NDLI: A hierarchical and adaptive mobile manipulator planner with base pose uncertainty

Content Provider	Springer Nature Link
Author	Pilania, Vinay Gupta, Kamal
Copyright Year	2015
Abstract	We present a hierarchical and adaptive mobile manipulator planner (HAMP) that plans for both the base and the arm in a judicious manner—allowing the manipulator to change its configuration autonomously when needed if the current arm configuration is in collision with the environment as the mobile manipulator moves along the planned path. This is in contrast to current implemented approaches that are conservative and fold the arm into a fixed home configuration. Our planner first constructs a base roadmap and then for each node in the roadmap it checks for collision status of current manipulator configuration along the edges formed with adjacent nodes, if the current manipulator configuration is in collision, the manipulator C-space is searched for a new reachable configuration such that it is collision-free as the mobile manipulator moves along the edge and a path from current configuration to the new reachable configuration is computed. We show that HAMP is probabilistically complete. We compared HAMP with full 9D PRM and observed that the full 9D PRM is outperformed by HAMP in each of the performance criteria, i.e., computational time, percentage of successful attempts, base path length, and most importantly, undesired motions of the arm. We also evaluated the tree versions of HAMP, with RRT and bi-directional RRT as core underlying sub-planners, and observed similar advantages, although the time saving for bi-directional RRT version is modest. We then present an extension of HAMP (we call it HAMP-U) that uses belief space planning to account for localization uncertainty associated with the mobile base position and ensures that the resultant path for the mobile manipulator has low uncertainty at the goal. Our experimental results show that the paths generated by HAMP-U are less likely to result in collision and are safer to execute than those generated by HAMP (without incorporating uncertainty), thereby showing the importance of incorporating base pose uncertainty in our overall HAMP algorithm.
Starting Page	65
Ending Page	85
Page Count	21
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	39
Issue Number	1
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2015-01-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mobile manipulation Planning under uncertainty Motion planning Navigation Adaptive Robotics and Automation Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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