Project Showcase
Dive into the details of how we leverage technology to solve complex problems and deliver exceptional results for our clients.
Challenge:
Existing AMR systems struggled with dynamic environments and efficient multi-robot coordination.
Solution:
Implemented a SLAM-based localization and path planning algorithm combined with a centralized fleet management system using deep reinforcement learning for task allocation.
Outcome:
Achieved a 30% increase in operational efficiency and a 20% reduction in robot idle time.
Technologies:
Challenge:
High costs associated with unplanned downtime and reactive maintenance in a manufacturing plant.
Solution:
Developed a system that collects data from IoT sensors, processes it in real-time, and uses machine learning models (LSTMs and Random Forests) to predict potential failures with high accuracy.
Outcome:
Reduced unplanned downtime by 40% and maintenance costs by 25%.
Technologies:
Challenge:
Manual inspection was slow, error-prone, and inconsistent, leading to defects reaching customers.
Solution:
Utilized high-resolution cameras and a custom-trained convolutional neural network (CNN) to detect and classify defects in real-time. Integrated with robotic arms for automated sorting of defective parts.
Outcome:
Improved defect detection rate to 99.5% and increased production throughput by 15%.