This article includes everything you need to know about autonomous mobile robots and their use.
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An autonomous mobile robot, or AMR, is a self-powered and self-propelling device engineered to execute repetitive tasks and organizational functions using its internal guidance system. These robots traverse their environments with the aid of advanced mapping and software technologies that enable them to "SEE" and comprehend their surroundings to perform a wide range of tasks. With the integration of sensors, artificial intelligence, machine learning, and computer algorithms, AMRs can effectively recognize obstacles and avoid collisions.
AMRs are equipped with sensors that continuously monitor their surroundings to identify possible hazards or obstacles. When such issues are detected, the AMR autonomously computes and follows an efficient route to bypass them. During their initial deployment, AMRs are furnished with mapping technologies like visual simultaneous localization and mapping (SLAM), facilitating real-time navigation decisions based on environmental observations.
AMRs are frequently described as having the capability to "SEE" their surroundings due to their utilization of light detection and ranging (LiDAR) technology. LiDAR uses pulsed laser sensors to ascertain distances, providing AMRs with a comprehensive understanding of both their position and the objects around them. Serving as the "eyes" of an AMR, this technology is crucial for accurate navigation and interaction within various environments.
To meet the demands of modern distribution operations, the order fulfillment industry has turned to advanced technological solutions. Traditional methods, such as forklifts and manual picking processes, lack the efficiency and speed required by today’s customers. In response to this need for automation, several innovations have emerged, utilizing computer-programmed robotic devices.
Initially, automated guided vehicles (AGVs) were introduced to address automation needs. These vehicles follow predefined paths marked by tape, wires, reflectors, or other guidance systems to reach their destinations. While AGVs have greatly enhanced order fulfillment, they are constrained by their reliance on fixed guidance mechanisms and cannot adapt their path when encountering obstacles.
With advancements in artificial intelligence (AI) and computer software, material handling companies have developed autonomous mobile robots that navigate facilities without the need for physical guidance systems. These sophisticated robots can be programmed to perform a variety of tasks while avoiding people and potential obstacles, offering a significant leap forward in automation technology.
Although AMRs are similar to AGVs, they differ in their amount of flexibility and autonomy. AMRs are capable of creating their own routes and finding the most efficient way to achieve their tasks. The effectiveness of AMRs makes processes and workflow more efficient and productive compared to traditional manual methods.
The implementation of robotic inventory systems is designed to take care of simple tasks that waste the time of personnel. Efforts to this effect have been reached and investigated for years and led to the implementation of robotic arms, conveyors, and quality checkers that have radically improved productivity and the quality of products.
A typical warehouse operation involves storing, picking, organizing products, and loading trucks or supplying production lines. Traditionally, these tasks were handled by workers and a well-organized racking system. However, advancements in technology have led to more efficient methods that minimize the need for manual picking and fulfillment, with automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) playing central roles in these improvements.
Transporting inventory or products from one location to another is a fundamental function ideally suited for AMRs. Order picking, which is labor-intensive and costly due to the time and personnel required, can be optimized with AMRs. Instead of workers walking between locations, AMRs can retrieve items and deliver them to pickers, thereby reducing travel time. AMR pickers transport carts, items, and products between workers and workstations, allowing workers to select items without leaving their stations. The AMR can then proceed to its next task or assist other workers.
In zone picking, an AMR travels to the zone where the item to be retrieved is located. A worker in that zone is guided by an augmented vision picklist or pick-to-light system provided by the AMR. After the item is loaded, the AMR proceeds to the next zone or moves directly to shipping. Additionally, AMRs can be fitted with carts that move between zones, allowing for efficient filling, packing, processing, and shipping of products.
Autonomous mobile robots play a crucial role in sortation processes. Their applications in sortation vary based on the handling technologies used, such as tilt trays, belt systems, and conveyor rollers. Some sortation systems utilize a fleet of AMRs equipped with tilt trays. These sortation AMRs are collaborative robots that work alongside human operators and chutes to position and locate orders effectively.
