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What Can Robots Actually Do in a Warehouse?


(Image source: Knapp)

There are a multitude of ‘robots’ available from materials handling suppliers for use in warehouses, offering benefits such as labor savings, faster throughput and greater accuracy. However, are all of these in fact robots and what can they actually do?

The International Federation for Robotics (IFR) adopts the definition set out by the International Organization for Standardization (ISO) in that an industrial robot is an “automatically controlled, reprogrammable, multipurpose manipulator, programmable in three or more axes, which can be either fixed in place or mobile”. This definition tends to be used to describe robots with arms that have two or more joints – but not all warehouse ‘robots’ fit this description. In this article, a slightly looser definition is therefore used – being basically whatever is described by materials handling suppliers and the warehousing trade journals as ‘robots’. A common theme of these ‘robots’ is that they have some degree of autonomy in terms of making their own decisions rather than relying entirely on a central computer system.

A common form of robot is one that can move around the warehouse by itself. Although these may look like automated guided vehicles (AGVs), they are ‘autonomous’ rather than ‘guided’ and can therefore find their own way around obstacles rather than being guided on a set path by, for example, wires buried in the warehouse floor. They normally do this by using cameras and lasers which feed data into light detecting and ranging (LiDAR) systems, linked to simultaneous localization and mapping (SLAM) software, which can create a map of an unknown environment and simultaneously recognize its own position on that map. In this way, AMRs can refer to a digital map of the warehouse and find their way around congested areas, employing algorithms and artificial intelligence (AI). Typical applications are:

  • Autonomous mobile robots (AMRs) used for transporting pallets, cases, or other items from and to various locations in the warehouse.
  • ‘Robotic drive units’, also known as ‘bots’ or ‘robotic butlers’, that bring whole shelf units to a picking station for a person to extract the required goods and then return the shelf unit back to its location.
  • ‘Cobots’, also known as ‘collaborative AMRs’ or ‘chucks’, that can lead human order pickers on the optimum route through the warehouse and may provide instructions on a screen of what and how many items to pick, showing photographs of the required items and lighting the pick locations as required. They may also incorporate ‘put-to-light’ technology indicating to the picker into which tote bin, or carton, to place the goods. The ‘cobots’ would then take the filled tote bins to the packing, or dispatch, area. While this is occurring a further ‘cobot’ may then assist the order picker.

When combined with cameras and vision technology on lift mechanisms, then the AMRs can work in the upper levels of racking, thus acting as:

  • Autonomous lift trucks (ie counterbalanced fork-lift trucks, reach trucks, or narrow-aisle trucks) that can navigate their way around a warehouse and then put away, and retrieve, pallets in the upper levels of racking.
  • Stock-taking robots, which can move along the aisles of warehouses (particularly when the warehouse is closed) in order to check what goods are in each location. These robots may have long vertical masts with cameras at the end so that they can recognize items (or bar codes of items) at high levels in the racking or shelving.

Robots may also be fitted with various handling attachments to perform a wide range of tasks, including:

  • Case picking robots that extract cases from shelving by means of clamps or suction pads. Thus, these robots can find their own way to shelving locations and, with the assistance of for example clamps raised on a scissor lift, may pick a case and take it to dispatch or take it to a manual order-picking station.
  • Miniload storage systems can be served by free-ranging robots (instead of cranes). These robots can work in the aisles both horizontally and vertically, thus putting away and extracting tote bins at any location in that aisle. The robots can also move into different aisles and then move across the warehouse floor to transport tote bins of goods to manual picking stations – even presenting at the ideal picking height by going up a slope or lifting the tote bin with an integral scissor-lift.
  • Robots with hoist mechanisms can serve multi-level grid systems where tote bins are stored one on top of another in a grid frame. The tote bins are inserted and extracted from the top by robots that run on wheels along the top of the grid. They lift the required tote bin or, if there are other tote bins on top of the one required, they would extract these first and relocate them into other columns of the grid frame. The required bin is then transferred to a pick station at the side of the grid frame and, finally, the robots would return the tote bin into an appropriate location in the grid after manual picking has taken place.
  • AMR sorters can be used in the same way as conveyorized sortation systems to sort goods to customer orders after picking. These sorters work in groups (or ‘swarms’) and pick up items and deposit them in the appropriate chutes. They are normally fitted with a tilt-tray or cross-belt mechanism similar to automated sorters.
  • AMRs fitted with scrubbers or sweepers act as robotic floor cleaners that find their way around a warehouse in a similar way to robotic lawnmowers used in gardens.

Most people think of industrial robots as having an arm (or arms) with two or three joints, and this view matches the ISO definition more closely. There are many examples of such robots in warehouses, including:

  • Static robotic pickers which actually pick the goods themselves rather than assisting human pickers, as in the case of most of the examples mentioned so far. These robotic pickers normally have jointed arms with tools such as grippers on the ends. They recognize the orientation of the goods, decide how to grip the required item and then extract it and place in a bin or carton for the appropriate customer order. The technology used to complete these tasks includes camera technology, neural networks and machine learning / deep learning - the latter being forms of artificial intelligence (AI). In such goods-to-robot systems, the required tote bins can be brought to the robotic pickers from mini-load storage by means of conveyors (or indeed free-ranging AMRs).
  • Robotic put-walls are similar except that each customer order is represented by a bin – which is normally arranged in a circular multi-level fashion around the robot so that the robot can easily reach each bin with its joint arm.
  • Mobile piece-picking robots combine the jointed arm/gripper features of static robotic pickers with the autonomous movement abilities of AMRs. In this way, mobile piece-picking robots can traverse the warehouse to visit pick locations and extract the relevant items required for a customer order (or orders).
  • Robotic case unloading systems can be used to extract cartons that have been loose loaded into shipping containers, using similar technology. This type of equipment can enter a container and unload the cartons using a robotic arm with grippers or suction pads. The goods may then be placed by the robot on an extendible conveyor to transfer the goods to the storage area or wherever they are next needed.
  • Robotic palletizers also normally have jointed arms and grippers or suction pads. These can stack cases onto pallets in the most effective way to make the best use of space, avoid crushing sensitive items and achieve a stable load.
  • Similarly, robotic tote bin loaders can be used to load tote bins onto dollies (ie wheeled platforms used to transport tote bins to shops).

Robots are thus now being used in almost every aspect of warehouse operations. Whereas traditional automation normally requires major capital investment and lengthy implementation timescales, robots can often be added in an incremental fashion and thus avoid these difficulties. In addition, there is a wide range of options as to the degree of reliance on robots – for example, they can be used as ‘cobots’ in collaboration with human pickers through to fully robotic piece picking AMRs that traverse the warehouse and pick the goods themselves.

Whilst ‘swarms’ of robots controlled by artificial intelligence may not be a common sight in warehouses today, these may well become the norm in the future. This obviously has significant implications for warehouse staffing levels and recruitment needs, as well as for future information technology, maintenance and operational management methods – particularly as deep learning and other forms of artificial intelligence are developed further.

Further details on the full range of options for warehouse operations, including manual, automated and robotic solutions, can be found in The Handbook of Logistics and Distribution Management, 7th Edition.