Project Description

General Idea

Imagine a social or an industrial facility of the future (e.g., a hotel, an office, a hospital, or a warehouse) where a team of robots have been deployed in order to provide services and accomplish everyday tasks such as object handling/transportation, or pickup and delivery operations. In such a context, different robots (e.g., mobile platforms, static manipulators, or mobile manipulators) with different actuation, manipulation and perception capabilities must be coordinated in order to achieve various complex tasks that require collaborative actions with each other and with human operators. Thus, the effective and efficient supervision and coordination of the overall heterogeneous system mandates a decentralized framework that integrates high-level task-planning, low-level motion planning and control and robust, real-time sensing of the robots’ dynamic environment.

The World Robotics Survey1 points out that “…service robots for professional use boom…”, with indoor logistics being a major market. Sales of professional service robots in the logistics segment registered a growth of 28% in 2014 with the value of sales increasing to USD 2.2 billion. This shows the huge opportunity for European companies to address this area and satisfy the market demand. Emerging markets for indoor logistics robotics are offices, hotels and hospitals, where a close interaction between the robots and humans is required. For example, studies have given ground to the estimate that “logistics-related activities account for over 40 percent of a hospital's spending”. and that medical staff spends up to 20% of their time on transportation tasks. Automating these tasks leads to great opportunities for cost savings. Indeed, several automated guided vehicles (AGVs) are already on the market and are operated in hospitals, including TUG by Aethon, MIR100 by Mobile Industrial Robots, UNITR, by MT Robot AG, or QC Bot by Vecna Technologies.

Considered Cases 

Motivation

Current practice in coordination of robotic teams is at a great deal based on offline, centralized planning and related tasks are almost exclusively fulfilled in a predefined manner. Only a little room is allowed for real- time and coordinated decentralized actions. We argue that this does not utilize the capabilities of the multi- robot system to operate efficiently in a crowded and dynamic environment in an optimal manner. In most cases, sudden changes in the environment, the type of assigned tasks, and the need for coordination, e.g., due to insufficient capabilities, would cause the system to halt, ask for a human intervention and restart. Despite the fact that public facilities are in some degree pre-structured, we argue here that the need for a framework for decentralized, real-time, automated task (re)-planning for semi-autonomous systems is evident in a twofold manner: (i) it will pave the way to an improved use of resources and a faster accomplishment of tasks inside public facilities and workspaces with high social activity; (ii) it will make an important contribution towards the vision of more flexible multi-robot applications in both professional or domestic environments, also in view of the “Industry 4.0” vision and the general need to deploy such systems in everyday life scenarios.

Based on the above ascertainments, our motivation for the Co4Robots proposal comes from two facts:

Task Coordination 
BOSCH lab 

Objectives

Imagine a scenario where multiple robots have been deployed to provide services such as object handling/transportation, or pickup and delivery operations. In such a context, different robots with varying capabilities must be coordinated in order to achieve various multi-tasking procedures.

Thus, the effective supervision and coordination of the overall heterogeneous system mandates a decentralized framework that integrates high-level task-planning, low-level motion control and robust, real-time sensing of the robots’ dynamic environment. Current practice is at a great deal based on offline, centralized planning and related tasks are usually fulfilled in a predefined manner: this does not utilize the capabilities of the system to operate efficiently in a dynamic environment. In most cases, sudden changes in the environment, the type of tasks, and the need for coordination, would cause the system to halt, ask for human intervention and restart. Despite the fact that public facilities are in some degree prestructured, the need for a framework for decentralized, real-time, automated task (re)-planning is evident in a twofold manner:

Within Co4Robots our goal is to build a systematic methodology to accomplish complex specifications given to a team of potentially heterogeneous robots; control schemes appropriate for the mobility and manipulation capabilities of the considered robots; perceptual capabilities that enable robots to localize themselves and estimate the state of the dynamic environment; and their systematic integration approach.

Cases Table