The State of Robot Fleet Management Systems from an intralogistics perspective. Part 2: Drivers&Enablers
This is the second part of a publication “The State of Robot Fleet Management Systems from an intralogistics perspective: Technologies, Challenges, Trends”.
Part 1: Introduction and Literature overview
Part 3: Emerging Technologies
1. Demand for innovations from the logistics industry (including manufacturing logistics).
According to the research “Automation in logistics”, the transportation-and-warehousing industry has the third-highest automation potential of any sector worldwide [20].
We already can observe a transition from manual labor to mechanization and automation: automated storage and retrieval systems (Figure 1), conveyors and sortation systems, automated guided vehicles (Figure 2). And nowadays we see a new transition from automation to robotization: autonomous mobile robots for transportation (Figure 3), industrial robots for palleting, packaging, and picking (Figure 4).
Demand from the industry converts into funding for the research and development of new RFM solutions. Companies also tend to share their needs, use-cases, data and also provide an environment for deployment and validation.
Innovations acquired using the aforementioned technologies are primarily new ways to store, move and handle goods in a more predictable and digital-friendly (in terms of connectivity) manner.
Also, a new value appears in the case of AMR usage: significant infrastructure changes are no needed anymore. It brings more flexibility (easy reconfiguration) and fewer costs.
The impact of these innovations on fleet management is taking over the cognitive load from humans because a human cannot satisfy requirements for (1) the growing complexity of making decisions (who when doing what) and (2) the speed of making decisions. More thinking and acting transfer from a human’s head into a computer.
This change entails new forms of supervising and interaction.
2. Automated guided vehicle (AGV) control systems.
First AGVs were introduced in the 1950s by Barrett Electronics of Northbrook. Since then logistics consumes thousands of units reached 110,700 units globally in 2018 [21]. AGVs within a facility can form intensive traffic. To control the traffic preventing collisions and deadlocks vendors tend to provide solutions often called AGV or transportation control systems which is a new way to provide value: set behavior by programming one central unit instead of every AGV within the fleet. But not only AGV vendors supply such solutions. A growing variety of the types and vendors of AGVs creates new niches and opportunities for the companies, e.g. unified vendor-independent control systems such as OpenTCS [22] or FLEETCONTROL [23].
Observing the growth of the AMR market, developers of AGV control systems find new business opportunities and try to adapt their software in order to work with AMRs. From this perspective, AGV control systems act as an ancestor and a springboard for RFMS. It can be considered as an advantage because developers can re-use some parts, like communication protocols or architecture, but at the same time it’s a disadvantage: legacy can impose constraints (sometimes building a new generation from scratch is faster than build upon legacy).
3. Growth of Autonomous Mobile Robots (AMR) market.
AMR market growth was more than 40% in a number of units in 2018–2019 and will continue to grow according to the forecast by the International Federation of Robotics (IFR) [24]. The same as in the AGV industry, each AMR vendor provides its own solution to manage a set of AMRs, in this case usually called Robot Fleet Management Systems (RFMS). Although AMRs less dependent on the infrastructure such as magnetic types and wires, a variety of robots from different vendors may be higher compared to AGVs within one facility. And again we can observe vendor-independent RFMSs from third-party software companies whose promise to manage different kinds of AMR.
As a sub-enabler, public research initiatives (RFMS for general purpose) can be highlighted. Many research projects make steps towards robotics fleet management, especially releasing deliverables public available. Examples are ROOSTER (CoCoAs project) [25], OPIL (L4MS project) [26], Safelog project [27], ILIAD project [28].
Also, private initiatives (RFMS for special cases) must be noticed. For instance, Robotic Middleware for Healthcare (RoMi-H) [29] an initiative driven by the Centre for Healthcare Assistive and Robotics Technology in Singapore. The center develops and maintains an open-source Robotics Middleware Framework that allows managing robots and fleet management systems from different vendors.
The growing market enables the development: the more robots in operation the bigger need for effective control, the more funding and research initiatives, and the more solutions in the end.
4. Robotics competitions
Competitions have two advantages: (1) they discover new talents (people and teams) and (2) provide measurable and unambiguous results. The same as the DARPA Grand Challenge [30] enabled the self-driving cars industry in 2004 or Amazon Picking Challenge series made a significant contribution to the bin picking industry, competitions such as RoboCup Logistics League [31] to push forward robot fleet management technologies. More people are engaged in the topic, they do more research and development, releasing new approaches, solutions, and evaluation methods.
References
[20] Automation in logistics: Big opportunity, bigger uncertainty. Ashutosh Dekhne, Greg Hastings, John Murnane, Florian Neuhaus. 2019.
[21] Global AGV sales volume by type 2018, Statista Research Department. https://www.statista.com/statistics/882696/global-agv-market-volume
[22] The open transportation control system https://www.opentcs.org/en/index.html
[23] TransportControl — control system for AGVs https://www.gsfleetcontrol.com/en/transportcontrol
[24] IFR press release, 2020. https://ifr.org/downloads/press2018/Presentation_WR_2020.pdf
[25] https://doi.org/10.4121/13387985.v1
[26] http://www.l4ms.eu/OPIL
[27] http://safelog-project.eu/index.php/heterogeneous-fleet-management/
[28] https://iliad-project.eu/
[29] https://www.cgh.com.sg/chart/sharp/romi-h
[30] https://www.darpa.mil/about-us/timeline/-grand-challenge-for-autonomous-vehicles
[31] https://ll.robocup.org/
