Knowledge-oriented task and motion planning for multiple mobile robots

ABSTRACT

Robotic systems composed of several mobile robots moving in human environments pose several problems at perception, planning and control levels. In these environments, there may be obstacles obstructing the paths, which robots can remove by pushing or pulling them. At planning level, therefore, an efficient combination of task and motion planning is required. Even more if we assume a cooperative system in which robots can collaborate with each other by e.g. pushing together a heavy obstacle or by one robot clearing the way to another one. In this paper, we cope with this problem by proposing $\kappa$-TMP, a smart combination of an heuristic task planner based on the Fast Forward method, a physics-based motion planner, and reasoning processes over the ontologies that code the knowledge on the problem. The significance of the proposal relies on how geometric and physics information is used within the computation of the heuristics in order to guide the symbolic search, i.e. how an artificial intelligence planning method is combined with low-level motion planning to achieve a feasible sequence of actions (composed of collision-free motions plus physically-feasible push/pull actions). The proposal has been validated with several simulated scenarios (using up to five robots that need to collaborate with each other to reach the goal state), showing how the method is able to solve challenging situations and also find an efficient solution in terms of power.

KEYWORDS:

• Task and motion planning
• manipulation planning
• knowledge-based representation
• reasoning process

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http:/protege.stanford.edu/.

2. OWL files are accessible at: https:/sir.upc.edu/projects/ontologies/.

Additional information

Funding

This work is partially supported by the Spanish Government through the project DPI2016-80077-R. Aliakbar Akbari is supported by the Spanish Government through the grant FPI 2015.