Evolutionary multi-criteria trajectory modeling of industrial robots in the presence of obstacles

Saravanan, R.; Ramabalan, S.; Balamurugan, C.

doi:10.1016/j.engappai.2008.06.002

Cited by 48 publications

(18 citation statements)

References 14 publications

(26 reference statements)

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“…Castillo 2007, Dellnitz 2009, Saravanan et al 2009). In those studies, different path descriptions have been coded within the solution-related chromosome code.…”

Section: Ga Emo and Their Utilization For Trajectory Planningmentioning

confidence: 99%

Trajectory planning for multiple unmanned vehicles under reciprocal constraints

Avigad

Eisenstadt

Cohen

2011

Engineering Optimization

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In this article, a special class of trajectory optimization problems is formalized and solved. It involves the optimization of different unmanned vehicle (UMV) trajectories that are coupled through reciprocal constraints. It is shown in the article that searching for a solution to the problem at hand may stipulate not just planning a longer than the shortest possible path for each UMV, but also choosing slower travel speeds in order to co-ordinate between the UMVs. Although it seems that solving the problem possesses merits, it has been only partially treated before. Here it is solved by utilizing an evolutionary approach which involves a new algorithmic feature that allows striving towards the desired optimality. The approach is demonstrated and studied through solving and simulating several trajectory planning problems. It is shown that a wide range of problems might be related to that class of problems.

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“…Castillo 2007, Dellnitz 2009, Saravanan et al 2009). In those studies, different path descriptions have been coded within the solution-related chromosome code.…”

Section: Ga Emo and Their Utilization For Trajectory Planningmentioning

confidence: 99%

Trajectory planning for multiple unmanned vehicles under reciprocal constraints

Avigad

Eisenstadt

Cohen

2011

Engineering Optimization

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“…It is very important for robot manipulators to avoid collisions with obstacles; otherwise, the robot manipulators or the objects being held may undergo serious damage. In addition, collision avoidance is a fundamental problem for the redundant robot manipulators (Aitkenhead & McDonald, 2002;Cheng, Lu, & Sun, 1998;Ding & Chan, 1996;Saravanan, Ramabalan, & Balamurugan, 2009;Wang & Xin, 2013;Zhang & Wang, 2004). It is about how to avoid obstacles cluttered in an environment while performing primary tasks at the end-effector.…”

Section: Introductionmentioning

confidence: 98%

Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators

Xiao

Zhang

2014

International Journal of Systems Science

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For avoiding obstacles and joint physical constraints of robot manipulators, this paper proposes and investigates a novel obstacle avoidance scheme (termed the acceleration-level obstacle-avoidance scheme). The scheme is based on a new obstacle-avoidance criterion that is designed by using the gradient neural network approach for the first time. In addition, joint physical constraints such as joint-angle limits, joint-velocity limits and joint-acceleration limits are incorporated into such a scheme, which is further reformulated as a quadratic programming (QP). Two important 'bridge' theorems are established so that such a QP can be converted equivalently to a linear variational inequality and then equivalently to a piecewise-linear projection equation (PLPE). A numerical algorithm based on a PLPE is thus developed and applied for an online solution of the resultant QP. Four path-tracking tasks based on the PA10 robot in the presence of point and windowshaped obstacles demonstrate and verify the effectiveness and accuracy of the acceleration-level obstacle-avoidance scheme. Besides, the comparisons between the non-obstacle-avoidance and obstacle-avoidance results further validate the superiority of the proposed scheme.

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“…Moreover, many proposals for obtaining the optimal trajectories of serial manipulators, including multi-objective path planning, can be found in the literature [7][8][9]. Nevertheless, the problem of optimizing the trajectories of PKMs is less explored.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning in parallel kinematic manipulators using a constrained multi-objective evolutionary algorithm

Chen

Pham

2011

Nonlinear Dyn

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Generating manipulator trajectories considering multiple objectives with kinematics and dynamics constraints is a non-trivial optimization. In this paper, a constrained multi-objective genetic algorithm (MOGA) based technique is proposed to address this problem for a general motor-driven parallel kinematic manipulator. The planning process is composed of searching for a motion ensuring the accomplishment of the assigned task, minimizing the traverse time, and expended energy subject to various constraints imposed by the associated kinematics and dynamics of the manipulator. This problem is treated via an adequate parametric path representation in the task space of the moving platform, and then the use of the constrained MOGA for solving the resulted nonlinear multi-objective optimization problem. Simulation results are presented for the trajectories of the parallel kinematic manipulator, and a subsequent comparison with the weighted sum method is also carried out.

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Evolutionary multi-criteria trajectory modeling of industrial robots in the presence of obstacles

Cited by 48 publications

References 14 publications

Trajectory planning for multiple unmanned vehicles under reciprocal constraints

Trajectory planning for multiple unmanned vehicles under reciprocal constraints

Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators

Trajectory planning in parallel kinematic manipulators using a constrained multi-objective evolutionary algorithm

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