3D time-of-flight (3D-TOF) sensor is capable of detecting distance by illuminating the scene with modulated light and then measuring the phase delay of reflection. The phase delay is proportional to distance. Different from LiDAR which illuminates the scene with a point source, 3D-TOF sensor illuminates the scene with a broad beam, similar to the difference between a laser pointer and a flood light. As result, LiDAR generates frames by scanning the scene point by point, while 3D-TOF sensor captures the scene frame by frame making it more suitable for working in a dynamic environment. This video provides more details of 3D-TOF sensor works.
3D-TOF sensors application in logistics is immense. As consumers are buying more goods online, responsive order fulfillment becomes more critical, and logistics optimization becomes increasingly important. In this article, I will give an overview of the logistic process and explore applications that can most benefit from 3D-TOF sensors.
Logistics can be viewed as a network shown in Figure 2. Suppliers provide different goods that are transported to regional collection centers where they sorted and bundled and transported to logistic centers (warehouses) for storage. When orders come in, stored goods are retrieved, unbundled, picked and repackaged for delivery. The goods are sorted according to regions and shipped to regional distribution stations, and finally local carriers deliver the goods to customers.
When a package is picked up, a bar code label is applied and scanned for tracking, then the package is transported to a collection station or a warehouse for receiving and sorting. Receiving and sorting may scan the goods again, checking for defects, and sorting the goods by category for storage. In some cases, goods are first bundled into pallets before they are stored. Shelving and retrieving pallets require forklifts. When an order comes in, goods are retrieved from the shelves, unbundled, and specific items are picked for shipping. Shipments to the same region are re-bundled into pallets and loaded onto the trucks. Figure 3 shows the typical flow. It should be noted that the left half of the flow is related to packing, transporting and tracking of goods, while the right half is related to sorting, storing and retrieving, part of logistic center and warehouse operation.
The right half of the logistic flow are logistic centers and warehouses operations. These facilities are central hubs that route goods from suppliers to customers, and serve as an inventory buffer. Typical logistic center (Figure 4) includes inbound docks for unloading the pallets, receiving areas for unpacking, inspecting, sorting and tagging goods before they are stored, storage areas for keeping the goods, commissioning areas for fulfilling incoming orders and replenishing goods, dispatch areas for packing, labeling and combining orders for distribution, and outbound docks for loading up shipment.
Both the left and right halves of logistics and warehousing benefits from knowing the dimensions of goods. Traditionally shipping fee is assessed by weight, but logistics companies are beginning to charge by dimensional weight, or dim-weight. FedEx and UPS are two primary examples. Dimension matters because there is a finite volume per transporter. Maximizing usage of transporter require knowing the package dimensions so the loading sequence can be planned. Many companies provide tools for analyzing loading efficiency and for creating loading plans.
Optimizing transport and tracking of goods requires knowing the weight, dimensions and location of packages; optimizing storing and retrieving of goods require
also efficient handling. Weight is usually measured by scales at the point of pick-up; location is tracked by bar code scanned at each way point; dimension information is either not collected or is measured manually at collection stations. If bar code scanner is equipped with 3D-TOF sensor, package dimensions can be measured at the point of pickup, and the data can help optimize the entire logistic chain downstream.
Building a proper pallet require dimension information. A pallet should built to its maximum allowed height with little to no “gaps”, and yet the center of gravity (CG) ought to be low enough for stability. Finally the payload must be distributed in such way as to keep the trailer stable during transport, thus the loading sequence must be carefully planned based on volumetric and weight information. In fact there are several companies marketing software that will do exactly that.
When bulk goods are received at a warehouse, one has to compare the received goods against the manifest, and sign for the delivery. Traditionally the goods are visually inspected or weighed. Nowadays, with 3D camera, the volume of the goods can also be used as an acceptance criteria. With 3D camera, pallets can be efficiently scanned for volume even as it is being unloaded, and the data is automatically compared with the associated electronic manifest to recommend whether the delivery should be signed for.
Received goods, usually in bulk, need to be efficiently and safely stored and subsequently retrieved and picked for redistribution based on incoming order. Storing and retrieval of bulk goods requires heavy equipments like forklift and trucks. Being indoor these vehicles must coexist with warehouse workers. 3D sensors can be used for obstacle detection to avoid
inadvertent collision with a person, as well as for accurate alignment of forks to the pallet slots. Some warehouses use storage racks that can be lifted by robots and transport to human for picking the goods–an arrangement known as “goods-to-picker”. Other warehouses have humans walk to the goods and pick the products to order–an arrangement known as “picker-to-goods”. Both arrangements use robots operating in the same workspace as humans, a potentially dangerous situation. 3D sensors can endow these robots with collision avoidance capability.
Picking of goods, currently a human role, will soon be replaced by robots also. Robotic
arms equipped with 3D sensor will enable the robot to reach into a bin and pick the desired goods. The present challenge is to recognize the initial pose and graspable surfaces of the object, and proper re-orientation to object so that it can be properly placed in a box. 3D sensor can provide (x, y, z) coordinates of the object’s visible surface so that the pose and graspable surfaces of the object can be extracted.
Modernization of logistics has ushered in a new era of online shopping, and is indeed the key enabler. 3D-TOF sensor and “big data” will make warehouse and logistic operation even more efficient, and order fulfillment more responsive. It is critical that companies in the logistic business realize the potential benefit of 3D-TOF sensors.
(The above article is solely the expressed opinion of the author and does not necessarily reflect the position of his current and past employers and associations)