In this webinar, we will cover the following key aspects:

The Mathematics of Safety: 3Laws' safety approach is built on Control Barrier Functions (CBFs), a technology developed at Caltech and pioneered by the company's founders. This mathematical precision enables the system to predict and prevent collisions using rigorous models rather than heuristic or trial-and-error methods. By leveraging real-time perception and localization data, the system computes safe commands that consider the robot's physical capabilities, braking ability, and uncertainty in dynamic environments.
Safety Filtering Layer: Acting as a real-time safety filter, the 3Laws Supervisor sits between the autonomy stack and embedded control system, intercepting and modulating commands to ensure only safe actions are executed. Key features include collision avoidance, geofencing, and reactive velocity filtering based on cost maps. Remove the burden of autonomy with 3Laws Supervisor.
Making a Universal Safety Layer: Designed as a plug-and-play module, the 3Laws software can integrate effortlessly with a variety of platforms, including mobile robots, drones, cars, boats, manipulators, and airplanes, while supporting any sensor technology. This enables small teams to manage diverse fleets across industries using the same safety layer. By separating safety from the autonomy stack, 3Laws allows for faster, safer innovation while ensuring scalability and adaptability across robotic systems.

Autonomous Mobile Robots (AMRs): Enhancing safety and efficiency in warehouse automation and logistics within dynamic environments.
Autonomous Vehicles: Supporting safety for self-driving cars, trucks, and boats.
Drones: Ensuring safe drone operation in unmapped terrain.
Aerospace: Highlighting from collaborations with the US Air Force.
Manipulators: Improving safety and precision in robotic manipulators used in manufacturing and industrial settings.

Webinar: Dynamic Safety Assurance of Autonomous Systems