Systematic dynamic modelling of mechanical systems containing kinematic loops

In this tutorial paper a systematic procedure is presented to obtain the dynamic models of mechanical systems containing kinematic loops, with a main emphasis on efficiency and with particular regard to robotic systems. The procedure retains a minimal set of generalized coordinates for the corresponding open loop structure, obtained by removing some additional constraints closing loops in the original structure, while adopting an efficient Newton-Euler formulation of the equations of motion. Two methods for including the loop closure equations are then discussed: the Lagrange multipliers method and the method based on an explicit solution of the constraint equations. In the first case the dynamic model is given in the form of an implicit Differential Algebraic Equations (DAE) system, while in the second case an Ordinary Differential Equations (ODE) system could be obtained.     

Application of laboratory robots in industrial automation

Attempts to automate the determination of biological oxygen demand in industrial waste water and the measurement of the pH and acidity of yoghurt samples from a production line are described. Both applications include novel approaches in order to solve the problems encountered. A brief discussion is given of the demands on a customer when installing robotics for automation.     

Asymptotic stability of a mechanical robotics model with damping and delay

In this paper we study the asymptotic stability of a mechanical robotics model with damping and delay. This model yields a certain linear third order delay differential equation. In proving our results we make use of Pontryagin's theory for quasi-polynomials.     

Path planning for robots under stochastic uncertainty *

In order to reduce large online measurement and correction expenses, the a priori informations on the random variations of the model parameters of a robot and its working environment are taken into account already at the planning stage. Thus, instead of solving a deterministic path planning problem with a fixed nominal parameter vector, here, the optimal velocity profile along a given trajectory in work space is determined by using a stochastic optimization approach. Especially, the standard polygon of constrained motion-depending on the nominal parameter vector-is replaced by a more general set of admissible motion determined by chance constraints or more general risk constraints. Robust values (with respect to stochastic parameter variations) of the maximum, minimum velocity, acceleration, deceleration, resp., can be obtained then by solving a univariate stochastic optimization problem Considering the fields of extremal trajectories, the minimum-time path planning problem under stochastic uncertainty can be solved now by standard optimal deterministic path planning methods     

一种复杂环境下移动机器人避障新方法

基于传统人工势场法的机器人避障算法虽然计算量小、实时性好,但在陷阱区域或在障碍物前可能产生震荡,导致机器人不能到达目标点,为解决上述缺陷,提出一种带记忆功能的沿边法。通过记录和分析障碍物边缘人工势场法的局部最小点来判断目标点是否被障碍物包围,从而避免机器人来回震荡和围着目标点旋转的发生。该方法步骤分为沿边行为激活和退出条件、局部最小点记录条件以及目标点被障碍物包围的判断准则。通过实验,结果表明该方法解决了复杂环境下机器人的避障问题。该方法用于复杂环境下的机器人避障是可行的、有效的。