Behavior and path planning for the coalition of cognitive robots in smart relocation tasks

TL;DR

This paper presents a cognitive approach to multi-robot smart relocation tasks, where robots collaboratively modify the environment to reach shared goals, using semiotic knowledge and hierarchical path planning.

Contribution

It introduces a semiotic knowledge representation and a hierarchical planning methodology for cooperative robot navigation in environment-modifying tasks.

Findings

Effective coordination of robots in environment modification tasks.

Hierarchical planning improves path feasibility and cooperation.

Use of semiotic models enhances behavioral decision-making.

Abstract

In this paper we outline the approach of solving special type of navigation tasks for robotic systems, when a coalition of robots (agents) acts in the 2D environment, which can be modified by the actions, and share the same goal location. The latter is originally unreachable for some members of the coalition, but the common task still can be accomplished as the agents can assist each other (e.g. by modifying the environment). We call such tasks smart relocation tasks (as the can not be solved by pure path planning methods) and study spatial and behavior interaction of robots while solving them. We use cognitive approach and introduce semiotic knowledge representation - sign world model which underlines behavioral planning methodology. Planning is viewed as a recursive search process in the hierarchical state-space induced by sings with path planning signs reside on the lowest level. Reaching this level triggers path planning which is accomplished by state of the art grid-based planners focused on producing smooth paths (e.g. LIAN) and thus indirectly guarantying feasibility of that paths against agent's dynamic constraints.