AMR systems excel in both primary and secondary sortation processes due to their flexibility and advanced programming. Primary functions include receiving items and sorting them into bins or totes, while secondary functions involve organizing products for shipment to customers. In both scenarios, sortation AMRs are tasked with ensuring that packages are delivered to the correct locations.
One popular type of sortation AMR is equipped with tilt trays, which work in collaboration with workers and chutes for effective order positioning and sorting. A camera scans a barcode to identify the item, directing it to the appropriate chute. When the AMR reaches the chute, the tray tilts to release the item into a container, and the AMR then transports the collected order to the shipping department.
Beyond shipping, sortation AMRs are also useful for consolidating and organizing returns. Operators collect items, input the item number and quantity, and the AMR uses this information to transport the items to their designated location. Return AMRs are typically used for short-term tasks and can be reprogrammed for other functions once the return process is complete.
Accurate inventory control is one of the most challenging and critical tasks in manufacturing and distribution. Traditionally, inventory accuracy was maintained through periodic counts—every three months, six months, or annually. After these counts, the inventory value was adjusted based on shrinkage or growth, requiring companies to make necessary adjustments to their inventory costs.
Today, inventory tracking has been revolutionized by technology, particularly through the use of inventory scanning AMRs. These advanced robots employ computer vision and analytics to collect real-time data on shelf inventory. AMR systems manage processes such as receiving, shelving, replenishing, and picking, using built-in checking methods to identify errors and irregularities. They also monitor cycle times to address potential issues before they escalate.
A sophisticated array of cameras mounted on an AMR reads and records item barcodes for future reference. Despite occasional glitches, an AMR can maintain an accurate record of every item in a warehouse. Additionally, AMRs can be programmed to pick items directly from a production line, facilitating the automatic transfer of completed products to shipping or storage.
AMR inventory scanners can be scheduled for periodic cycle counts of stored products and parts to ensure accurate inventory levels. This process helps identify errors, inconsistencies, and irregularities, preventing production downtime due to part shortages, improving customer service, and reducing capital loss from overstocks.
Collaborative AMRs are used to optimize logistics and work closely with human workers to assist in completing a variety of functions. An essential element of collaboration AMRs are safety features due to their close proximity to humans. Although it may be presumed that fences and barriers would be necessary, a more accurate representation is the implementation of tested safety programming.
Human-AMR collaboration can vary from no interaction to sharing a workspace and working as a team. In these collaborative environments, AMRs handle tasks such as delivering parts or items and removing completed products based on instructions from human operators. The complexity of these interactions can increase significantly, with AMRs following workers as they place finished products on shelves and retrieving items needed for assembly.
Collaborative AMRs are equipped to handle multiple functions such as put-away, picking, counting, replenishing, and sorting, all of which can be programmed into their systems. These tasks can be executed individually or in combination, depending on the design and programming of the AMR. A key advantage of collaborative AMRs is their ability to reduce walking time and distances for workers, allowing them to remain focused on their assigned tasks.
The optimal performance of a collaborative robot (cobot) relies on the guidance of its human partner, who provides instructions. Unlike other AMRs that operate autonomously based on their programming, a cobot functions as an assistant, supporting its human partner according to the evolving needs of the applications.
Storage picking AMRs are scalable and require specific adjustments to their environment to function effectively. One potential limitation is the need for specialized racking systems to which the AMR is attached during the picking process. These AMRs are designed to move up and down the racks as they retrieve items from the shelves.
The dimensions, location, and design of the racking must align with the requirements of the AMR. Implementing a scalable storage picking AMR involves several site-specific factors. The number of AMRs can vary widely, from as few as five to ten at some locations to 50 to 75 at others. This variation is crucial for the successful deployment of scalable storage picking AMRs, as it requires adjustments to the existing setup and potentially impacts the operation of other AMRs in the facility.
