利用整体法巧解牛顿第二定律

隔离法适用范围广,但有时解题比较繁琐,整体法适用范围小,只适用于求解系统内各物体保持相对静止或匀速直线运动的情况,也适用于求解相对作匀变速直线运动的情况。     

质点匀速匀变速直线运动在直角坐标系中的数学描述

本文提出了一种运用矢量在直角坐标系中直接描述质点的匀速、匀变速直线运动的方法并讨论了其可行性。     

在空气中作匀速直线运动的薄圆盘受到的曳力

主要应用麦克斯韦速率分布函数和动量定理,近似推导出薄圆盘在空气中作匀速直线运动时受到的曳力。     

在空气中作匀速直线运动的薄圆盘受到的曳力

主要应用麦克斯韦速率分布函数和动量定理,近似推导出薄圆盘在空气中作匀速直线运动时受到的曳力。     

完备的狭义相对论③(群论分析及对v的物理依附性)

完备的狭义相对论(Ⅰ)提出了(亚光速)v-(超光速)二重匀速直线运动下的(特殊)洛伦兹变换可分别表达为v下洛伦兹变换     

巧解一个连接体问题

正连接体问题是力学中学生能力考查的重要内容。在学习斜面-滑块组成的连接体问题时,我发现了一个有趣的结论,下面通过具体问题进行阐述。一、问题的提出问题1.如图1所示,木楔M置于粗糙的水平地面上,一物块以某一初速度沿斜面滑下时,物块恰好保持匀速直线运动,整个过程中     

铜管中磁铁运动特性分析

研究铜管中磁铁下落的运动特性,通过实验证明传统观念认为的,磁铁达到稳定状态后将做匀速直线运动的结论是片面的,仅限于感应磁力较小的情况。当感应磁力足够大时,轨迹是曲线,这是由于磁铁不仅受到感应磁力,还会受到感应磁力矩的旋转作用。     

“牛顿的长期误导应该终止”——读“挑战与争鸣”栏目文章有感

首先，让我们来重温牛顿第一运动定律：物体如不受到外力的作用，它将保持原有的静止或匀速直线运动状态。在《发明与创新》2003年7月号的《相对论“马失前蹄”在惯性原理》以下简称《相》文)一文中，作者以断了电的电扇、停止抽打的陀螺等物的转动为例，得出“失去动力继续转动的物体不受外力(重力和摩擦力不计)作用”的结论，即“惯性运动不必是直线运动”且是“毋庸置疑”的。     

匀速运动模糊图像恢复的离散化模型研究

图像恢复是图像处理领域的热点问题之一。利用导致图像退化的先验知识,沿着图像退化逆过程进行恢复。根据运动模糊图像的成像原理,建立了运动模糊图像的退化模型,结合图像复原的基本数学模型,运用积分变换以及微分数值化方法,建立了匀速运动模糊图像恢复的离散化过程,最后推导给出了匀速运动模糊图像恢复的离散化数学模型。该模型只利用了产生模糊的相关参数以及能够获取的退化图像信息,结果简单直接,对于后续相关算法设计提供了理论上的支撑。     

"牛顿的长期误导应该终止"吗--读"挑战与争鸣"栏目文章有感

首先,让我们来重温牛顿第一运动定律:物体如不受到外力的作用,它将保持原有的静止或匀速直线运动状态。在《发明与创新》2003年7月号的《相对论“马失前蹄”在惯性原理》以下简称《相》文)一文中,作者以断了电的电扇、停止抽打的陀螺等物的转动为例,得出“失去动力继续转动的物     

Distributed formation for robotic swarms considering their crossing motion

This paper discusses formation problem for robotic swarms when multiple robotic swarms cross one another's path. To realize the crossing motion, collision avoidance between agents is an important issue, with the potential to cause a general mix-up of formation during the crossing motion. To realize an orderly and well-organized crossing motion with the least mix-up, as well as collision avoidance, we propose a distributed controller. This well-organized crossing motion can realize visually appealing and highly entertaining robotic mass games. This paper proposes a distributed controller using the gradient of the cost functions about the formation maintenance, collision avoidance, and tracking to the desired trajectory. We then prove that we can achieve a well-organized crossing motion of multiple robotic swarms under several assumptions. Finally, experimental and numerical simulations are carried out to investigate whether the well-organized crossing motion can be achieved.     

一种用于图像序列斑点去除的运动估计修正方法

在数字视频处理中经常需要考虑斑点去除问题。运动矢量估计是斑点检测和去除的基本步骤。在斑点存在的区域，运动向量的估计可能会出现较大的偏差。大多数算法只通过空间上运动矢量的相关性来进行修正。本文根据空间上和时间上图像灰度和运动矢量相关的特点，并考虑块分级之间运动矢量的相关性，提出了一种运动估计的修正方法。仿真结果显示，本文的方法是有效的。     

A design of fine motion assist equipment for disabled hand in robotic rehabilitation system

This paper reports a newly designed system intended to aid in hand rehabilitation. The motion assistance equipment consists of three parts: mechanisms for the fingers and thumb, a base of these mechanisms, and a motion assistance mechanism for the wrist. The structure of each mechanism is designed to achieve independent, fine motion assistance, especially, for the individual fingers. First, the features of each mechanism in the equipment are explained. Next, the control systems are introduced, which are constructed to realize a self-motion control strategy (i.e., the motion is controlled by its user). Using this control system, the transient response and steady state characteristics of the motion assistance mechanisms for the thumb are evaluated. Consequently, the possibility of practical application is found in regard to some improved points.     

