Robotics and Autonomous Machines
Robotics and Autonomous Machines
Every industry strives to enhance operations, efficiency, and safety by increasing automation. The use of autonomous machines/robots is changing the way businesses are run, from repetitive factory floor work to operations in warehouses, agriculture, logistics, surveillance, and other sectors. Autonomous robots are robotic devices capable of operating and navigating in dynamic environments without constant human supervision or control. Using a navigation technique like collision avoidance to slow, stop, or redirect their journey around the obstacle, and then resume their activity. They have several cameras and sensors, as well as AI/ML algorithms and onboard processing. Manufacturing, logistics, healthcare, retail, and smart cities are all large industries that have invested heavily in the deployment of autonomous robots.
To enable Autonomous Mobile Robots and Machines, eInfochips has built a Robotics Centre of Excellence (CoE) with a strong team of subject matter specialists in hardware design, sensor integration, AI/ML, and camera development. eInfochips develops autonomous systems for intelligent navigation, remote monitoring, surveillance, and industrial automation. eInfochips has multiple strategic partnerships with leading platform suppliers like as NVIDIA, Qualcomm, and ADI, through which our team has developed several proof-of-concepts using their robotics-specific hardware and software tool chain. We have a strong partner ecosystem and are dedicated to contributing to collaborative innovation with our clients.
Why eInfochips?
In-house depth sensor benchmarking lab
Extensive understanding of the NVIDIA robotics suite, platforms, ROS1/2, Micro ROS, Isaac ROS gems, AI/ML, CUDA
Strategic partnerships with technology leaders such as NVIDIA, Qualcomm, NXP, and ADI
Deep experience in AI/ML, model development, and training
Cloud enablement partnerships with Amazon and Microsoft
State-of-the-art infrastructure – Hardware Lab, Image Tuning Lab, and Wireless / RF testing lab
eInfochips Services
Hardware Design
- Hardware Design (PCB design, PoC)
- Power Optimization
- Firmware Embedded Services
- Edge Computing (NVIDIA, Qualcomm, Ambarella, NXP)
- Algorithms: VSLAM, Stereo depth estimation, navigation
Digital
- Software: ROS2, ROS1, OpenCL, Docker, Containers, CUDA, drivers
- IoT Solutions & Services
- Cloud Services & DevOps
- Mobility Solutions
- AI/ML/DL Services
- Remote Device Management
- Digital Twin (Scene creation and Simulations , URDF, Gazebo, ISAAC SIM)
Sensing
- Camera design services
- Image Tuning
- Sensor Integration (Camera tuning, Depth camera, LiDAR 2D/3, IMU)
- Sensor Fusion
Quality
- Product & IoT Testing
- QA Automation
- Cognitive QA
- Mobile/ Web Testing
- Compliance & Certification
Connectivity
- RF Antenna Services
- Wireless Testing
- Cloud Connectivity
Others
- Re-engineering
- Industrial/ Mechanical Design
- Electrical Design
- Sustenance/ Obsolescence
Kits and Modules
Qualcomm – QRB5165 Robotics platform
Nvidia
Jet carrier board
Jet carrier board
NXP – i.MX93 development platform
Blogs and Success Stories
Autonomous Machines: Technologies & Platforms to lookout
Today, the finances and resources spent on research and development in robotics and automation technology have seen an exponential increase. With advancements in technology, we have seen the adoption of robots across various sectors including industrial, manufacturing, consumer, and many more.
From Concept to Completion: Streamlining 3D Scene Creation for Robotics with Nvidia Isaac Sim and Blender
The article discusses the benefits and challenges of combining Blender and Nvidia Isaac Sim to create realistic simulations for robotics applications. By integrating Blender's 3D modeling and animation capabilities with Nvidia Isaac Sim's simulation software, more detailed and accurate models can be developed for testing and refining algorithms.
Sensor Fusion – Part 1
This blog is the first part of a two-part series in which we will introduce the usage of sensor fusion in Autonomous Mobile Robots (AMRs) that are enabled to work with ROS 2 (Robot Operating System 2). In this part, we will introduce sensor fusion, briefly cover a sensor fusion algorithm called EKF (Extended Kalman Filters), and then walk through some of the experiments we did in simulation and on our AMR.
Sensor Fusion – Part 2
This blog is the second part of a two-part series where we will introduce the usage of sensor fusion in Autonomous Mobile Robots (AMRs) that are enabled to work with ROS (Robot Operating System) 2. In the first part, we introduced sensor fusion, briefly covered a sensor fusion algorithm called EKF (Extended Kalman Filters), and then walked through some of the experiments we did in simulation and on our AMR.
Success Stories
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