Towards Safe and Aggressive Motion Generation for Dynamic Targets Pick-and-Place
Jun Shao, Jianfeng Liao, Han Li, Haoyang Zhang, Shiqiang Zhu, Wei Song, Yinchun Huang
Abstract
In this paper, we present a framework to generate time-optimal trajectories for dynamic target pick-and-place tasks. We develop an optimization-based trajectory generation method for manipulators, which can conduct spatial-temporal deformation under user-defined requirements. We formulate the problem of dynamic target pick-and-place, in which the trajectory duration and jerk are optimized and terminal states are adjusted instead of being fixed. The motions are constrained within the mechanical limits and to avoid collisions. Constraints transcription is adopted to convert constraints to weighted penalties. Then the problem can be solved based on the trajectory generation method with a high-level optimizer. We integrate the proposed method with online perception into a robot arm platform, in which a conveyor belt is used to transport the objects. Simulations and real-world experiments are conducted under a range of object speeds. Results show that the proposed method achieves online grasping under the object velocity up to 0.5m/s with an average computing time of 190ms.
BibTeX
@inproceedings{iros2023_towardssafeandag,
title = {Towards Safe and Aggressive Motion Generation for Dynamic Targets Pick-and-Place},
author = {Jun Shao and Jianfeng Liao and Han Li and Haoyang Zhang and Shiqiang Zhu and Wei Song and Yinchun Huang},
booktitle = {IROS 2023},
year = {2023}
}