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5 AMR Deployment Challenges in Today’s (and Tomorrow’s) Intralogistics Environments

5 Challenges in AMR Deployments featured image

About MOV.AI 

MOV.ai disrupts Autonomous Mobile Robot development with a Robotics Engine Platform that contains everything needed to quickly build, deploy and operate intelligent robots.

More and more organizations are looking to deploy autonomous mobile robot in order to meet the demands of modern logistics. However, integrating this new technology within complex and dynamic warehouse environments creates many challenges. Listed below are five challenges robotics manufacturers, software providers, and automation integrators must consider for successful warehouse deployment.

But first, why is now the ideal time to look into autonomous robot deployments?

Why Deploy Autonomous Mobile Robots?

E-commerce use is exploding, and consumers have become accustomed to quick and low-cost delivery right to their door. But as all warehouse operators know, the incredible logistical efficiency required to turn this expectation into a reality places tremendous pressure on employees.

Unfortunately, as the demand for warehouse output grows, the supply of labor shrinks. Recent reports show the scope of the current labor shortage, suggesting that by 2030, more than 85 million jobs could go unfilled. This level of talent shortage equates to roughly $8.5 trillion in unfulfilled annual revenue.

Amongst all these job vacancies, warehouses are one of the hardest-hit sectors. There is no getting around the fact that working in a warehouse is a hard, physically demanding job. Most warehouse roles involve the unappealing triple-D threat of dull, dirty, and dangerous tasks. Given workers have more choices now than ever, it is difficult to see a viable way for warehouses to entice the numbers they need to match demand.

Instead, warehouses must look for a technological alternative, and autonomous mobile robot deployments are at the top of the list. Automation through robotics is already widespread in the automotive, food production, and petrochemical industries; and now, thanks to AMRs, they are taking over logistics.

AMR development is at a point where robots and humans can seamlessly collaborate and cooperate to improve operations and boost efficiency. AMRs can perform long trips and heavy lifting, allowing humans to perform the many necessary roles around them.

A Typical AMR Deployment Process

A typical autonomous mobile robot deployment has six stages: 

  1. Analyzing human operations: Understanding existing workflows and individual procedures and identifying those that are ready for automation.
  2. Selecting AMRs and other components: Finding the optimal AMR fleet as well as accompanying components capable of improving your specific warehouse performance.
  3. Engineering combined AMR/human workflows: Deciding the best way to combine humans and AMRs for this specific deployment.
  4. Simulation: Using a digital twin to test different scenarios for implementing AMRs based on real-life data.
  5. Deployment: Making decisions that are informed by simulations, and implementing the new AMR workflows.

Long-term support and changes: Providing the hardware/software updates to adapt workflows based on feedback.

A typical AMR deployment process

During each step of this process, it is essential to remember the overall goal of autonomous mobile robot deployment: ensuring continuous and effective robot operations that safely increase warehouse efficiency.

There are five main challenges that you will have to be ready for—and make sure your AMRs can deal with—when deploying AMRs in your warehouse:

Challenge #1: Autonomy in Ever-Changing Surroundings 

Autonomy in mobile robots has come a long way. In older generation Automatic Guided Vehicles (AGVs), movement was fixed to specific paths, marked in various methods such as magnets under the flooring, painted tracks, and more. Now with Autonomous Mobile Robots (AMRs) we have much more sophisticated robots that behave closer to how an actual human would. For example, AMRs can navigate their own path between two points using advanced cameras and sensors, and autonomy algorithms.

One of the key algorithms for AMR autonomy is self-localization. Based on all the sensory data available to them, the robots generate a map of their environment and find their location within that environment. However, given the dynamic nature of warehouses, AMRs must also continually compare real-time sensory data to the original map and assess any layout changes or obstacles between their current location and the next destination.

Another autonomy feature of modern AMRs is dynamic path planning. Just as the route planning application in our cars updates based on traffic conditions, so do AMRs, reacting to new circumstances and adjusting their path accordingly. This could be a blocked route or an unexpected delivery needing to be urgently unloaded.

Another issue for autonomous mobile robot deployments within warehouses is maintaining strong WiFi connections. Warehouses typically house a lot of metal, which is excellent at blocking WiFi. Therefore, AMRs need local autonomous capabilities to continue functioning without a continuous connection to the centralized computer.