AMRs are also making strides in the hotel and restaurant industries by performing simple services that traditionally require human staff. These tasks include scrubbing and vacuuming floors, delivering food, and collecting trash. While the use of AMRs in these settings is still in its early stages, future innovations may enhance their capabilities and expand their applications in these sectors.
Autonomous mobile robot (AMR) forklifts serve the same purpose as operator-driven forklifts but with enhanced efficiency and minimal human interaction. These AMR forklifts perform a variety of tasks flexibly and effectively, utilizing a 3D detection system for operation. Unlike traditional forklifts that use propane or electric motors and require a driver, AMR forklifts are more compact and operate quietly.
One major advantage of AMR forklifts is their seamless integration into warehouse operations. By programming their software to align with the specific conditions of the warehouse environment, AMR forklifts can quickly adapt to changes in their surroundings, making them highly versatile and efficient in dynamic settings.
A common part of forklift operation is constant change in routes and picking requirements. In the middle of a picking, further instructions may require a change of route to a new location. Forklift AMRs easily adapt and change to meet any new programming and instructions. As with other forms of AMRs, forklift AMRs read their environment and use their sensors to guide their path. When given new instructions, they choose the most efficient way to a location and immediately initiate it.
Autonomous mobile robots (AMRs) are advanced, computer-controlled robotic vehicles designed to navigate their environment independently, without relying on guidance mechanisms such as wires, reflectors, or tape. They utilize sensors, artificial intelligence, machine learning, and sophisticated software to assess their surroundings, avoid obstacles and people, and dynamically adjust their paths to complete assigned tasks.
AMRs identify and map their surroundings, enabling them to "SEE" walls, fixtures, columns, and shelving. They use this data to navigate their environment through Simultaneous Localization and Mapping (SLAM), a set of algorithms designed for mapping and determining their position within a space.
SLAM is a broad term encompassing various algorithms and technical approaches, including graph SLAM, Extended Kalman Filter (EKF) SLAM, fast SLAM, topological SLAM, visual SLAM, 2D and 3D LiDAR SLAM, and Oriented FAST and Rotated BRIEF (ORB) SLAM. The core function of SLAM is to help robots locate their position and orientation on a map while simultaneously creating that map to perform their tasks effectively.
While the concept of SLAM has been featured in science fiction for years, its practical implementation required significant technological advancements, including improvements in computer processing speed and reductions in the cost of sensors and scanners. The effectiveness of SLAM relies on two key technologies: sensor signal processing and pose graph optimization.
Pose Graph Optimization (PGO) is a technique used to refine the positions and orientations, or poses, of a robot or camera. The goal of PGO is to minimize pose errors by considering the relationships and constraints between them. A pose graph is a graphical model where nodes represent poses, and edges represent spatial constraints derived from odometry and loop closures.
Odometry constraints estimate the movement between poses, while loop closures provide constraints based on locations that an AMR revisits. By recognizing and correcting these loops, consistency is achieved between current and previous observations of a location. Pose graph optimization aims to find the best set of poses that satisfy these constraints as closely as possible, adjusting poses to reduce errors and ensure accuracy and consistency.
The initial stage of deploying an AMR involves using SLAM to map its surroundings. An operator guides the AMR through its workspace using a joystick. During this process, the AMR’s sensors detect and record the locations of walls, equipment, machinery, and other stationary elements in the environment. This data is saved by SLAM, creating a comprehensive map of the workspace with just one tour.
As the workspace evolves with new objects or equipment, the SLAM map is updated to reflect these changes. The AMR’s mapping system dynamically adjusts to incorporate new developments and ensure the map remains accurate.
During mapping, the AMR takes snapshots of its surroundings, capturing feature points represented in 3D space, including the distances from objects. These feature points form a point cloud—a 3D representation of the environment. The AMR uses this point cloud to track its position and orientation for accurate localization.
