Optimization of the Trajectory Planning of Robot Manipulators Taking Into Account the Dynamics of the System

Saramago, Sezimária de Fátima Pereira; Steffen, Valder

doi:10.1016/s0094-114x(97)00110-9

Cited by 108 publications

(56 citation statements)

References 2 publications

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“…In addition, in contrast to higher-order polynomials, the cubic splines can overcome excessive oscillations and overshoot problems between any pair of reference points. In this paper, we implement the joint trajectory planning by utilizing the cubic splines described in [10,11,14]. The trajectory planning performance depends largely on the objective function.…”

Section: The Minimum Time-jerk Trajectory Planning Problemmentioning

confidence: 99%

Solving the Time‐Jerk Optimal Trajectory Planning Problem of a Robot Using Augmented Lagrange Constrained Particle Swarm Optimization

Zhao

Jiang

et al. 2017

Mathematical Problems in Engineering

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The problem of minimum time-jerk trajectory planning for a robot is discussed in this paper. The optimal objective function is composed of two segments along the trajectory, which are the proportional to the total execution time and the proportional to the integral of the squared jerk (which denotes the derivative of the acceleration). The augmented Lagrange constrained particle swarm optimization (ALCPSO) algorithm, which combines the constrained particle swarm optimization (CPSO) with the augmented Lagrange multiplier (ALM) method, is proposed to optimize the objective function. In this algorithm, falling into a local best value can be avoided because a new particle swarm is generated per initial procedure, and the best value gained from the former generation is saved and delivered to the next generation during the iterative search procedure to enable the best value to be found more easily and more quickly. Finally, the proposed algorithm is tested on a planar 3-degree-of-freedom (DOF) robot; the simulation results show that the algorithm is effective, offering a solution to the time-jerk optimal trajectory planning problem of a robot under nonlinear constraints.

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Section: The Minimum Time-jerk Trajectory Planning Problemmentioning

confidence: 99%

Solving the Time‐Jerk Optimal Trajectory Planning Problem of a Robot Using Augmented Lagrange Constrained Particle Swarm Optimization

Zhao

Jiang

et al. 2017

Mathematical Problems in Engineering

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“…Examples of energy optimal trajectory planning are provided in some literatures. Both optimal travelling time and minimum mechanical energy of the actuators are considered together as objective functions in some literatures [5,6] .…”

Section: Introductionmentioning

confidence: 99%

“…When moving obstacles share the same workspace occupied by the robot manipulator, the optimisation of the trajectory defined by the end-effector is complex [1,5,6] . This complexity is associated with the large number of constraints to be taken into account by the optimiser.…”

Section: Introductionmentioning

confidence: 99%

“…These constraints are time dependent in this case. A design methodology using sequential unconstrained minimisation techniques is proposed by Saramago and Junior [5] to obtain the optimal off-line trajectory planning of robot manipulators, when oscillating obstacles have to be avoided by the end-effector. Their problem of optimal trajectory planning concerns with the determination of the endeffector robot motion in a minimum time and minimum mechanical energy between two given points, while satisfying the limits of the actuator efforts and avoiding collision with oscillating and fixed obstacles.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary trajectory planning for an industrial robot

Saravanan¹,

Ramabalan²,

Balamurugan³

et al. 2010

Int. J. Autom. Comput.

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This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.

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“…Saramago and Steffen (Saramago & Steffen, 1998) have formulated off-line joint-space trajectories to optimize traveling time and minimize mechanical energy of the actuators using spline functions. Zhu et al (Zhu et al , 1999) have formulated real-time collision free trajectory by minimizing an energy function.…”

mentioning

confidence: 99%

A unified dynamics-based motion planning algorithm for autonomous underwater vehicle-manipulator systems (UVMS)

Podder¹,

Sarkar²

2004

Robotica

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IntroductionAmong the underwater robotic systems that are currently available, remotely operated vehicles (ROVs) are the most commonly used underwater robotic systems. A ROV is an underwater vehicle that is controlled from a mother-ship by human operators. Sometimes a ROV is equipped with one or more robotic manipulators to perform underwater tasks. These robotic manipulators are also controlled by human operators from a remote site (e.g., mother-ship) and are known as tele-manipulators. Although the impact of ROVs with telemanipulators is significant, they suffer from high operating cost because of the need for a mother-ship and experienced crews, operator fatigue and high energy consumption because of the drag generated by the tether by which the ROV is connected to the ship. The performance of such a system is limited by the skills, coordination and endurance of the operators. Not only that, communication delays between the master and the slave site (i.e., the mother-ship and the ROV) can severely degrade the performance. In order to overcome some of the above-mentioned problems, autonomous underwater vehicles (AUVs) are developed. However, an AUV alone cannot interact with the environment. It requires autonomous robotic manipulator(s) attached to it so that the combined system can perform some useful underwater tasks that require physical contact with the environment. Such a system, where one or more arms are mounted on an AUV, is called an autonomous underwater vehicle-manipulator system (UVMS). One of the main research problems in underwater robotics is how to design an autonomous controller for a UVMS. Since there is no human operator involved in the control of a UVMS, the task planning has become an important aspect for smooth operation of such a system. Task planning implies the design of strategies for task execution. In other words, a task planning algorithm provides a set of desired (i.e., reference) trajectories for the position and force variables, which are used by the controller to execute a given task. Task planning can be divided into motion planning and force planning. In this research, we focus on the design of motion planning algorithms for a UVMS. The motion planning of a UVMS is a difficult problem because of several reasons. First, a UVMS is a kinematically redundant system. A kinematically redundant system is one which has more than 6 degrees-of-freedom (DOF) in a 3-D space.

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Optimization of the Trajectory Planning of Robot Manipulators Taking Into Account the Dynamics of the System

Cited by 108 publications

References 2 publications

Solving the Time‐Jerk Optimal Trajectory Planning Problem of a Robot Using Augmented Lagrange Constrained Particle Swarm Optimization

Solving the Time‐Jerk Optimal Trajectory Planning Problem of a Robot Using Augmented Lagrange Constrained Particle Swarm Optimization

Evolutionary trajectory planning for an industrial robot

A unified dynamics-based motion planning algorithm for autonomous underwater vehicle-manipulator systems (UVMS)

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