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Global Minimum-Jerk Trajectory Planning of Space Manipulator
Panfeng Huang,Yangsheng Xu,Bin Liang 대한전기학회 2006 International Journal of Control, Automation, and Vol.4 No.4
A novel approach based on genetic algorithms (GA) is developed to find a global minimum-jerk trajectory of a space robotic manipulator in joint space. The jerk, the third derivative of position of desired joint trajectory, adversely affects the efficiency of the control algorithms and stabilization of whole space robot system and therefore should be minimized. On the other hand, the importance of minimizing the jerk is to reduce the vibrations of manipulator. In this formulation, a global genetic-approach determines the trajectory by minimizing the maximum jerk in joint space. The planning procedure is performed with respect to all constraints, such as joint angle constraints, joint velocity constraints, joint angular acceleration and torque constraints, and so on. We use an genetic algorithm to search the optimal joint inter-knot parameters in order to realize the minimum jerk. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation result shows that GA-based minimum-jerk trajectory planning method has satisfactory performance and real significance in engineering.
Robust People Counting in Complicated Situations
Zhi Zhong,Ning Ding,Weizhong Ye,Xinyu Wu,Yangsheng Xu 한국과학기술원 인간친화 복지 로봇 시스템 연구센터 2008 International Journal of Assistive Robotics and Me Vol.9 No.2
People counting is a basic yet important aspect in intelligent video surveillance. In some places full of moving pedestrians or objects, it is a real challenge for traditional image processing approaches. This paper addresses the issue of how to enhance the robustness of people counting in two complicated situations, one is to count individuals mixed with objects and the other is to count people in a crowded scene. Based on modeling method and machine learning, we employ a new way to tackle the problem, which reduces the error rate and the miss rate dramatically. Experimental results have demonstrated the robust function of the system.
Robust People Counting in Complicated Situations
Zhi Zhong,Ning Ding,Weizhong Ye,Xinyu Wu,Yangsheng Xu 동국대학교 정보융합기술원 2008 International Journal of Assistive Robotics and Sy Vol.9 No.2
People counting is a basic yet important aspect in intelligent video surveillance. In some places full of moving pedestrians or objects, it is a real challenge for traditional image processing approaches. This paper addresses the issue of how to enhance the robustness of people counting in two complicated situations, one is to count individuals mixed with objects and the other is to count people in a crowded scene. Based on modeling method and machine learning, we employ a new way to tackle the problem, which reduces the error rate and the miss rate dramatically. Experimental results have demonstrated the robust function of the system.