How AMR Pallet Detection Improves Warehouse Efficiency

High Capacity AMRs and AGVs have a singular, shared primary function: to move pallets efficiently. However, when it comes to pallet handling, not all robots are not created equal. When automating, it is important to look at the capabilities of each autonomous vehicle and consider the true advantages of AMRs over AGVs in increasing warehouse efficiency and material handling performance.

Pallet Pickup Capabilities of Autonomous Vehicles

A key example that highlights these differences is the way AMR and AGV forklifts approach pallet pickup. Traditional AGV forklifts cannot pick up pallets and require a human to manually load the pallet onto the forks. Advanced AGVs can independently pick pallets, but come with significant restraints on pallet detection. Across the board, AGVs need precise staging and can only move a limited range of pallets. AMRs are an all-around, robust and flexible automated pallet handling solution – reducing direct worker involvement and offering higher level capabilities that are critical to operational warehouse efficiency.

Advanced AMR Pallet Detection

Perhaps the most important distinction to be made is that of AMR and AGV pallet detection abilities. AGVs navigate to a location where they think their assigned pallet should be. If the pallet is not placed in just the right spot with precise alignment, the AGV will attempt to pick up the pallet, fail, and then wait for a human to assist. Warehouses are notoriously fast-paced and dynamic environments, where goods and people are moving quickly. The likelihood that a pallet is placed askew in the drop-zone is fairly high, meaning that the operational warehouse efficiency gains you expect from introducing robots will be diminished with AGVs.

AMRs use intelligent pallet detection. While AGVs will only drive to one exact location to find a pallet, AMRs will use sensory data to determine the pallet is in place before they attempt to pick it up. If the chosen pallet is not in the correct drop zone, the AMR will not attempt, fail and wait for assistance. Instead, it will sense this and continue to search in likely pallet drop-off locations nearby. This approach unlocks massive efficiency gains as it allows operators to work quickly and with less specificity, as AMRs will forgive imperfect drop-offs as long as the pallet is nearby. Most importantly, AMRs allow operators the freedom to work without interruption with fewer stops and calls for assistance. A pallet mover robot that can adapt to a realistic, ever-changing environment will ultimately provide the most flexibility and the best benefits.

AMRs Maximize Warehouse Efficiency with Task Accuracy and Superior Navigation

Not only do AMRs have superior localization and obstacle avoidance into account, but task competency as well. With combined intelligence and industry leading skills in these categories as well as pallet detection, AMRs offer the most benefits in all applications. In all categories, AMRs such as Vecna Robotics’ Pallet Truck, Fork Truck and Tugger have the better navigation systems and execute tasks with greater accuracy. Additionally, Vecna Robotics offers AMRs with AI that can not only excel from the start in variable conditions, but will improve over time – no matter how the floorplan or workflows may shift. With continuous learning and business intelligence tools, operational efficiency will increase and facilities can gain a competitive advantage. This advanced technology maximizes investments in autonomy, and it is best to choose robots with capabilities that best match the needs of your organization.

Navigation and task accuracy are the most important factors towards reaching the highest efficiency gains. Download the full guide to AMR and AGV Navigation here.

Learn more about AMR Obstacle Avoidance and Localization.

Obstacle Avoidance in AMR and AGV Robots Explained

One of the more distinguishing features of AMRs is their fine-tuned ability to avoid obstacles. As discussed in this article about localization, AGV robots navigate using features installed within the facility. They follow distinct paths and are not able to reroute, in most cases requiring human intervention when unknown elements are introduced. AMRs use a more advanced technique of obstacle avoidance, providing increased efficiency and a richer return on investment.

AMR Obstacle Avoidance vs. AGV Collision Avoidance

All mobile robots are enabled with collision avoidance. At a minimum, their safety system will sense any blockage and safely slow or stop the robot before a collision occurs. However, this is typically the limit of what AGVs can accomplish. AMRs, on the other hand, have obstacle avoidance. The robot is not only able to avoid collisions, but can reroute and continue its task as a human would.

In a dynamic warehouse environment, obstacle avoidance is crucial. With only basic collision avoidance, AGVs need to be monitored more frequently. Any obstacle – say, packaging that has been discarded, or another vehicle – will need to be moved by a human. These frequent interruptions consume workers’ time, often depleting labor resources that are better used elsewhere. This level of interaction undermines much of the purpose of integrating warehouse robots in the first place.

AMRs possess capabilities much closer to true autonomy. They are able to fix problems themselves, and add value on a more consistent basis by not interrupting other workflows.

AMR and AGV Robots in Traffic Congestion

Just as all mobile robots have the ability to detect an obstacle and avoid collision, all mobile robots also have what are known as safety fields, or speed-limited zones. In these areas, robots are programmed to slow or stop, and take a certain amount of buffering time before continuing their task. AGV vendors program these locations into the robots during the mapping process.

However, its important to mention again that warehouses are dynamic environments. An area that was not being used when the AGV was installed could become the busiest hub in the facility, and that which was originally a high traffic area goes unused. This could be handled in one of two ways:

  • Firstly, the AGV may detect traffic and slow in the newly busy area, and also continue to slow down as programmed in the original, but now empty area unnecessarily. This is a drag on the efficiency of the vehicles.
  • Secondly, the AGV may be programmed to ignore traffic in the previously-empty, now high-traffic area and speed through it. This is a major safety concern.

