing network architecture as a module without trainable weights. We also introduce a novel local canonicalization approach to ensure the transformation-invariance of encoded implicit features. With its local mechanism, our implicit feature encoding module can be applied to not only point clouds of single objects but also those of complex real-world scenes. We have validated the effectiveness of our approach using five well-known point-based deep networks (i.e., PointNet, SuperPoint Graph, RandLA-Net, CurveNet, and Point Structuring Net) on object-level classification and scene-level semantic segmentation tasks. Extensive experiments on both synthetic and real-world datasets have demonstrated the effectiveness of the proposed point representation. [ABSTRACT FROM AUTHOR]