Aceinna Inc. announced that Aceinna has been selected as a winner of the 2020 RBR50 Robotics Innovation Awards.
Aceinna earned recognition in the Product, Technology & Services Innovation (Product Introduction) category for the new OpenRTK33L guidance module for robots, drones, and autonomous vehicles.
The Aceinna OpenRTK330L is a low cost, state-of-the-art, high-performance triple-band RTK/GNSS receiver with built-in triple redundant inertial sensors. Designed to replace the expensive and bulky precision RTK/INS systems used in today’s autonomous systems, this compact navigation solution meets the challenging performance, reliability and cost requirements of the automotive market along with the needs of robot, drone, construction and agriculture systems.
According to Dan Kara, VP Robotics & Intelligent Systems at WTWH Media:
“Each year since 2012, Robotics Business Review, the leading source of analysis, opinion and research focused on the global robotics sector, has produced the RBR50 Robotics Innovation Awards (RBR50). The awards recognize and highlight critical robotics innovations and are also an important indicator of the robotics sector growth. The selection committee received numerous submissions for the 2020 RBR50 Robotics Innovation Awards. Winners were determined following a rigorous vetting and review process.”
Aceinna’s OpenRTK330L integrates a triple-band RTK/GNSS receiver with redundant inertial sensor arrays to provide cm-level accuracy, enhanced reliability, and superior performance during GNSS outages.
The OpenRTK330L utilizes a very precise 2 Degree/Hour IMU to offer ten to thirty seconds of high accuracy localization during full GNSS denial. This enables autonomous system developers to safely deliver highly accurate localization and position capabilities in their vehicles at prices that meet their budgets. OpenRTK330L‘s embedded Ethernet interface allows easy and direct connection to GNSS correction networks around the world. OpenRTK330L‘s CAN bus interface allows simple integration into existing vehicle architectures.