TY - JOUR
T1 - Planning Jerk-Optimized Trajectory with Discrete Time Constraints for Redundant Robots
AU - Dai, Chengkai
AU - Lefebvre, Sylvain
AU - Yu, Kai Ming
AU - Geraedts, Jo M.P.
AU - Wang, Charlie C.L.
N1 - Funding Information:
Manuscript received September 16, 2019; revised December 1, 2019; accepted February 12, 2020. Date of publication March 5, 2020; date of current version October 6, 2020. This article was recommended for publication by Associate Editor Z. Xiong and Editor K. Saitou upon evaluation of the reviewers’ comments. This work was supported in part by the Seed Fund of Industrial Design Engineering Faculty, TU Delft; in part by the Natural Science Foundation of China under Grant 61628211; in part by the CUHK Direct under Grant 4055094; and in part by the Grant from the Research Grants Council of the Hong Kong under Project CUHK/14202219. (Corresponding author: Charlie C. L. Wang.) Chengkai Dai is with the Department of Design Engineering, Delft University of Technology (TU Delft), 2628 CD Delft, The Netherlands, and also with the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (CUHK), Hong Kong. Sylvain Lefebvre is with Inria, 54600 Nancy, France.
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - We present a method for effectively planning the motion trajectory of robots in manufacturing tasks, the tool paths 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 filtered 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 robot-Assisted 3-D printing are given in this article to demonstrate the functionality of our approach. Note to Practitioners-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 freedom (DOF) is often less than the robotic hardware system (e.g., a robotic arm has six-DOF). 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 the 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 article 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.
AB - We present a method for effectively planning the motion trajectory of robots in manufacturing tasks, the tool paths 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 filtered 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 robot-Assisted 3-D printing are given in this article to demonstrate the functionality of our approach. Note to Practitioners-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 freedom (DOF) is often less than the robotic hardware system (e.g., a robotic arm has six-DOF). 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 the 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 article 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.
KW - Discrete time constraints
KW - kinematic redundancy
KW - robotic fabrication
KW - trajectory planning
UR - http://www.scopus.com/inward/record.url?scp=85092189608&partnerID=8YFLogxK
U2 - 10.1109/TASE.2020.2974771
DO - 10.1109/TASE.2020.2974771
M3 - Journal article
AN - SCOPUS:85092189608
VL - 17
SP - 1711
EP - 1724
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
SN - 1545-5955
IS - 4
M1 - 9025760
ER -