Robotics in logistics: the path to efficiency. By Chris Froud

Picking and placing orders in large warehouses, where items can be stored far apart from one another, can mean travelling significant distances. As such, relying solely on human operators can limit efficiency due to their walking speed. In many warehouses, robots like those manufactured by Locus Robotics are ‘doing the leg work’ to reduce the distances human operators must walk, in turn improving efficiency.

Using a team of robots (18), items can be transported across the warehouse much more quickly than human operators (50) could walk. This means they can spend more time picking and placing items, which they still tend to be better at than robots.

The robots (18) have a display (48) which instructs the human operator (50) to, for example, retrieve a particular item from a shelf and place it into a tote (44) on the robot (18). The robot (18) will then take care of transporting the tote (44) to another part of the warehouse in order to pick another item or prepare the items for despatch.

To allow the robots to navigate the warehouse accurately, each must have a map of item locations, as well as any obstacles, such as walls and shelves. The robots build and update their own map using a process called simultaneous localisation and mapping (SLAM) by constantly scanning the warehouse environment with a laser scanner as they travel.

By using the map and a path planning algorithm, the robots are able to plot a route to their desired location. Previously, such algorithms sought to minimise path length and the risk of colliding with fixed obstacles.

However, until now, no attempt had been made to plan a route which also avoided putting the robot on a collision course with other robots. In a busy warehouse, there might be dozens of robots moving around the space, and each had to constantly modify its route to avoid collisions or wait for other robots to move out of the way. This reduced efficiency overall.

Locus Robotics’ recently granted European patent EP 3,437,880 B1 provides a smart solution to this problem by allowing each robot to take other robots’ travel plans into account. This allows each robot to proactively plan its route, factoring in the possibility of other robots crossing its path.

To do this, the robot (640) has a map (600) which not only identifies known collision risks with fixed obstacles, such as shelving units (602), but also represents the travel plans of other moving robots (620, 622).

Along the planned travel paths of other robots (620, 622) are shown superimposed cost images (630, 632) indicating the risk of a collision along the path. The darker pixels denote a high risk of collision, gray pixels indicate that the potential hazard is further away, and white pixels indicate low or no risk of a collision. The width of the superimposed cost images relates to the width of the robots, including an allowance for a buffer zone to enable safe passing.

The path-finding algorithm can use the map (600) to evaluate the risk of collision between fixed obstacles and other robots along any given path. Therefore, a more efficient overall journey can be planned and completed, with less likelihood that the path will have to be re-evaluated during travel.

This improved algorithm gives Locus Robotics’ robots a competitive edge in the marketplace. Having secured patent protection for the technology, competitors are unable to use it for up to 20 years. This period of market exclusivity will help to boost the enterprise value of the business and enable it to stay ahead of the competition for many years to come.

Chris Froud is a senior associate and patent attorney at European intellectual property firm Withers & Rogers. He specialises in advising innovators in electronics and computing, including robotics and autonomous systems.


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