PurposeThe purpose of this paper is to solve the path‐planning problem of industrial robots in complex environments.Design/methodology/approachA direct method (in each step, the path is been recorded) is presented in which the search of the path is made in the state space of the robotic system, and it makes use of the information generated about the characteristics of the process, introducing graph techniques for branching. The method poses an optimization problem that aims at minimizing the distance travelled by the significant points of the robot.FindingsA new approach to solve the path‐planning problem has been introduced in which the behaviour of three operational parameters (computational time, distance travelled and number of configurations generated) have been analyzed so that the user can choose the most efficient algorithm depending on which parameter he is most interested in.Research limitations/implicationsA new technique has been introduced which yields good results as the examples show.Practical implicationsThe algorithm is able to obtain the solution to the path‐planning problem for any industrial robot working in a complex environment.Originality/valueGives a new tool for solving the path‐planning problem.
Purpose The purpose of this paper is to analyze the impact of the torque, power, jerk and energy consumed constraints on the generation of minimum time collision-free trajectories for industrial robots in a complex environment. Design/methodology/approach An algorithm is presented in which the trajectory is generated under real working constraints (specifically torque, power, jerk and energy consumed). It also takes into account the presence of obstacles (to avoid collisions) and the dynamics of the robotic system. The method solves an optimization problem to find the minimum time trajectory to perform the tasks the robot should do. Findings Important conclusions have been reached when solving the trajectory planning problem related to the value of the torque, power, jerk and energy consumed and the relationship between them, therefore enabling the user to choose the most efficient way of working depending on which parameter he is most interested in optimizing. From the examples solved we have found the relationship between the maximum and minimum values of the parameters studied. Research limitations/implicationsThis new approach tries to model the real behaviour of the actuators in order to be able to upgrade the trajectory quality. So a lot of work has to be done in this field. Practical implicationsThe algorithm solve the trajectory planning problem for any industrial robot and the real characteristics of the actuators are taken into account which is essential to improve the performance of it. Originality/value This new tool enables us to improve the performance of the robot by combining adequately the values of the mentioned parameters (torque, power, jerk and consumed energy).
Purpose -The purpose of this paper is to solve the trajectory planning problem of industrial robots in a complex environment. Design/methodology/approach -A simultaneous algorithm was presented in which the trajectory was generated gradually as the robot moves. It takes into account the presence of obstacles (to avoid collisions) and differential constraints related to the dynamics of the robotic system. The method poses an optimization problem that aims at minimizing the time to perform the trajectory when several interpolation functions are used. Findings -A new approach to solving the trajectory planning problem in which the behaviour of four operational parameters (execution time, computational time, distance travelled and number of configurations) have been analyzed when changing the interpolation functions, therefore enabling the user to choose the most efficient algorithm depending on which parameter the user is most interested in. From the examples solved the interpolation function that yields the best results has been found. Research limitations/implications -This new technique is very time consuming due to the great number of mathematical calculations that have to be made. However, it yields a solution. Practical implications -The algorithm is able to obtain the solution to the trajectory planning problem for any industrial robot. Also, even mobile obstacles in the workspace could be incorporated at the same time as the robot is moving and creating the path and the time history of motion. Originality/value -It gives a new tool for solving the trajectory planning problem and describes the best interpolation function.
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