Trajectory planning in workspaces with obstacles taking into account the dynamic robot behaviour

Valero, Francisco; Mata, Vicente; Besa, Antonio

doi:10.1016/j.mechmachtheory.2005.08.002

Cited by 54 publications

(51 citation statements)

References 12 publications

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“…The kinematic equations of mobile robot were found by inverse kinematics method to obtain the desired values of the linear and angular velocities, which represented the input to adaptive controller. Valero et al [15] investigated a trajectory planner for 3-DOF industrial robots that have to operate in workspaces with obstacles. They found the workspace modeling analytically using the differential equations of all joints angle and solved them in Cartesian coordinates system.…”

Section: Introductionmentioning

confidence: 99%

Micro-Robot Management

Altabey¹

2012

MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 3

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Section: Introductionmentioning

confidence: 99%

Micro-Robot Management

Altabey¹

2012

MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 3

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“…Indirect approaches firstly seek for a path in the configuration space, and then the trajectory adjusts; subjected to the dynamic constraints of the manipulator, see Steffen 2001 andValero et al 2006. On the other hand, the search takes place in the system's state space in the direct approaches.…”

Section: Trajectory Planningmentioning

confidence: 99%

“…For depth knowledge you should refer to Piazzi and Visioli 1997a, Saramago and Steffen 2001, Plessis and Snyman 2003, Behzadipour and Khajepour 2006, Valero et al 2006, Bertolazzi et al 2007, Gasparetto and Zanotto 2007 A characteristic of indirect methods is that the path is known previously; either because it depends on the activity to be done by the industrial robot or because it has been generated by a path planner. Generally speaking, the indirect methods combine the path planning with the obtaining of the time history of motion usually in a sequential way.…”

Section: Trajectory Planningmentioning

confidence: 99%

“…This space of configurations is based on the obtaining of adjacent configurations developed by Valero 1990, Valero et al 1997, Valero et al 2006, and redefined by Abu-Dakka et al 2007, Abu-Dakka et al 2008.…”

Section: Adjacent Configurations For Trajectory Planningmentioning

confidence: 99%

“…The algorithm works on a discretized configuration space which is generated gradually as the direct procedure solution evolves, demands less computational effort than the corresponding indirect procedure, Valero et al 2006.…”

Section: Obtaining the Trajectorymentioning

confidence: 99%

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Trajectory planning for industrial robot using genetic algorithms

Abu‐Dakka¹

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En el nombre de Allah, el Misericordioso, el Compasivo(1) ¡Lee en el nombre de su Señor que ha creado! (2) Ha creado al hombre de un coágulo. ABSTRACTIn the last decades, many researches have been proposed concerning the path and trajectory planning for manipulators. Path and trajectory planning have important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. On the other hand, the evolutionary algorithms have been applied in this plethora of fields, which motivates the author's interest on its application to the path and trajectory planning for industrial robots.In this work, an exhaustive search of the existing literature related to the thesis has been carried out, which has served to create a comprehensive database used to perform a detailed historical review of developments since its origins to the current state of the art and the latest trends.This thesis presents a new methodology that uses genetic algorithms to develop and evaluate path and trajectory planning algorithms. Problem-specific knowledge and heuristic knowledge are incorporated into encoding, evaluation and genetic operators of the genetic algorithm.This methodology introduces new approaches that aim at solve the problem of path planning and trajectory planning for industrial robotic systems operating in 3D environments with static obstacles. Therefore, two algorithms (somehow, they are similar, but with some variations) are created to solve the mentioned planning problems.Obstacles modeling have been done by using combinations of simple geometric objects (spheres, cylinders, and plans) which provide an efficient algorithm for collision avoidance. Trajectory Planning for Industrial Robots Using Genetic AlgorithmsPath planning algorithm is based on global genetic algorithms optimization techniques, which aim to minimize the sum of the distances between significant points of the robot along the path considering the restrictions to avoid collisions with obstacles. The path is composed of adjacent configurations obtained by an optimization technique using genetic algorithms, seeking to minimize a multiobjective function that involves the distance between significant points of the two adjacent configurations, and the distance from the points of the current configuration to the final one. An evaluation method is designed according to the problem presentation by defining individuals and genetic operators capable of providing efficient solutions to the problem. The result of this algorithm is the shortest path between two configurations given by the user.Trajectory planning algorithm is also based on genetic algorithms optimization techniques using the direct procedure. The algorithm is similar to the mentioned previously algorithm for path planning problem, but with some differences in the objective function and some details related to the conceptual difference between path and trajectory planning. The objective of this algorithm is to minimize the time required to mo...

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Simultaneous algorithm for trajectory planning

Rubio

Suñer

Mata

2010

Asian Journal of Control

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This paper addresses the solution of smooth trajectory planning for industrial robots in environments with obstacles using a direct method, creating the trajectory gradually as the robot moves. The presented method deals with the uncertainties associated with the lack of knowledge of kinematic properties of intermediate via-points since they are generated as the algorithm evolves looking for the solution. Several cost functions are also proposed, which use the time that has been calculated to guide the robot motion. The method has been applied successfully to a PUMA 560 robot and four operational parameters (execution time, computational time, distance travelled and number of configurations) have been computed to study the properties and influence of each cost function on the trajectory obtained.

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Trajectory planning in workspaces with obstacles taking into account the dynamic robot behaviour

Cited by 54 publications

References 12 publications

Micro-Robot Management

Micro-Robot Management

Trajectory planning for industrial robot using genetic algorithms

Simultaneous algorithm for trajectory planning

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