Trajectory planning of mobile manipulators using dynamic programming approach

Korayem, M. H.; Irani, M.; Charesaz, A.; Korayem, Moharam Habibnejad; Hashemi, Arash

doi:10.1017/s0263574712000628

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…Despite the computational complexities and the probability of non-convergence in indirect methods, these methods are relatively accurate and reliable. Hamilton-Jacobi-Bellman equation and Pontryagin's maximum principle are two known methods in indirect methods which use calculus variation for conversion of optimization problem to a set of differential equations [8]. Because of the very high nonlinearities of system equations of flexible robot manipulator and too many parameters, a method based on Pontryagin's maximum principle is used to solve the optimal control problem in this article.…”

Section: Methodsmentioning

confidence: 99%

“…To convert Eqs. (8) to (12) a standard form of an optimal control problem, the equations should be as follows:…”

Section: Formulation Of Trajectory Optimization In Point-to-point Motionmentioning

confidence: 99%

“…They converted optimal control problem into standard nonlinear programming by using this technique and could achieve the minimum traveling time and actuating torque in point-to-point motion. Korayem et al [8] used dynamic programming in determining the optimal trajectory of rigid manipulators in both motions along a specified path and point-to-point motion. They increase convergence speed using a method based on sequential quadratic programming.…”

Section: Introductionmentioning

confidence: 99%

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Separation of motion techniques for trajectory optimization in flexible redundant robot manipulators

Faritus¹,

Heidari²,

Shateyi³

2017

J. vibroeng.

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This paper presents a computational method for optimization of trajectory in redundant robot manipulators. For this purpose, all possible answers are acquired based on rigid conditions and redundancy of the robot. Using open loop optimal control method, the trajectory which minimizes the objective function will be obtained. The objective function is considered as an integral index that will be minimized in the entire trajectory. The objective function and constraints of optimization problem will be selected based on conditions of motion. Dynamic equations of the system are constraints of optimization problem in point-to-point motion. For motion conditions in the specified path, kinematic equations will be added. Also, unequal constraints are applied for limiting the velocity and torque. By selecting the state and control signal vectors which are obtained by assuming rigid motion of the robot, the objective function and constraints will be changed to standard form of an optimization problem. Pontryagin's maximum principle is used to solve equations. So, the equations of classical form will be changed into two-point boundary value problem. Suggested method is applied to point-to-point motion and movement in the specified path. Results demonstrate accuracy and efficiency of suggested method.

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Section: Methodsmentioning

confidence: 99%

“…To convert Eqs. (8) to (12) a standard form of an optimal control problem, the equations should be as follows:…”

Section: Formulation Of Trajectory Optimization In Point-to-point Motionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Separation of motion techniques for trajectory optimization in flexible redundant robot manipulators

Faritus¹,

Heidari²,

Shateyi³

2017

J. vibroeng.

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“…Several classical optimization approaches have been used to deal with the trajectory planning and the DLCC calculation. Dynamic programming approach is applied to solve optimization problem with nonlinear constraints and the sequential quadratic programming formulation is used to increase the speed of convergence for both fixed and mobile base mechanical manipulators [34]. The iterative linear programming method and the discrete augmented Lagrangian technique are used to solve the nonlinear constrained optimization problem for cable driven robots [35] and parallel kinematic machines [21], respectively.…”

Section: Introductionmentioning

confidence: 99%

A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

Guo¹,

Li²,

Cao³

et al. 2016

Mathematical Problems in Engineering

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A new methodology using a direct method for obtaining the best found trajectory planning and maximum dynamic load-carrying capacity (DLCC) is presented for a 5-degree of freedom (DOF) hybrid robot manipulator. A nonlinear constrained multiobjective optimization problem is formulated with four objective functions, namely, travel time, total energy involved in the motion, joint jerks, and joint acceleration. The vector of decision variables is defined by the sequence of the time-interval lengths associated with each two consecutive via-points on the desired trajectory of the 5-DOF robot generalized coordinates. Then this vector of decision variables is computed in order to minimize the cost function (which is the weighted sum of these four objective functions) subject to constraints on joint positions, velocities, acceleration, jerks, forces/torques, and payload mass. Two separate approaches are proposed to deal with the trajectory planning problem and the maximum DLCC calculation for the 5-DOF robot manipulator using an evolutionary optimization technique. The adopted evolutionary algorithm is the elitist nondominated sorting genetic algorithm (NSGA-II). A numerical application is performed for obtaining best found solutions of trajectory planning and maximum DLCC calculation for the 5-DOF hybrid robot manipulator.

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“…Nonlinear feedback control of robotic manipulators is a well-researched area. [1][2][3] The latest robotic controllers are microprocessors-based, leading to the sampled-data-based control schemes. This offers both online and offline programming controls.…”

Section: Introductionmentioning

confidence: 99%

Sampled data output regulation of n-link robotic manipulator using a realizable reconstruction filter

Liaquat

Malik

2014

Robotica

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SUMMARYThis paper proposes a solution to the sampled-data regulation problem for feedback linearizable n-link robotic manipulators. The prime focus is on the development and stability analysis of the proposed control scheme in the presence of model uncertainties and external disturbances. A major constraint is the availability of sampled measurements of output signal. This leads to designing an impulsive observer for feedback linearization. The discrete-time control input is mapped into its continuous-time counterpart using a realizable reconstruction filter (RRF). The underlying control scheme relies on the sampled-data regulator theory based on the discrete-time equivalence of the plant and RRF modeled as impulsive system. This method leads to controller/observer design in discrete time. The working of the entire scheme is dependent on the stability of impulsive observer; hence a Lyapunov-based stability analysis is also included to ensure the stability of a closed-loop system. The working of the proposed scheme along with a comparison with conventional solution is presented, when applied to the control of a 3-degree-of-freedom PUMA 560 robot.

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Trajectory planning of mobile manipulators using dynamic programming approach

Cited by 11 publications

References 16 publications

Separation of motion techniques for trajectory optimization in flexible redundant robot manipulators

Separation of motion techniques for trajectory optimization in flexible redundant robot manipulators

A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

Sampled data output regulation of n-link robotic manipulator using a realizable reconstruction filter

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