Planning Jerk-Optimized Trajectory With Discrete Time Constraints for Redundant Robots

Abstract

We present a method for effectively planning the motion trajectory of robots in manufacturing tasks, the toolpaths of which are usually complex and have a large number of discrete-time constraints as waypoints. Kinematic redundancy also exists in these robotic systems. The jerk of motion is optimized in our trajectory planning method at the meanwhile of fabrication process to improve the quality of fabrication. Our method is based on a sampling strategy and consists of two major parts. After determining an initial path by graph-search, a greedy algorithm is adopted to optimize a path by locally applying adaptive filers in the regions with large jerks. The filtering result is obtained by numerical optimization. In order to achieve efficient computation, an adaptive sampling method is developed for learning a collision-indication function that is represented as a support-vector machine. Applications in robotassisted 3D printing are given in this paper to demonstrate the functionality of our approach.Note to Practitioner Abstract-In robot-assisted manufacturing applications, robotic arms are employed to realize the motion of workpieces (or machining tools) specified as a sequence of waypoints with the positions of tool tip and the tool orientations constrained. The required degree-of-freedoms (DOFs) are often less than the robotic hardware system (e.g., a robotic arm has 6-DOFs). Specifically, rotations of the workpiece around the axis of a tool can be arbitrary (see Fig. 1 for an example). By using this redundancy -i.e., there are many possible poses of a robotic arm to realize a given waypoint, the trajectory of robots can be optimized to consider the performance of motion in velocity, acceleration and jerk in the joint space. In addition, when fabricating complex models each tool-path can have a large amount of waypoints. It is crucial for a motion planning algorithm to compute a smooth and collision-free trajectory of robot to improve fabrication quality. The time taken by the planning algorithm should not significantly lengthen the total manufacturing time; ideally it would remain hidden as computing motions for a layer can be done while the previous layer is printing. The method presented in this paper provides an efficient framework to tackle this problem. The framework has been well tested on our robot-assisted additive manufacturing system to demonstrate its effectiveness and can be generally applied to other robot-assisted manufacturing systems. Fig. 1. An example tool-path for robot-assisted 3D printing system [1], rotation around the red axis can be freely changed because of kinematic redundancy.

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

“…Ma et al [ 22 ] proposed a new convex optimization method, which transforms non-convex jerk into linear acceleration and solves the acceleration limitation problem. Dai et al [ 23 ] used a greedy algorithm to optimize the path of a robot with large jitters during manufacturing tasks, so as to improve its trajectory acceleration performance.…”

Singularity-aware motion planning for multi-axis additive manufacturing

“…Huang et al [24] present an optimization-based planning method for robot-assisted frame structure of 3D printing, which finds feasible fabrication sequence to avoid collision. Dai et al [25] develop an algorithm to preserve discrete time constraints when optimizing jerk behavior for the motion of robotic arm. Differently, our study presented in this paper focuses on parallel multi-axis machines (as shown in Fig.…”

Layer-by-layer generation of optimized joint trajectory for multi-axis robotized additive manufacturing of parts of revolution

“…Optimization strategies that take advantage of the redundancy of robot axes for additive manufacturing work have been little developed to date. Dai et al [41] proposed jerk minimization in the joint space as well as collision detection based on machine learning, but the manufactured parts were limited to 2D-extruded parts. Dharmawan et al [22] proposed trajectory optimization guided by an objective function based on dexterity.…”