You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.
The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.
For the reports in this repository we specifically note that
- the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
- the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
- technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
- in other cases, please contact the copyright owner for detailed information
By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.
If you are in doubt, feel free to contact webmaster@ide.mdh.se
Task Roadmaps: Speeding up Task Replanning
Publication Type:
Journal article
Venue:
Frontiers in Robotics and AI
DOI:
10.3389/frobt.2022.816355
Abstract
Modern industrial robots are increasingly deployed in dynamic environments, where
unpredictable events are expected to impact the robot’s operation. Under these
conditions, runtime task replanning is required to avoid failures and unnecessary
stops, while keeping up productivity. Task replanning is a long-sighted complement to
path replanning, which is mostly concerned with avoiding unexpected obstacles that can
lead to potentially unsafe situations. This paper focuses on task replanning as a way to
dynamically adjust the robot behaviour to the continuously evolving environment in which it
is deployed. Analogously to probabilistic roadmaps used in path planning, we propose the
concept of Task roadmaps as a method to replan tasks by leveraging an offline generated
search space. A graph-based model of the robot application is converted to a task
scheduling problem to be solved by a proposed Branch and Bound (B&B) approach and
two benchmark approaches: Mixed Integer Linear Programming (MILP) and Planning
Domain Definition Language (PDDL). The B&B approach is proposed to compute the task
roadmap, which is then reused to replan for unforeseeable events. The optimality and
efficiency of this replanning approach are demonstrated in a simulation-based experiment
with a mobile manipulator in a kitting application. In this study, the proposed B&B Task
Roadmap replanning approach is significantly faster than a MILP solver and a PDDL based
planner.
Bibtex
@article{Lager6444,
author = {Anders Lager and Giacomo Spampinato and Alessandro Papadopoulos and Thomas Nolte},
title = {Task Roadmaps: Speeding up Task Replanning},
volume = {9},
pages = {1--14},
month = {April},
year = {2022},
journal = {Frontiers in Robotics and AI},
url = {http://www.es.mdu.se/publications/6444-}
}