Planning of robot trajectories with genetic algorithms

Monteiro, D.; Madrid, Marconi Kolm

doi:10.1109/romoco.1999.791079

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…The trajectory planning is carried out in the joint space, and the path is represented by knots connected through cubic splines. Monteiro and Madrid [19] have used GA to plan the stages of the trajectory of a robot arm called Jeca III. They have proposed the use of GA to plan a trajectory with obstacle avoidance and to implement joint space using classical GA.…”

Section: Introductionmentioning

confidence: 99%

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

et al. 2014

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SUMMARYThis paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance traveled, and significant points distance traveled) have been computed to study and analyze the algorithm efficiency. The experimental results show that the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

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Section: Introductionmentioning

confidence: 99%

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

et al. 2014

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“…The planner proposed by Valero et al, 1997, in which all the workspace is analysed, is also worth mentioning. Besides the methods already mentioned, genetic algorithms have also been implemented successfully for the path planning problem (see Monteiro et al, 1999, Davidor, 1991and Yang et al, 2008 There are many authors who have made a historical review of these algorithms over the years. Some examples are Kavraki and Latombe, 1998, Isto, 1996, LaValle, 1996and 2000, Gupta, 1998.…”

Section: Brief Introductionmentioning

confidence: 99%

“…The current issue and full text archive of this journal is available at www.emeraldinsight.com/0143-991X.htm mentioned, genetic algorithms have also been implemented successfully for the path planning problem (Monteiro and Madrid, 1999;Davidor, 1991;Yang et al, 2008).…”

Section: Brief Introductionmentioning

confidence: 99%

Comparing the efficiency of five algorithms applied to path planning for industrial robots

Rubio

Abu‐Dakka

Valero

et al. 2012

Industrial Robot: An International Journal

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Purpose-To compare the quality and efficiency of five methods for solving the path planning problem of industrial robots in complex environments Design/methodology/approach-Five methods are presented for solving the path planning problem and certain working parameters have been monitored using each method. These working parameters are the distance travelled by the robot and the computational time needed to find a solution. A comparison of results has been analyzed. Findings-After this study, it could be easy to know which of the proposed methods is most suitable for application in each case, depending on the parameter the user wants to optimize. The findings have been summarized in the conclusion section. Research limitations/implications-Five techniques have been developed which yield good results in general.Practical implications-The algorithms introduced are able to solve the path planning problem for any industrial robot working with obstacles. Social implications-The path planning algorithms help robots perform their tasks in a more efficient way due to the fact that the path followed has been optimized and therefore they help humans work together with the robots in order to obtain the best results from them. Originality/value-The paper shows which algorithm offers the best results depending on the example the user has to solve and the parameter to be optimized.

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“…For example, Monteiro and Madrid (1999) have used GA to plan the stages of the trajectory of a robot arm called Jeca III. They have proposed the use of GA to plan a trajectory with obstacle avoidance and implement joint space using classical GA.…”

Section: Introductionmentioning

confidence: 99%

Optimization techniques applied to multiple manipulators for path planning and torque minimization

Garg

Kumar

2002

Engineering Applications of Artificial Intelligence

114

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This paper presents the formulation and application of a strategy for the determination of an optimal trajectory for a multiple robotic configuration. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used as the optimization techniques and results obtained from them compared. First, the motivation for multiple robot control and the current state-of-art in the field of cooperating robots are briefly given. This is followed by a discussion of energy minimization techniques in the context of robotics, and finally, the principles of using genetic algorithms and simulated annealing as an optimization tool are included. The initial and final positions of the end effector are specified. Two cases, one of a single manipulator, and the other of two cooperating manipulators carrying a common payload illustrate the proposed approach. The GA and SA techniques identify the optimal trajectory based on minimum joint torque requirements. The simulations performed for both the cases show that although both the methods converge to the global minimum, the SA converges to solution faster than the GA. r

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Planning of robot trajectories with genetic algorithms

Cited by 7 publications

References 2 publications

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

Comparing the efficiency of five algorithms applied to path planning for industrial robots

Optimization techniques applied to multiple manipulators for path planning and torque minimization

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