The path planning abilities of AMRs offer a fix to this problem, adjusting not only speed, but direction. Robots using this technology use intelligent analytics to chose the most efficient route to complete their task, taking traffic and obstacles into consideration in real time. While AGVs are limited to waiting until their path is clear, AMRs can venture briefly off path to route around traffic at a safe distance, providing plenty of room for other actors to complete tasks. If this is not the most efficient choice, AMRs will choose a new route entirely.

Dynamic Obstacle Avoidance: Passing Moving Vehicles

AGVs – or even some AMRs – employ limited obstacle avoidance, diverting for their path for only a short distance to avoid static obstacles. While this works in isolated incidents, it is not enough in congested areas. Moving vehicles are a frequent feature of industrial spaces, and AMRs are much better equipped to succeed in these interactions.

AMRs know the rules of the road. They are designed to share space with manually operated vehicles, other AMRs, and AGVs, and move seamlessly with other objects and entities in its shared space. In a situation where an AMR meets oncoming traffic, it can move to the side to pass, wait until the coast is clear and then resume its mission. In this same instance, an AGV would stop because it would perceive the moving object as an insurmountable challenge. It could then block traffic altogether and require an operator to intervene.


The Benefits of AMRs over AGV Robots

AGV robots

In most dynamic warehouse environments, goods are moving quickly and workflow or floor plans change frequently. A path following AGV is not able to handle these changes. A site manager must manually re-route the AGV robot to teach it a new route. These interventions are interruptive consume workers’ time. AMRs avoid obstacles in a way that is superior in providing unmatched autonomy. They are much more equipped to function without assistance, giving floor managers peace of mind, and optimized, autonomous workflows.


Autonomous Mobile Robots, like self-driving tuggers, fork trucks, and pallet trucks, apply obstacle avoidance technology in a way that maximizes safety and efficiency.

For more about the differences between AMRs and AGVs, download the infographic.




AMR Path Planning: A Smarter, More Efficient Navigation Method

While AGVs and AMRs preform similar tasks, there is a large variance in their methods and in the capabilities that stem from them. Both, as a baseline function, are able to transport materials through industrial facilities and effectively reduce the need for manually operated machinery. However, the efficiency gains generated will depend on the distinct abilities of your robot’s competencies in navigating your facility and accomplishing its tasks in a dynamic environment. Understanding the varying abilities of AGVs and AMRs in path-planning and navigating will help guide those automating towards the best investment.

Traditional AGV Navigation: Path-Following

Traditionally, AGVs navigate using infrastructural landmarks, such as wires, magnets, tape, reflectors, or QR codes placed throughout the facility. Any AGV will rely on one or many of these features, moving along predetermined paths like a train on a track. Because these features are permanently and geographically set from the start, the robot is limited to operating on these fixed routes. This means any changes would require further infrastructural updates, newly planned routes, and a loss of valuable operating time.

AMR Natural Feature Localization

AMRs, and some advanced AGVs, employs a technique called natural, feature-based localization. Using a combination of Lidar and camera-based sensors to make a virtual map of its environment in real time, the robot locates static features such as walls, racking, and pillars and uses these features to orient itself. Due to this, AMRs are not confined to any specific route and are able to navigate dynamic and ever-changing environments easily. Paths can be changed or interrupted with little consequence. This method forgoes the inconvenience and cost of infrastructure changes associates with traditional AGV installation, and provides a more flexible form of navigation.

While advanced AGVs will claim that the integration of localization is the only necessary differentiating factor, there are more advanced technologies integrated into AMRs that allow them to vastly increase efficiency and decrease the need for human intervention, making them truly autonomous vehicles that provide the highest return. The winning factor that keeps AMRs mobile and working is path planning.

AMR Path Planning

What truly distinguishes the best AMRs from the rest of the pack is their uniquely intelligent ability to engage path-planning. In essence, path-planning is what it sounds like: robots with this feature will choose the most efficient route to accomplish their task, taking into account a multitude of actively changing environmental factors. In addition to using natural feature localization, path-planning AMRs are able to adapt to their environment and re-assess routes as a human would. When AMRs encounter obstacles, they can reroute efficiently and get back on task – saving workers from having to unblock a gridlock caused by a stuck AGV.

Vecna Robotics has employed a combination of path-planning and natural feature localization that mirrors how a human thinks. Humans use input from their eyes to assess and navigate their environment. In much the same way, an AMR will actively use sensory data capture to navigate. The AMR can move around effectively using real-time intelligence rather than initial inputs of precise measurements of the warehouse. An AMR that relies on a combination of obstacle avoidance, smart localization and path planning will not be thrown by an environment in flux. The AMR’s performance will never degrade as the facility changes – in fact, it has the potential to improve its performance over time.

AMR using localization

AGVs vs AMRs: Mobile Robots the Clear Winner in Navigation

AMRs apply natural feature localization in a way that makes for an easily integrated, flexible solution. Moreover, when paired with path planning technology, like in Vecna Robotics’ self-driving tuggers, fork trucks, and pallet trucks, the robots are more robust to changing environments and workflows, and require less support from workers. Thus, all resources can continue carrying out their work with as little interruption as possible.

Read about how path planning, obstacle avoidance and pallet detection set AMRs apart from AGVs.


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