The successful adoption of mobile robotics requires an investment in charging infrastructure that considers the best methods to support daily operations. Mobile robots and lithium batteries have already brought substantial efficiency gains and operational changes that are redesigning workflows and floorplans. However, reaching the next breakthrough and broader adoption will require autonomous charging systems to fully streamline operations.
To do so, original equipment manufacturers (OEMs), facility owners, and mobile robot fleet managers must weigh three significant factors:
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Where to charge
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When to charge
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How to charge
These variables cannot be an afterthought, as they significantly affect how mobile robot fleets can and will be managed. When building new mobile robots and warehouses—or renovating old ones—these variables must be weighed to create purpose-built, autonomous charging infrastructure for cutting-edge and future-proof warehouses.
1. Where to Charge?
The first thing OEMs, facilities, and fleet managers must consider before adopting autonomous mobile robot (AMR), and autonomous guided vehicle (AGV) technologies are where charging will occur.
Mobile warehouse equipment (e.g., Class I lift trucks) traditionally required dedicated charging rooms. This was due to the sheer amount of space necessary to house the equipment, chargers, and extra lead-acid batteries. However, this is no longer the case following advancements in lithium battery technologies and the premium on warehouse space.
Still, where charging will occur significantly impacts ongoing workflows in the immediate area for employees and other mobile robots. To simplify the decision, you can consider centralized vs. decentralized charging infrastructure.
Centralized Charging Infrastructure
Suppose you operate a warehouse facility that relies on a central location that mobile robots repeatedly return to (e.g., a conveyor belt for shipment labeling and processing). It would likely make the most sense to organize the chargers nearby. Then, between performing their “picker” tasks, the robots will already be in a central location and can easily charge as needed.
Factors to consider when evaluating centralized charging for AMRs and AGVs include:
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How far do they travel during their use?
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How long is their average usage?
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How many can be in use at one time while others charge?
Based on the movement flow and work processes around your facilities, centralizing your charging infrastructure in one strategic area may be the most beneficial approach.
Decentralized Charging Infrastructure
You should consider decentralized charging if:
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Your robots travel long distances
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Near-continuous use discharges battery capacity down to 30%
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Operations cannot pause
Decentralized charging disperses charging locations throughout your facility so mobile robots can more easily charge as needed based on their current location. This method leverages natural breaks or pauses in activity to charge for shorter periods—“opportunity charging.”
To evaluate decentralized charging infrastructure, ask yourself:
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Would directing all mobile robots in need of charging to a central location disrupt the everyday work and movement flows around my facility?
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Do mobile robots need to be deployed until they complete a larger task (e.g., floor cleaning)?
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Will mobile robots discharge their capacity too low to return to a central location, risking the battery’s health and longevity?
This is a relatively recent advantage that lithium battery technologies and sophisticated charging systems have introduced. With lead-acid batteries, opportunity charging was not possible.
2. When to Charge
When building robust infrastructure with autonomous charging capabilities, determining when your mobile robots charge is the next factor to consider. Advancements in lithium battery technology allow for opportunity charging, so there are few if any limitations from the batteries or chargers as to when the process needs to occur. Instead, the determining factors are:
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Your artificial intelligence (AI) and machine learning program
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Any applicable telematic-based manual overrides
Additionally, your decision on whether to build a centralized or decentralized charging infrastructure will affect this, which determines the number of nearby charging stations needed.
Determining When to Charge with AI Programming
The AI systems that govern your AGV and AMR vehicles can be programmed to return to physical or wireless inductive charging stations. In theory, this readily enables quick recharging and return to operation. However, additional variables must be accounted for in the AI’s decision-making regarding when (and where) charging should occur for full automation.
For example, what happens if other mobile robots already occupy all nearby charging stations?
Or, on a deeper level: what happens if the AI system recognizes two mobile robots that need to charge simultaneously and must evaluate which one to send to the nearest available charging station? How does it select another station for the one remaining?
Per a 2019 study, the four primary considerations that factor into AI’s heuristic decision-making for which charging station to choose are:
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The nearest charging station
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Which station will result in the fastest charging time, including travel time and queueing
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Charging stations along the current path of an AMR or AGV
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The farthest reachable charging station
Unless you design your facility to accommodate significantly greater charging station availability than the number of mobile robots active on facility floors at a given time, these advanced decision-making capabilities must be incorporated into the AI’s processing. Mobile robots competing for charging stations will only cause operational delays and breakdowns.
