Minimum energy trajectory for an underwater manipulator and its simple planning method by using a Genetic Algorithm

Shintaku, Eiji

doi:10.1163/156855399x00171

Cited by 25 publications

(7 citation statements)

References 7 publications

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“…The translational veloci de (13) The sign function sgn(x) is def ties with respect to link i are fined as (14) In order to derive the dynamics of an underwater robot m em order to minimize the cost function under certain con-anipulator, the external force exerted by the fluid drag needs to be added to the robot manipulator dynamics. The fluid drag on an object is proportional to the square of the object's speed [16].…”

Section: Variablementioning

confidence: 99%

“…These studies were carried out with the objective of constructing an optimal time trajectory for a manipulator, from its initial position to the target position. On the other hand, Eiji presented a method for determining the minimum energy trajectory of an underwater manipulator [14]. Uno et al presented a minimum torque change model [15] A method for drag reduction control has not been developed thus far.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Trajectory of Underwater Manipulator Using Adjoint Variable Method for Reducing Drag

Shinohara¹

2011

OJDM

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In order to decrease the fluid drag on an underwater robot manipulator, an optimal trajectory method based on the variational method is presented. By introducing the adjoint variables, which are Lagrange multipliers, we formulate a Lagrange function under certain constraints related to the target angle, target angular velocity, and dynamic equation of the robot manipulator. The state equation (the partial differentiation of the Lagrange function with respect to the state variables), adjoint equation (the partial differentiation of the Lagrange function with respect to the adjoint variables), and sensitivity equation (the partial differentiation of the Lagrange function with respect to torques) can be derived from the stationary conditions of the Lagrange function. Using the state equation, we can calculate the state variables (angles, angular velocities, and angular acceleration) at every time step in the forward time direction. These state variables are stored as data at every time step. Next, by using the adjoint equation, we can calculate the adjoint variables by using these state variables at every time step in the backward time direction. These adjoint variables are stored as data at every time step. Third, the sensitivity equation is calculated by using both the state variables and the adjoint variables. Finally, the optimal trajectory of the manipulator is obtained using the sensitivities. The proposed method is applied to the problem of two-link manipulators. It can obtain the optimal drag reduction trajectory of the manipulator under the constraints mentioned above.

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Section: Variablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory of Underwater Manipulator Using Adjoint Variable Method for Reducing Drag

Shinohara¹

2011

OJDM

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“…The 2 nd and 5 th legs are chosen as the symmetric pair of supporting legs. The reference angle trajectory for each joint is designed via the quintic polynomial (Shintaku, 1999):…”

Section: Simulation Studymentioning

confidence: 99%

Life Extending Minimum-Time Path Planning for Hexapod Robot

Zhou

et al. 2011

International Journal of Advanced Robotic Systems

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This paper presents a minimum-time path planning scheme for life-extending operation of legged robots, illustrated with a six-legged walking robot (hexapod). The focus of this study is on extending the bearing fatigue life for leg joints. As a typical treatment, the minimum-time path planning is performed through a bisecting-plane (BP) algorithm with the constraints of maximum joint angular velocity and acceleration. Based on bearing fatigue life theory, its fatigue life increases while the dynamic radial force on the bearing decreases. By imposing more rigorous constraint on the dynamic radial force, the minimum-time path planning algorithm is thus revised by reinforcing the constraint of maximum radial force based on the expectation of life extension. A symmetric hexapod with 18 degree-of-freedom (DOF) is adopted as the illustrative example for simulation study. The simulation results validate the effectiveness of possible life extending with moderate compromise in transient performance.

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“…Many research works have concentrated on minimum time trajectories [9][10][11][12][13]. The robot manipulator trajectory planning using the energy criteria is reported in several literatures [14][15][16][17][18]. This kind of manipulator trajectory planning can produce smooth trajectories and reduce the stresses between the actuators and the robot manipulator structure.…”

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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Minimum energy trajectory for an underwater manipulator and its simple planning method by using a Genetic Algorithm

Cited by 25 publications

References 7 publications

Optimal Trajectory of Underwater Manipulator Using Adjoint Variable Method for Reducing Drag

Optimal Trajectory of Underwater Manipulator Using Adjoint Variable Method for Reducing Drag

Life Extending Minimum-Time Path Planning for Hexapod Robot

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

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