Improving Productivity Using Multi-Robot Systems

What happens when you aim industrial AI at production scheduling but treat it like every other engineering problem? We built a multi-agent AI system that achieved a 21% increase in profit. Here’s how: 1. Make the goals explicit Production scheduling is a complex process with numerous trade-offs. Highest demand or most efficient run? Overtime or on-time delivery? We spelled out the real goals and KPIs so the agent system knew exactly which knot it had to untangle. 2. Capture expertise through machine teaching Machine teaching breaks the job into bite-size skills. An engineer shows the system why a decision works, not just what happened in the data. Rather than rely purely on data, machine teaching transfers deep human expertise into the system - digitizing decades of experience and knowledge, crucial as expert operators retire. 3. Structuring the Multi-Agent System The multi-agent system was designed to mimic human decision-making: Sensors: Gather real-time data on production status, resources, and external market conditions. Skills: Modular units responsible for specific actions, such as forecasting demand, optimizing scheduling, or adapting to sudden changes. Each skill can evolve on its own, giving the plant the same modular flexibility you expect from any well-engineered system. 4. Establishing a Performance Benchmark Good engineering demands clear benchmarks. We ran a standard optimization-based system as our baseline. This allowed us to objectively measure whether our AI agents delivered measurable improvements. 5. Rigorous Testing & Iteration Engineering thrives on iteration. We created and tested 13 agent system designs, continuously iterating based on performance data. Each iteration leveraged insights from the previous, systematically improving performance until we identified the optimal solution. --- By treating AI as an engineered system (modular, explainable, and configurable) it demonstrates significant potential results: ✅ 21% higher profit margins ✅ Improved adaptability to rapidly changing market conditions ✅ Preservation and amplification of valuable human expertise Full breakdown of the build and tests is below.👇 #ProductionScheduling #IndustrialAI #MachineTeaching #SmartManufacturing

Hot off the press! Heterogeneous Collaborative Pursuit via Coverage Control Driven by Fokker-Planck Equations.  R. Lin, S. Kim, and M. Egerstedt. IEEE Transactions on Robotics. https://lnkd.in/gA_hrnt3 Abstract: Inspired by common features found in collaborative behaviors in nature, we investigate a general collaborative pursuit framework enabling heterogeneous multi-robot systems to adapt to dynamic environments and diverse tasks. A class of augmented Fokker-Planck equations is formulated to characterize dynamic environmental conditions, and the resulting time-varying density functions drive a novel coverage-based controller, with provable stability properties, for the participating robots to perform tasks in real time. The developed framework is decentralized and incorporates heterogeneity among different robots in task suitability, relative performance in a specific task, and safe operating regions. To demonstrate its adaptivity and effectiveness, the framework is implemented across four experimental applications ranging from multi-robot coordination to collaboration, namely forest firefighting, pursuit-evasion, monitoring of various environmental phenomena, and phoretic interactions.

One of the biggest misconceptions in warehouse automation is that more robots automatically mean more productivity. In many cases, the opposite is true. We've walked into facilities where the first conversation is about buying additional AMRs. But after looking at the operation, the real problem wasn't fleet size. It was downtime. If robots spend part of every shift traveling to chargers, waiting to charge, or sitting idle because charging has to be scheduled, you're often compensating by buying more robots than the operation actually needs. One recent project challenged that thinking. Instead of adding robots, the customer: Reduced the fleet by 25% Eliminated two charging stations Increased robot utilization from 59.5% to 83.1% Improved throughput Saved $98,000 while avoiding another $197,000 in future costs The lesson? Before adding robots, ask whether your current fleet is spending too much time not moving material. Sometimes the biggest opportunity isn't buying another robot. It's getting the robots you already own to work more of the day. I have a question for operations leaders. If you could increase throughput by 20–30% without purchasing another robot, would your organization be willing to rethink how your fleet is powered? I'd love to hear your perspective—or if you'd like me to review your operation, send me a message. #amrdowntime #amr #uptime #charging #wireless

Reinforcement Learning for Multi-Robot Task Allocation! Coordinating heterogeneous robots to complete tasks efficiently is a major challenge in robotics. Centralized scheduling methods are slow, and traditional reinforcement learning struggles with cooperation and deadlocks. This research introduces a reinforcement learning-based framework for multi-robot task allocation and scheduling, enabling decentralized agents to dynamically form teams and minimize idle time. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 1. Attention-Based Coordination: Robots learn task dependencies and adapt their schedules in real time. 2. Constrained Flash forward Mechanism: Prevents deadlocks by guiding agent decisions and improving cooperative planning. 3. Decentralized Multi-Agent RL: Scales to large problems, avoiding the bottlenecks of mixed-integer programming (MIP) solvers. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? The framework achieves near-optimal task allocation, outperforming heuristic and optimization-based methods while being 100x faster.  It successfully scales to 150 robots and 500 tasks, demonstrating real-world potential for applications like search and rescue, logistics, and industrial automation. Kudos to Weiheng DAI, Utkarsh Rai, Jimmy Chiun, Yuhong Cao, and Guillaume Sartoretti! 🔗 Read the full paper: https://lnkd.in/gSER3_-D I post the latest and interesting developments in robotics - 𝗳𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! #ReinforcementLearning #MultiRobot #TaskAllocation #AI #Robotics #Automation #DeepLearning #Optimization