Mapping is just the beginning; localization is the next critical step. Once the environment is mapped, SLAM helps the AMR understand its location based on its surroundings. This allows the AMR to plan its path and complete its tasks effectively.
Upon activation, an AMR’s primary task is to determine its position using a combination of cameras, sensors, and LiDAR, or just one of these technologies depending on the AMR's design. In some cases, GPS is also used for additional support. The localization process involves interpreting camera images at high frame rates, typically 30 frames per second or more. SLAM uses these frames to estimate distances, match features to previously tracked points, verify against the map, incorporate new features, and accurately locate the AMR.
Visual SLAM employs cameras and sensors to provide a visual understanding of the AMR's environment. This can range from a single camera to complex setups with compound eye cameras and RGB-D cameras. These cameras aid in landmark identification and detection. Visual SLAM utilizes complex algorithms such as PTAM and ORB-SLAM for sparse feature point matching, as well as dense methods like DTAM, LSD-SLAM, DSO, and SVO, which use image brightness for navigation.
LiDAR uses laser sensors to provide precise distance measurements, making it suitable for high-speed AMRs like self-driving cars. It excels in map construction and localization by using point cloud data, which is matched using algorithms such as Iterative Closest Point (ICP) and Normal Distributions Transform (NDT). LiDAR-generated point clouds can be represented as grid or voxel maps.
To maximize effectiveness, LiDAR is often combined with other measurement methods, including wheel odometry, global navigation satellite systems, and Inertial Measurement Units (IMUs). In environments with sparse obstacles or long distances between obstacles, LiDAR may require additional support to function optimally.
The MiR600 is equipped with advanced laser scanning technology, providing 360° visibility for optimal safety. It can autonomously pick up, transport, and unload pallets without requiring additional guidance systems. The MiR600 supports downloading facility maps via CAD files or can create its own map. It is an IP52-rated AMR, offering protection against dust particles and water droplets. The MiR600 can operate near fences and open gates. It is controlled through an intuitive MiR Robot interface accessible via smartphone, tablet, or PC, and can be easily programmed without prior experience.
The MiR250 Hook is designed for towing heavy products in manufacturing environments or moving carts in hospitals. It can support loads up to 500 kg (1100 lbs.), offering a versatile logistics solution. The MiR250 Hook identifies carts using AprilTags and transports them to predefined locations. Commands for the MiR250 Hook can be quickly adjusted using a smartphone or tablet via standard Wi-Fi. Its robust base enhances maneuverability and performance.
The OPEX Sure Sort system provides a scalable and cost-effective solution for multi-line orders, package sorting, and reverse logistics. It efficiently handles small packages of various shapes, weighing up to five pounds. The system minimizes package handling with its six-sided scan tunnel, capable of reading barcodes from any angle. Sure Sort is suitable for small businesses seeking affordability or large businesses aiming to optimize operations.
In the Sure Sort system, items are placed on a belt that moves through the scan tunnel. An iBOT, a multidirectional vehicle, then deposits items into designated bins. When a bin is filled with all items for an order, the operator is notified that the order is ready for packing and shipping.
The K55 pallet stacker is designed to move and stack palletized loads at a low height and can perform cyclical or conditional routes by interacting with other AMRs, systems and people. It is the modern automated solution for transporting and organizing medium weight palletized orders. The K55 pallet stacker is adaptable to any pallet storage application, merchandise reception, and material handling system. It optimizes storage space and improves process efficiency. The K55 pallet stacker can lift 1000 kg (2204 lbs.) to a height of one meter. It uses mapping software and has exceptionally high accuracy and precision. For safety, the K55 has 360o laser scanners with PLC safety and led signaling and front touch monitoring for AMR status, potential errors, and circuits.
The MaxMover CB D 2000 is a highly maneuverable counterbalance forklift, capable of pivoting on the spot for exceptional agility. Its advanced safety system prevents overloading, accidental pushing, and dragging of loads. With a maximum payload capacity of 4,409 lbs (2,000 kg), it is versatile enough to handle various heavy loads beyond just pallets. The MaxMover CB D 2000 boasts impressive lifting speed and gradeability, making it a valuable addition to any warehousing system. Its strength and durability allow it to effortlessly reach heights of 16 ft (5,000 mm). Overall, the MaxMover CB D 2000 offers an efficient and cost-effective solution for material handling needs.
The ODM is engineered to transport totes or small loads weighing up to 300 kg (661 lbs.), making it ideal for the electronics and pharmaceutical industries. It can operate within a workspace without requiring modifications, thanks to its omnidirectional drive system, which enables smooth navigation into narrow rack aisles and allows for instant turns. The ODM features an advanced route-finding system that helps it avoid obstacles and people. If a route becomes blocked or impassable, the ODM swiftly recalculates an efficient alternative path to complete its task. A standout feature of the ODM is its swarm application, which enables a fleet of ODMs to communicate and share data, enhancing coordination and efficiency.
The primary goal of the AMR industry is to enhance employee efficiency in tasks such as picking, locating, and moving products and inventory. By operating continuously, AMRs help minimize downtime and boost productivity. Moreover, picking AMRs offer high accuracy, which reduces the likelihood of customer returns and further improves operational efficiency.
AMRs enhance operational efficiency and streamline workflows by eliminating the need for manual intervention. Their advanced routing systems reduce material handling and transportation, which lowers energy consumption and an organization’s carbon footprint. Operating around the clock without breaks, AMRs maintain high productivity levels.
Each task performed by an AMR is precise, ensuring consistency and minimizing human error. By monitoring production in real time, AMRs can identify bottlenecks, inefficiencies, and process errors, enabling management to make timely corrections and address potential issues.
Automated inventory tracking and data collection by AMRs significantly improve inventory visibility. Equipped with sophisticated sensors, cameras, and barcode scanners, AMRs conduct accurate and rapid inventory audits and provide real-time updates on stock levels and locations. Enhanced visibility helps maintain optimal inventory levels, avoiding costly stock overages or shortages.
The data collected by AMRs provides valuable insights into inventory usage, allowing businesses to make informed ordering decisions and better understand supply chain dynamics. Analyzing this data helps identify patterns and trends, enabling more efficient order processing and preparation for business changes. Ultimately, this leads to a more cost-effective and productive operation.
AMRs play a crucial role in alleviating employees from heavy-duty tasks that can be physically demanding and risky. By handling the movement of large and bulky items, AMRs improve workplace safety, reducing the risk of injuries and freeing staff to focus on tasks involving planning and problem-solving. As AMRs manage pallet movement, employees can concentrate more on quality control and order processing.
Furthermore, the use of AMRs enhances the skill level of the workforce, allowing employees to spend more time developing their skills and devising practical solutions. This transition positively impacts worker morale and attitude, as staff can engage in strategic planning, training, and programming for AMRs. As employees become more familiar with AMRs, they are better equipped to adapt to innovations and changes in the workplace.
Order fulfillment and collection are among the most time-consuming tasks in operations. The implementation of AMRs significantly enhances these processes, leading to faster and more accurate order processing. AMRs efficiently handle tasks such as picking, packaging, and shipping, thereby reducing the time required to prepare an order.
The future of manufacturing, warehousing, and retail operations is increasingly reliant on autonomous mobile robots. Organizations of all sizes and industries will need to understand how AMRs work and how they can enhance operational efficiency. Over the past decade, business operations have evolved with the integration of new technologies, and this trend will continue as we progress toward mid-century. Companies that do not embrace AMR technology risk falling behind.
Change is an inherent aspect of successful business practices. Processes that were effective a few years ago can become obsolete, making way for more efficient and adaptable methods. AMRs are designed to evolve with the changing business landscape. As facilities are updated and redesigned, AMRs can be reprogrammed and reconditioned to align with new operational dynamics.
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