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Logistic centers are a crucial part of the supply chain for virtually all industries that require physical goods shipped, stored, or processed. The number of orders now being fulfilled at these centers, as well as the types and methods of material handling, are increasing globally. Autonomous systems are growing in adoption to safely and efficiently handle the increased volume and complexity of logistic center fulfillment. These trends are further driving advancements in autonomous mobile robots (AMRs), which are seen as an essential building block in the future of logistic center operations for enhanced worker safety, flexibility, efficiency, and productivity.¹
Here we discuss AMRs in general, emphasizing the technologies that enable the newest generation of AMRs and how this technology will impact the logistics supply chain.
AMRs—unlike their predecessors, autonomous robots (ARs) or autonomous guided vehicles (AGVs)— are independently mobile instead of installed in a fixed position or on fixed tracks with a limited task, such as robotic assembly arms. An AMR’s mobility enables these robots to provide a wider range of services and greater flexibility within industrial settings, as generally, no operator or predefined paths are needed. This range of flexibility is why AMRs are now used in healthcare, agriculture, warehousing, and manufacturing operations around the globe.
The sensors and artificial intelligence/machine learning (AI/ML) capabilities designed into modern AMRs allow these machines to operate truly unguided, to the point where they can navigate even unknown environments with unexpected obstacles using simultaneous location and mapping (SLAM). Moreover, the rugged design of many of these machines facilitates their use in environments that would otherwise be hazardous or too difficult for human operators to navigate or fulfill the required tasks.
For AMRs to provide these functions, they require a suite of onboard AI/ML resources, motor control/drive technology, battery/charging system, sensors, and wireless communication capability. Each aspect of these technologies must be carefully designed and implemented to achieve optimum efficiency, safety, and productivity.
AI/ML systems involve extensive development and potential use of a variety of hardware solutions, including microprocessor units (MCUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs). Designing and implementing the foundational AI/ML system requires an entirely different set of tools before an AI/ML technology can be deemed effective/safe and deployed. Often multiple of these hardware solutions are used to most efficiently process the sensor data, communicate, and operate the control systems necessary for mobility.
Generally, the desired response time, vision system frame rate, and task complexity drive the resource requirement for an AMR to perform at a given level. The other factor to this is development time and resources. Both of these implementation aspects of an AI/ML system can be accelerated by using kits or bundles of hardware and software that come prepackaged to solve various AMR challenges, such as 3D vision, motion control, or safety.
For an AMR to be mobile, it requires drive technology, energy storage, and an energy transfer/charging system. These systems will most likely be implemented as electric motors, battery storage, and electrical energy charging, similar to what is used with electric vehicles. For the most efficient and reliable AMR electric motor systems, brushless direct current (BLDC) motors for drive and stepper motors for manipulation functions are most common. This is similar to the technology used in power tools and electronic machine equipment, such as computer numerical controlled (CNC) mills. These systems require motor drivers and sophisticated control systems to enable their reliable operation of motion and manipulator controls.
Though tethered operation can be viable for tracked or fixed robotic systems, an AMR will generally need to be self-powered. This requires energy storage onboard and a charging system that will likely involve docking with a charger. Lithium-ion (Li-ion) battery chemistries are the most common high-density energy storage solutions with DC fast charging (DCFC) to transfer grid electricity to an AMR and minimize downtime. Li-ion batteries require a specialized charge, discharge, balancing, and other battery safety/health electronics. Though the charging of electronics may not necessarily be incorporated into the AMR itself, the alternating current (AC) to DC (AC/DC) inverter and DC/DC power converters will need to be installed for the AMR to dock and charge itself, minimizing the energy and time wasted traveling to and from the charging infrastructure.
For an AMR to avoid obstacles and successfully reroute in case of environmental changes in the operation zone landscape, an AMR requires a suite of sensors that inform the AI/ML algorithms of the surroundings. These vision systems are necessary for an AMR to successfully navigate, ensure safety, and avoid damaging itself, the property, or goods. These sensor systems can be traditional visual spectrum cameras, infrared (IR) cameras, ultrasonic range sensors, laser radar (LiDAR), IR proximity sensors, and even 3D Vision systems. The resolution, acquisition time of sensor data, and number of sensors determine the data throughput that the AI/ML system will need to process. Often this processing needs to be accomplished within a few milliseconds for optimal performance and safety.
Wireless communications are essential for AMR to communicate with facility systems and each other if multiple AMRs deployed. Common wireless standards used for these purposes include Wi-Fi®, 4G LTE, BLUETOOTH®, and now 5G. The type of wireless communication system used depends on the amount of data and types of communication that may be necessary between an AMR and the facility or other AMRs. If there will only be occasional software updates from a centralized location, 4G LTE may be adequate. If large amounts of data are exchanged but not necessarily time-critical, Wi-Fi may be suitable. Emerging 5G solutions facilitate massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). They may be the most viable wireless solution if time-critical data is being exchanged or determinism may be required.
AMRs are the future for many logistic centers and manufacturing tasks, where it is either too costly or unsafe to employ human operators. AMRs are complex machines with a host of electronic subsystems that must be selected, designed, programmed, and integrated to achieve a reliable, safe, and efficient AMR.
1. Using Autonomous Robots to Drive Supply Chain Innovation.” Deloitte. Accessed June 21, 2022. https://www2.deloitte.com.
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Jean-Jacques (JJ) DeLisle attended the Rochester Institute of Technology, where he graduated with a BS and MS degree in Electrical Engineering. While studying, JJ pursued RF/microwave research, wrote for the university magazine, and was a member of the first improvisational comedy troupe @ RIT. Before completing his degree, JJ contracted as an IC layout and automated test design engineer for Synaptics Inc. After 6 years of original research—developing and characterizing intra-coaxial antennas and wireless sensor technology—JJ left RIT with several submitted technical papers and a US patent.
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