Constrained Minimum-time Path Planning For Robot Manipulators Via Virtual Knots With Genetic Algorithm

In the last 20 years genetic algorithms (GAs) have been applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling. In this paper a genetic algorithm based technique is proposed to address this problem. The MT (minimum time) is used as a criterion for trajectory generation, by using a real number genetic algorithm as an optimization tool. This paper addresses the planning of trajectories, meaning the development of an algorithm to find a continuous motion that takes the robotic manipulator from a given starting configuration to a desired end position in the workspace without colliding with any obstacle. Cubic spline functions are used for constructing joint trajectories for industrial robots. The motion of the robot is specified by a sequence of Cartesian knots, i.e. positions and orientations of the hand. For an N-joint robot, these Cartesian knots are transformed into N sets of joint displacements, with one set for each joint. Piecewise cubic polynomials are used to fit the sequence of joint displacements for each of the: N joints. Because of the use of the cubic spline function idea, there is only n - 2 equations to be solved for each joint, where n is the number of selected knots Cubic Splines are used in a framework that assures overall continuity of velocities and accelerations in the robot movement. An algorithm is developed to schedule the time intervals between each pair of adjacent knots such that the total traveling time is minimized subject to the physical constraints on joint velocities, accelerations, and jerk.

کلیدواژه ها:

Manipulator Trajectory ، Minimum TimePath ، Genetic Algorithm