Abstract
Abstract In this paper, we propose a realization method for a rearrangement task involving multiple movable objects and mobile robots. All of the objects are transported from their initial positions to goal positions. It is important for mobile robots to cope with map errors in order to execute tasks in a real environment. However, a rearrangement task is a very complicated process involving constraints related to transportation order and scarcity of spatial resources. Therefore, it is difficult to develop an adequate method to deal with map errors and still maintain task performance. We primarily address two questions: what kinds of tactics are needed and when these tactics should be applied. Our method involves adopting a problem-partitioning structure that divides a complicated rearrangement problem into simple subproblems. Furthermore, we design a real mobile robot group. Using the realization method and real robot group, we conduct experiments involving a rearrangement task in an actual environment. The results show that our proposed method can cope with map errors quickly, while maintaining task realization performance.
Highlights
These days, mobile robots are expected to execute tasks with increasing variety and complexity
A rearrangement task by multiple robots is a fundamental task that is involved in various applications
It is very complicated with constraints involving transportation order and scarcity of spatial resources
Summary
These days, mobile robots are expected to execute tasks with increasing variety and complexity. Among these tasks, rearrangement tasks are fundamental. Robots transport objects from initial positions to goal positions. Rearrangement tasks are used in various applications, such as product transportation in warehouses [1] and clean-up tasks in indoor situations [2,3]. In this figure, each box labeled with an “R” indicates a robot. Other boxes indicate movable objects, and the black squares indicate obstacles. Objects are located in their initial positions, as shown in “Initial state,” and in their goal positions, as shown in “Goal state”
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