With advanced navigation solutions such as 3D SLAM,  obstacle detection, and dynamic path planning, AMRs can seamlessly operate in dynamic warehouse environments alongside human employees. They can avoid other robots, humans, and objects, stop at “give way” signs and even assess their kinematic constraints (for example, weight and dimensions of a current load) to determine if they are nimble enough for a given path. 

Challenge #2: Traffic & Task Management for Heterogeneous Robots

These are in fact two separate related tasks:

  1. Allocation of different types of tasks amongst a variety of robots in the most efficient way. For example: unloading pallets from a morning delivery. How do you perform the job in the most efficient way using 30 AMRs? Does the task require more than one AMR type?
  2. Ensuring that those 30 AMRs unloading pallets move around safely considering that there are other robot types from different vendors performing other tasks (such as picking or cleaning) and humans moving around in the same space. 

Consider high-traffic junctions or crossings. Robots and humans all need regular access to pass through this space, making it a high-risk area for collisions and bottlenecks. 

Task management systems need to be able to support multiple robot types and arrange multi-step and multi-robot workflows to maximize productivity and meet deadlines. They also need to interface with other warehouse systems to dynamically reallocate tasks and schedules. Warehouses can be a chaotic workplaces, and it is common for itineraries to get thrown out by unexpected events.

Heterogeneous traffic management systems need to control and coordinate the movement of all robots, regardless of vendor, based on real-time needs. This system must take in all the relevant data (location reporting, entry requests for junctions, etc.) to design the optimal paths and minimize robot transit times—all while combining a range of AMR hardware and software from different vendors.

Challenge #3: Interoperability: Communication with Warehouse Management Systems, Multiple Robots, and IoT Devices

AMRs are not the only automation systems operating inside warehouses. In fact, they are usually just one part of a wide automated process. In addition, there is often more than one type of AMR; fleet management systems need to interact with different hardware (Other robots and IoT devices such as elevators, cameras, sensors, conveyors, sorting machines, etc.) and software (ERP or WMS) to realize their purpose.

For example, a warehouse operating across multiple floors with elevators needs to incorporate this information into its fleet management considerations. AMRs must interact with the elevator to autonomously move between levels.

The solution is to take a holistic approach to automation for the entire facility and devise systems that work based on all available data, effectively making all automation equipment part of a single fleet.

Challenge #4: Fast Deployment Without Disturbing Ongoing Operators

Fast and successful autonomous mobile deployments that limit disruption have one thing in common: simulation using a digital twin. You can’t expect seamless integration without putting in the work beforehand, and multiple simulation types make a real difference. These include:

  • Modeling the robot’s logic using simulated device drivers, external signals, and the sensor response they produce.
  • Physics simulations to place a virtual robot within an accurate digital twin—a model of the real-life warehouse. This helps create AMR pathing and speed rules to prevent accidents (i.e., robots tipping on corners) and ensure safety.
  • Large-scale simulations to represent an entire fleet of robots operating within the same facility. Through statistical analysis, it is possible to tweak fleet management strategies for maximum cooperation and performance.

Challenge #5: Supporting Changes in Operations & Business Requirements 

Autonomous mobile robot deployments need to last 5 to 10 years; over this time, significant support and updates are required. You can think of an AMR fleet as a data center on wheels. They need continuous cyber security updates and future-proof computation power to run new, more powerful algorithms.

Plus, given the world of human-robot interactions (HRI) is constantly evolving, AMR deployments should be able to adapt to future advances. For example, humans might soon control AMR fleets using voice commands and natural language processing.


As you can tell, there is a lot to consider when deploying autonomous mobile robots. However, each of these challenges has real-world solutions for real-world operations.  These include:


  • Advanced navigation for dynamic environments
  • Heterogenous traffic and task prioritization systems
  • Interoperability and holistic fleet management incorporating all automation equipment
  • Digital Twin and simulations to ensure fast deployments
  • Flexible software that supports continuous software updates without disturbing operations

Consideration of the challenges and the solutions when selecting AMRs to work with, and during planning and deployment, will ensure smooth deployment as well as ongoing operations.

With labor shortages and the continual pressure for greater productivity, the challenges of introducing AMRs seem like a walk in the park compared to the challenges of competing in the logistics industry without automation to help.

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