运动补偿图像编码中的并行三步算法

本文提出了一种计算运动图像位移矢量Ｄ的新算法。这种新算法称为并行三步算法，模拟结果表明：这种新算法比现有的同类算法有更好的性能。     

Application of graph theory to the determination of kinetic energy of rigid body systems

The process of determining the kinetic energy as a function of generalized coordinates and velocities of rigid body systems is often extremely laborious. In this paper a method is presented by means of which all the kinetic energy terms of a system possessing a tree structure are calculated in a systematic manner. The paper contains the derivation of a general formula for kinetic energy which is then exemplified in the case of planar motion. A particular example of the six degree-of-freedom rigid body system in planar motion is also provided.     

Layer,Lie algebraic method of motion planning for nonholonomic systems

In this paper a layer, Lie algebraic method of motion planning for nonholonomic systems is presented. It plans locally a motion towards a goal by searching for optimal directions in equi-cost spaces. The spaces are easy to determine via exploiting Lie algebraic properties of vector fields that define the controlled system. The method was illustrated on the unicycle robot and the inverted pendulum.     

Determination method for power-saved driving motions of manipulators by heuristic algorithms (in case of PTP control)

This study proposed a method for suppressing torques and consumed energy of manipulators considering an effect of inertial forces in order to achieve power-saved drive. The present paper describes the case that the start and end points of a work path of a manipulator are specified and the path between the points can be determined arbitrarily, which is so-called point-to-point (PTP) control. The proposed method determines both the initial pose of a manipulator and input motions of its joints so that the torque or consumed energy is suppressed. The input motion of the joint is expressed by a motion curve that indicates the time profiles of input displacements, velocities and accelerations. The proposed method determines the motion curve as a polynomial with appropriate coefficients. Those coefficients and their values are decided by a heuristic algorithm in order to suppress the input torques or consumed energy of a manipulator. The paper actually determines the power-saved drive motion of “PUMA560” by the proposed method and reveals its effectiveness.     

MEMS陀螺仪在检测小车运动状态中的应用

研究了MEMS陀螺仪在检测小车运动状态的应用,利用陀螺仪等组成的运动状态检测系统采集陀螺仪实时传回的数据,快速、可靠地对小车运动状态进行检测,实时、准确地检测出自动小车的偏转角度.从而实现自动小车稳定、快速地控制.     

A general motion control framework for an autonomous underwater vehicle through deep reinforcement learning and disturbance observers

This paper investigates the application of deep reinforcement learning (RL) in the motion control for an autonomous underwater vehicle (AUV), and proposes a novel general motion control framework which separates training and deployment. Firstly, the state space, action space, and reward function are customized under the condition of ensuring generality for various motion control tasks. Next, in order to efficiently learn the optimal motion control policy in the case that the AUV model is imprecise and there are unknown external disturbances, a virtual AUV model composed of the known and determined items of an actual AUV is put forward and a simulation training method is developed on this basis. Then, in the given deployment method, three independent extended state observers (ESOs) are designed to deal with the unknown items in different directions, and the final controller is obtained by compensating the estimated value of ESOs into the output of the optimal motion control policy obtained through simulation training. Finally, soft actor-critic is chosen as deep RL algorithm of the framework, and the generality and effectiveness of the proposed method are verified in four different AUV motion control tasks.     

Model-based deep reinforcement learning for data-driven motion control of an under-actuated unmanned surface vehicle: Path following and trajectory tracking

Unmanned surface vehicles (USVs) are a promising marine robotic platform for numerous potential applications in ocean space due to their small size, low cost, and high autonomy. Modelling and control of USVs is a challenging task due to their intrinsic nonlinearities, strong couplings, high uncertainty, under-actuation, and multiple constraints. Well designed motion controllers may not be effective when exposed in the complex and dynamic sea environment. The paper presents a fully data-driven learning-based motion control method for an USV based on model-based deep reinforcement learning. Specifically, we first train a data-driven prediction model based on a deep network for the USV by using recorded input and output data. Based on the learned prediction model, model predictive motion controllers are presented for achieving trajectory tracking and path following tasks. It is shown that after learning with random data collected from the USV, the proposed data-driven motion controller is able to follow trajectories or parameterized paths accurately with excellent sample efficiency. Simulation results are given to illustrate the proposed deep reinforcement learning scheme for fully data-driven motion control without any a priori model information of the USV.