ASTRON: Action-Based Spatio-Temporal Robot Navigation

Abstract

To achieve the tasks provided by a user, it is necessary for robots to have a plan that fully exploits their functionalities in an environment. The objective of this study is to realize robot task planning in real space for effectively use of the robot's functions The plan is formed by deriving a feasible action sequence by interpreting the instructions within the scope of the action possibilities of the robots and the changes in them. In this paper, we first propose an action graph as a novel environmental representation approach to facilitate the understanding of the robot's action possibility in real space. In the action graph, the action possibility is represented by nodes, which describe the spatial position to perform each feasible action, and edges, which describe the feasible actions, based on the subsystem-level affordance and the arrangement of objects in the environment. We also propose an action-based spatio-temporal robot navigation (ASTRON), which focuses on robot navigation tasks. ASTRON enables the robots to determine a feasible action sequence that utilizes their functions by interpreting the instructions based on the action graph. The effectiveness of the proposed method was evaluated through simulations and actual machine experiments in a coffee shop environment. In the actual machine experiments, the proposed method was applied to robots with different subsystem configurations. The experimental results demonstrated that the proposed method could plan the feasible action sequence to complete the tasks by considering the environmental state and the subsystem configurations of the robot.

Multi-Agent Action Graph Based Task Allocation and Path Planning Considering Changes in Environment

“…However, it is difficult to achieve planning that considers environment changes in advance, as in our study, because the agents affected by the change are replanned. Kawasaki et al [14] proposed a robot navigation system that considers the environment changes associated with task execution. Similar to our work, this work can plan in advance considering environment changes; however, because of the structure of the graph, it can only be applied to singlerobot planning.…”