Journal ArticleDOI
Optimal Trajectory Planning for Robots under the Consideration of Stochastic Parameters and Disturbances
Kurt Marti,S. Qu +1 more
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TLDR
Measuring the violations of the basic mechanical conditions by means of expected penalty costs, a stochastic optimization problem is obtained for the computation of an optimal open-loop control.Abstract:
Efficient control strategies of robots should cause only low on-line correction expenses. Hence, the mostly available statistical and a priori informations about the random parameters and disturbances of the underlying mechanical system and its environment should be considered already for off-line programming of robots. Measuring the violations of the basic mechanical conditions by means of expected penalty costs, a stochastic optimization problem is obtained for the computation of an optimal open-loop control. The stochastic optimization problem can be solved — after discretization — by parameter optimization.read more
Citations
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Stochastic optimization methods
TL;DR: Many concrete problems from engineering, economics, operations research, etc., can be formulated by an optimization problem of the type ============�ατερατατηραγαγηαγγααγεγαβααβγατταγβαγ βγα βαγ αγα αβαββα αγγγ βαβ βα β βαα ββα β αγββ β β βββ α βα αα β
Journal ArticleDOI
Path Planning for Robots by Stochastic Optimization Methods
TL;DR: The problem of adaptive trajectory planning for robots under stochastic uncertainty is considered, where new information about the robots and their environment is presented on-line and the optimal control can be calculated in real-time.
Book ChapterDOI
Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) for Robots
TL;DR: By incorporating into the control design the available a priori and sample information about the unknown model parameters using stochastic optimization methods, the mean absolute deviation between the actual and prescribed trajectroy can be reduced cosiderably and robust optimal controls are obtained.
Journal ArticleDOI
Path planning for robots under stochastic uncertainty
TL;DR: In this paper, instead of solving a deterministic path planning problem with a fixed nominal parameter vector, the optimal velocity profile along a given trajectory in work space is determined by using a stochastic optimization approach.
Journal ArticleDOI
Stochastic Optimization Methods in Robust Adaptive Control of Robots
TL;DR: In the optimal control of industrial or service robots, the standard procedure is to determine first off-line a feedforward control and a reference trajectory based on some nominal values of the model parameters, and to correct then the resulting inevitable deviation of the trajectory or performance of the system from the prescribed values by on-line measurement and control actions.
References
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Robot analysis and control
TL;DR: The basic concepts of robot manipulation are introduced--the fundamental kinematic and dynamic analysis of manipulator arms, and the key techniques for trajectory control and compliant motion control.
Journal ArticleDOI
A concept for manipulator trajectory planning
Friedrich Pfeiffer,R. Johanni +1 more
TL;DR: A transformation of the equations of motion from joint coordinates to path coordinates leads to a set, which cannot only be solved by formal quadrature but defines as well the phase space of admissible motion constrained by path geometry and joint torques.
Journal ArticleDOI
A complete generalized solution to the inverse kinematics of robots
TL;DR: An iterative solution is presented that is suitable for any class of robots having rotary or prismatic joints, with any arbitrary number of degrees of freedom, including both standard and kinematically redundant robots.
Journal ArticleDOI
Robot Path Planning with Obstacles, Actuator, Gripper, and Payload Constraints
Zvi Shiller,Steven Dubowsky +1 more
TL;DR: A method is presented to obtain the time-optimal motions for robotic manipulators that considers the full nonlinear dy namics of the manipulator, its actuator saturation limits, and gripper and payload constraints.