Kollmorgen Launches NDC Layout Assistant for Mobile Robot Optimization

The industrial automation landscape faces a persistent bottleneck: engineers often discover layout inefficiencies only after systems are deployed. Kollmorgen is attempting to address this with the launch of NDC Layout Assistant, a software tool designed to analyze and optimize routes for automated guided vehicles and autonomous mobile robots before they ever move a single pallet.

The announcement came via the company's official press release, which positions the tool as a response to limited visibility into layout performance during the design phase. Many organizations struggle with discovering problems late in the process, requiring extensive manual fine-tuning that delays deployment. The NDC Layout Assistant aims to make layout performance transparent from the start.

According to the official Kollmorgen press release, the software breaks down routes into smaller segments for granular analysis. This segment-level approach allows users to see exactly where inefficiencies occur, even in complex facility layouts. Each segment receives specific metrics: travel time, drive speed, and optimization potential.

Visual indicators flag segments with inefficient travel times or drive speeds. This matters because engineers can now spot problem areas without running endless physical tests. The tool highlights areas with the highest optimization potential, helping teams focus efforts where they'll deliver the greatest impact.

Independent coverage from Robotics & Automation News corroborates the core functionality and timeline. The outlet notes that these machines typically move materials around distribution centers, factories, and logistics hubs—environments where every second of travel time compounds across hundreds of daily trips.

The physical reality of warehouse automation involves constant friction. A robot that hesitates at a corner, slows down unnecessarily on a straight path, or waits too long at a charging station creates ripple effects throughout the entire operation. Traditional validation required deploying the actual hardware, watching it move, measuring delays, then going back to redesign the route. Repeat. The NDC Layout Assistant attempts to compress this cycle.

Kollmorgen positions the tool as part of its broader software platform for managing fleets of automated vehicles. This platform coordinates routes and controls navigation across industrial environments. The new assistant integrates into that ecosystem rather than standing alone as a disconnected utility.

From a technical standpoint, the segment-level analysis represents a shift from holistic to surgical optimization. Instead of treating a route as a single entity, engineers can now identify which specific 10-meter stretch is costing them three extra seconds per pass. Over a thousand daily cycles, those seconds become hours of lost productivity.

The company adds that the tool could form the basis for more advanced, AI-driven optimization in the future. As automation systems become more complex and data-driven, having structured layout data creates a foundation for machine learning models to suggest improvements. (Whether those suggestions will actually be better than experienced engineers remains to be seen.)

Design workflow efficiency is a key selling point. By reducing time spent identifying issues and validating changes, the tool supports faster, more predictable workflows. Users can prioritize improvements, shorten simulation cycles, and communicate results more effectively. The end goal: layouts that perform as intended, sooner.

This addresses a real pain point in industrial automation. Facility managers have long complained about the gap between simulation and reality. Software models often assume perfect conditions—no unexpected obstacles, consistent battery levels, ideal floor surfaces. The NDC Layout Assistant doesn't solve all of that, but it does provide better data before hardware touches the floor.

The tool's value proposition hinges on early detection. Finding a bottleneck during the design phase costs minutes of software time. Finding it after deployment costs days of downtime, reprogramming, and potentially reconfiguring physical infrastructure. The economics are straightforward, even if the implementation details remain proprietary.

What's notably absent from the announcement are specific performance metrics. Kollmorgen doesn't claim X% reduction in design time or Y% improvement in route efficiency. The company focuses on qualitative benefits: faster improvements, higher design quality, fewer iterations. This is common for software tools where results vary wildly based on facility complexity.

For context, the industrial automation market has been racing to reduce deployment friction. Competitors offer simulation tools, fleet management platforms, and route optimization software. Kollmorgen's approach of segment-level analysis with visual indicators distinguishes the product, though direct comparisons remain difficult without third-party benchmarking.

The timing aligns with broader industry trends toward data-driven facility management. As warehouses become more automated, the margin for error shrinks. A poorly designed route doesn't just waste time—it creates congestion, increases collision risk, and strains battery management systems. The NDC Layout Assistant attempts to catch these issues before they compound.

Whether the tool delivers on its promises depends on adoption and real-world validation. Software tools that look good in demos often struggle with the messiness of actual industrial environments. Dust, uneven floors, unexpected obstacles, and human workers sharing space with robots create variables that no simulation fully captures.

The company's claim about establishing a foundation for future AI capabilities suggests this is version one of something larger. That's a reasonable position—gather data, refine algorithms, expand functionality. But it also means current users may need to wait for the promised intelligence features to mature.

For facility planners and automation engineers, the immediate question is integration. How does this tool connect with existing CAD systems, fleet management software, and simulation platforms? The press release doesn't detail compatibility requirements or data import/export formats. These practical concerns will determine whether the tool becomes essential or sits unused on a shelf.

The bottom line: Kollmorgen has identified a genuine problem in mobile robot deployment and offered a structured solution. Whether users actually pay for it—and whether it delivers measurable improvements—remains the real question. The automation industry has seen plenty of tools promise to solve layout challenges. Execution matters more than announcements.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn