With the rapid development of autonomous driving and large-scale industrial scenes, emulators play an increasingly important role in the development of robot systems. It has gradually become a mainstream technology in the industry to use emulators capable of rendering fine scenes to simulate real driving scenes and collect data.
According to ABI Research, the number of industrial and commercial robots installed will grow more than 6.4 times from 3.1 million in 2020 to 20 million in 2030. To develop, validate, and deploy these new AI robots, companies will need to use simulation techniques to put these robots into real-world scenarios.
NVIDIA has announced the release of NVIDIA Isaac Sim 2022.2. As a robot simulation and synthetic data generation (SDG) tool, the NVIDIA Omniverse application accelerates the development, testing, training and deployment of intelligent robots.
With Isaac Sim, roboticists can build realistic environments that verify a robot's physical design and adequately train the robot's software stack to ensure performance. In addition, synthetic data sets can be built and used to train the AI model in the robot perception system during the simulation. Researchers can use reinforcement learning to train models in the robot control stack.
The latest version focuses on improving the performance and functionality of manufacturing and logistics robot use cases. Adding people to the simulation environment is now supported. More assets and popular robots are pre-integrated to reduce the time before simulation.
In the field of robotics, Nvidia has made a big layout. In addition to deep involvement in various industrial businesses, it also hired industry experts on a large scale to provide ideas and direction for the development of robot simulators. Isaac Sim was born from the accumulation of physics simulation, graphics rendering, autonomous driving and robotics research.
NVIDIA Isaac Sim Version 2022.2 highlights
Warehouse logistics: Warehouse logistics is one of the most important areas of robotics innovation. Several tools in Isaac Sim can be used to simulate warehouse robots. The previous version included a warehouse building tool. Isaac Sim now includes a tool for building a real conveyor belt. There is also a people simulator that adds digital people to the warehouse environment. This will help validate the perception and safety systems of robots working near humans.
Manufacturing: Robotic robots have long been used to automate processes in manufacturing environments. Version 2022.2 has many new features to deal with the motion control of robotic robots. An updated motion generation extension in Isaac Sim simplifies the integration and benchmarking of motion control algorithms in simulations. The included algorithm, RMPFlow, creates a smooth trajectory for the robot with intelligent collision avoidance function. In addition to improved performance, this release offers a number of usability improvements, including a graphical editor for Lula's robot description file.
Fleet Optimization: Launch of the cuOpt extension for Isaac Sim, a deployment of NVIDIA's cuOpt engine that integrates directly with Isaac Sim. NVIDIA cuOpt is an operational research optimization API that helps developers create complex real-time fleet routes. These apis can be used to solve complex routing problems with multiple constraints and provide new capabilities such as dynamic rerouting, job scheduling, and robot routing while taking advantage of subsecond solver response times.
ROS support upgrade: ROS support has been upgraded to support ROS 2 Humble, and Isaac ROS, also based on the Humble version, can be easily simulated and tested in this release. ROS 2 support has also been added to Windows machines.
Research Tools: Key new features designed for robotics researchers include Isaac Gym (reinforcement learning) performance improvements. A new example of the Isaac Cortex for collaborative robot programming. Finally, a new open source tool, Isaac ORBIT, provides a simulated operating environment and benchmark for robot learning and motion planning.