Optimizing Human-Robot Collaboration: A New Decision Support System
Autonomous mobile robot teams have become increasingly popular in manufacturing and other industries, as they offer improved productivity and reduced risk to human workers. However, as the number of robots increases, it becomes increasingly difficult for human operators to supervise and assist them all. This is where decision support systems (DSS) come in, providing assistance to human operators in managing multiple robots.
The Problem and Solution
In this project, we present a new DSS for a multi-robot system comprising a fleet of autonomous robots and a human operator. The robots navigate through a series of tasks, each with different completion times depending on whether they are executed autonomously or under teleoperation. The human operator can assist/teleoperate at most one robot at a time. The goal of the DSS is to provide the operator with a teleoperation schedule that minimizes the completion time of all robots' tasks.
Modeling the Problem
The problem is formulated as a Mixed Integer Linear Program and solved optimally for small to moderate sized problem instances.
The Algorithm
An anytime algorithm is also developed that makes use of the problem structure to provide a fast and high-quality solution, even for larger problem instances. The algorithm identifies "blocking tasks" in greedily-created schedules and iteratively removes those blocks to improve the quality of the solution.
Evaluation and Results
Through numerical simulations, it is demonstrated that the proposed algorithm is an efficient and scalable approach that outperforms other greedy methods.
Advantages of the New DSS
The new DSS offers several advantages compared to existing solutions. First, it is able to handle larger problem instances, making it suitable for use in real-world scenarios. Second, it is able to provide high-quality solutions in a timely manner, ensuring that human operators are able to effectively assist robots in completing their tasks. Third, it is an efficient and scalable approach, meaning it can be adapted for use in different environments and with different types of robots.
Conclusion
In conclusion, the new DSS presented in this project offers a promising solution for addressing the challenges of human-robot collaboration in uncertain environments. It is an efficient, scalable and effective decision support system that can assist human operators in managing multiple robots. As the field of robotics continues to advance, we can expect to see more developments in this area and a greater reliance on human-robot collaboration.
Future Work
The proposed DSS is a step towards efficient and effective human-robot collaboration in various settings. However, there is still room for improvement and further research. For example, the approach can be tested and evaluated in more realistic scenarios, such as a warehouse or search-and-rescue operation. Additionally, the DSS could be extended to consider more complex decision making processes, such as incorporating human preferences and priorities. Overall, the goal is to continue to improve the effectiveness and efficiency of human-robot collaboration in various settings.