Manual Overrides with Telematics
While building autonomous charging infrastructure is the goal, you should also ensure that fleet managers can manually override AI decisions based on telematic data when appropriate.
Telematics is the communication of critical operational data (e.g., remaining battery capacity, overall battery health, sensor failures) back to fleet management personnel.
The mobile robot fleet management personnel and data engineers you employ will occasionally need to suspend a given unit’s workflow. For example, they may need to perform predictive maintenance or initiate charging before the AI will. These actions require adequate access and authorization to override the system at times.
Centralized charging infrastructure can better enable this process. There will be one location to call robots back to rather than fleet management personnel having to go to individual, decentralized charging stations.
3. How to Charge—Inductive Charging
Finally, OEMs and facility owners will need to determine how mobile robots charge when their shift ends or when other possibilities for opportunity charging arise. For fully autonomous charging, your facility and mobile robots would likely need to support sophisticated contact-based options or wireless methods (e.g., magnetic resonance charging).
The choice between contact-based and wireless solutions entirely depends on facility owners’ needs and per-unit costs.
Contact-Based Charging Methods
Contact-based charging solutions that can enable autonomous operations include:
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Guided plate-to-plate – Robots are guided through a docking process that aligns large conductive plates, connecting their on-board battery and charging technology with a power source.
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Automated – Whereas large plates facilitate easy docking, automated charging utilizes physically smaller connections that are much harder to align. AI will need to guide the charger connection through three axes. This is the most sophisticated and technologically demanding contact-based solution but is the best for enabling on-board charging.
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Battery swapping – Involves removing a depleted battery directly from the robot and replacing it with a fully-charged battery. Before the robot returns, the removed battery is charged. This method can be manual or automated depending on the technologies used.
Some contact-based methods do still require manual intervention, but the fleet management personnel already on-site can complete those tasks.
Wireless Charging Methods
Wireless charging relies on an electromagnetic field to transmit energy. It only requires proximity to facilitate the charging process; this is an obvious advantage for achieving fully autonomous operations. The AI merely needs to direct units to stop above a pad or nearby the charging station to top off their batteries.
Although the wireless charging process is simple and requires the least manual intervention, the components must be engineered precisely. Generating the electromagnetic field that transmits power requires a copper coil in the robot and charger. These coils must match each other as close as possible to perform charging efficiently (e.g., size, alignment, gaps).
However, wireless charging is less efficient with energy consumption regardless of coil engineering precision. And efficiency decreases the further mobile robots are from the charging station. Therefore, facilities and stations must be designed to accommodate the closest possible charging proximity to achieve maximum efficiencies with wireless options.
Examples of Wireless Charging
The technology for wirelessly charging large lithium batteries already exists. It is already in use—at both the consumer and enterprise level—and is continuously being improved.
In 2017, Daihen, a Japanese robotics company, began including wireless inductive charging with their AGVs. Many EVs already utilize inductive charging, and the technology has progressed enough that a pilot program for making existing roadways into inductive chargers will be launched near Detroit, MI in 2023. It is possible that these capabilities could soon be used to support inductive charging across an entire facility’s floor.
Meet All Three Considerations for a Fully Autonomous Charging System
There are two foundational steps to achieving full autonomy: determining where to place your charging stations and developing advanced AI systems that decide when to charge and at which locations.
Once these steps are achieved, it is not too challenging to rely on fleet management personnel to connect mobile robot units and the chargers at these stations.
However, achieving fully autonomous charging systems will require OEMs and battery charging manufacturers to continue to create sophisticated and economically viable induction charging possibilities.
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About Delta-Q Technologies
Delta-Q Technologies is charging the future and driving the world’s transition into electric energy! We collaboratively design, test, and manufacture robust battery chargers, that improve the performance of our customer’s electric drive vehicles and industrial machines. As the supplier of choice for Tier 1 OEMs, they use their values, perseverance, and engineering expertise to guide their customers through the electrification process for a sustainable world.
They are part of the Zapi Group of companies and headquartered in Vancouver, Canada. Delta-Q’s team and distribution span five continents to service industries such as electric golf cars, lift trucks, aerial work platforms, e-mobility, floor care machines, utility/recreational vehicles, and new markets, like outdoor power equipment, marine and construction equipment. For more information, please visit www.delta-q.com or follow company updates on Twitter and LinkedIn.
