PROBLEM STATEMENT

Conventional Robots Fails in Complex Smart Manufacturing Operations

Icon to represent the lack of flexibility in traditional robot arm applications.

Inflexible Automation in Dynamic Environments

Traditional robots fail in dynamic environments (slight part variations, environmental changes, and unpredictable production scenarios).

Icon to represent how traditional robot arm applications cause higher costs due to cost of high manual work.

Inefficiencies Due to Costly Delays & Rework

Any design change or new product variant requires manual reprogramming—slow, costly, and skill-intensive.

Icon to represent how traditional robot arm use cases have limited adaptability

Limited Adaptability & Single-Purpose Systems

Rigid automation limits adaptability—traditional robots require extensive adjustments for even small process modifications.

Icon to represent the difficulties in scaling robot arm use cases to a fleet

Scaling Constraints & Integration Complexity

Scaling is restricted by proprietary systems, complex integrations, and limited flexibility in multi-robot environments.

TECHNOLOGIES BEHIND

How robolaunch AIR works?

STEP 1

See

AI-POWERED PERCEPTION

robolaunch AIR integrates 3D vision to enable robots to understand their environment in real time. AI-powered cameras and sensors capture object geometry, and track motion dynamically. The system continuously scans surfaces and adapts to production variations, ensuring precise execution without manual intervention.

AI-powered robotic arm with 3D vision system scanning a surface for real-time object localization and precise motion guidance.

STEP 2

Think

SMART MOTION & TASK PLANNING

With real-time AI processing, robolaunch AIR optimizes robotic motion to generate collision-free paths and dynamically adjust based on environmental changes. Instead of following rigid pre-programmed trajectories, the system continuously refines movement strategies, switching between tasks such as welding, inspection, pick-and-place, and surface treatment without requiring reprogramming.

AI-driven robotic system analyzing object positions and planning optimal motion paths for collision-free, efficient task execution.

STEP 3

Act

INTELLIGENT EXECUTION, CONTINUOUS LEARNING

At the execution stage, edge-based AI inference ensures that robots react instantly to changes, eliminating cloud-processing delays. Autonomous real-time motion control fine-tunes force, speed, and positioning, while the system continuously learns from past tasks to improve efficiency. Over time, robots become smarter, optimizing accuracy and productivity with each cycle.