Opportunity : Lawrence Livermore National Laboratory (LLNL), operated by the Lawrence Livermore National Security (LLNS), LLC under contract no. DE-AC52-07NA27344 (Contract 44) with the U.S. Department of Energy (DOE), is offering the opportunity to enter into a collaboration to further develop and license its novel collision avoidance technology for unmanned vehicles using sensor data and traffic management information. Background : Collision avoidance is necessary for autonomous vehicles to safely navigate an environment and avoid obstacles, other drones, planes, ground vehicles and even humans operating in the same environment. Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is a developing system for managing the airspace operations for unmanned aerial vehicles (UAVs) – airborne drones – to enable multiple beyond visual line-of-sight drone operations in airspace where FAA air traffic services are not provided. Future UTM capabilities are expected to enable cooperative interactions for the management of low-altitude uncontrolled drone operation. Similarly, integrating data from a central “traffic management” system– like a future smart city’s traffic controllers, a digitally-enabled warehouse’s command center or a vehicle fleet management hub – can help autonomous vehicles understand and move through their environment while avoiding obstacles, moving objects and people. Description : LLNL’s newest collision avoidance technology provides a fusion of known information, sensor data and UTM data to predictively plan the future position of all objects in a field of engagement. With the fused data, the UAV’s path planner can direct a flight controller to the optimal flight trajectory to avoid collisions and transit toward the vehicle's desired destination. LLNL’s new method for collision avoidance utilizes a novel algorithm to detect, track and avoid objects. This invention expands beyond LLNL’s existing sense and avoid methods in that it predicts the future position of detected obstacles and navigates to avoid present and future positions of the moving and stationary hazards. Advantages : Unlike existing systems, this method enables autonomous vehicles to efficiently use a multiplicity of data sources, including information from a UTM, to avoid colliding with objects while still achieving its navigation objectives. Potential Applications : Potential applications include: Autonomous vehicles traffic management systems Package delivery Remote inspection Emergency response Defense applications Drone-based filming and videography Robotics applications Development Status: This early-stage technology has been reduced to practice. Simulation of sensors and algorithm showed successful collision avoidance using the described method. LLNL is interested in collaborating with industrial partner(s) to mature this early-stage technology and integrate it with commercial and government systems. Of particular interest to LLNL is the application of this technology to drones and autonomous vehicles of interest to the U.S. Government. The technology could also be matured and integrated for commercial applications. A movie of the capabilities in computational collision sense and avoid strategies for radar-based sensors being developed at LLNL can be viewed at https://www.youtube.com/watch?v=u67wyYJTfdE LLNL has an available portfolio of U.S. and foreign patents and patent applications for its unmanned vehicle sense-and-avoid systems. This method is captured in the latest patent application in the portfolio, filed in November 2020. LLNL is seeking industry partners with a demonstrated ability to bring such inventions to the market. Moving critical technology beyond the Laboratory to the commercial world helps our licensees gain a competitive edge in the marketplace. All licensing activities are conducted under policies relating to the strict nondisclosure of company proprietary information. Please visit the IPO website at https://ipo.llnl.gov/resources for more information on working with LLNL and the industrial partnering and technology transfer process. Note: THIS IS NOT A PROCUREMENT . Companies interested in commercializing LLNL's Collision Avoidance Fusion with UAS Traffic Management should provide a written statement of interest, which includes the following: 1. Company Name and address. 2. The name, address, and telephone number of a point of contact. 3. A description of corporate expertise and facilities relevant to commercializing this technology. Written responses should be directed to: Lawrence Livermore National Laboratory Innovation and Partnerships Office
[email protected] Livermore, CA 94551-0808 Attention: FBO 499-21 Please provide your written statement within thirty (30) days from the date this announcement is published to ensure consideration of your interest in LLNL's Collision Avoidance Fusion with UAS Traffic Management.