Cartesian-Space Motion Planning for Autonomous Construction Machines

摘要

Cartesian-Space Motion Planning for Autonomous Construction Machines Matteo Ragaglia, Alfredo Argiolas and Marta Niccolini Pages 983-990 (2017 Proceedings of the 34rd ISARC, Taipei, Taiwan, ISBN 978-80-263-1371-7, ISSN 2413-5844) Abstract: Nowadays, in the construction industry, several operations that require both high power and high accuracy (such as panel positioning, plumbing, material handling) are still manually performed by human workers in very inefficient and dangerous ways. After a thorough field investigation, we believe that the construction industry could definitely benefit from the introduction of Autonomous Construction Manipulators (ACM) in terms of both productivity and human workers’ safety. Among the huge number of functionalities that are required to develop an ACM, motion planning plays a fundamental role. As a matter of fact, the planner has the responsibility to compute a trajectory that allows the ACM to move its end-effector from its original Cartesian pose to a desired one, while satisfying kino-dynamic constraints and avoiding collision with obstacles. Traditionally, this kind of motion planning problem is solved using Joint-Space randomized algorithms [1-2], that unfortunately tend to output quite counterintuitive trajectories from the point of view of a human being. Given the fact that in construction sites humans and machines usually work side-by-side and considering the results presented in [3], it is clear that the intuitiveness of the planned trajectories can have a strong impact on the comfort, the productivity and the level of safety perceived by human workers sharing their workspace with an ACM. In order to avoid these drawbacks, a possible solution is represented by addressing the motion planning problem directly at the Cartesian-Space level. We here propose a Cartesian-Space randomized algorithm that consists in a modified version of the RRT algorithm that allows the tree to be extended directly towards the goal, as it has been originally proposed in [4]. Once the tree reaches the goal, a path shortening procedure is performed to reduce the length of the output trajectory while keeping it collision-free. Then, a spline-based interpolation is used to eliminate Cartesian-Space velocity discontinuities. Finally, the final Joint-Space trajectory is obtained using inverse kinematics and kinematic scaling (to guarantee that joint velocities do not exceed their lower and upper bounds). The proposed algorithm has been tested and validated in a simulated scenario, using a UR5 manipulator as test bench. [1] S. M. LaValle and J. J. Kuffner, “Randomized kinodynamic planning,” International Journal of Robot Research, vol. 20, no. 5, pp. 378–400, 2001; [2] S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” International Journal of Robot Research, vol. 30, no. 7, pp. 846–894, 2011; [3] A. Zanchettin, L. Bascetta, and P. Rocco, “Achieving humanlike motion: Resolving redundancy for anthropomorphic industrial manipulators,” Robotics Automation Magazine, IEEE, vol. 20, no. 4, pp. 131–138, 2013; [4] Lacevic, B. & Rocco, P. Safety-oriented path planning for articulated robots Robotica, Cambridge University Press, 2013, 31, 861–874; Keywords: Robotics, Motion Planning, Construction Machines DOI: https://doi.org/10.22260/ISARC2017/0136 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley