Safeai, Inc.

Lalin Theverapperuma

Bibhrajit Halder

Koushik Balasubramanian

Systems and methods enable an autonomous vehicle to perform an iterative task of transferring material from a source location to a destination location, such as moving dirt from a pile, in a more efficient manner, using a combination of reinforcement learning techniques to select a motion path for a particular iteration and visual servo control to guide the motion of the vehicle along the selected path. Lifting, carrying, and depositing of material by the autonomous vehicle can also be managed using similar techniques.

Systems and methods for transfer of material using autonomous machines with reinforcement learning and visual servo control

The present disclosure relates generally to identification of drivable surfaces in connection with autonomously performing various tasks at industrial work sites and, more particularly, to techniques for distinguishing drivable surfaces from non-drivable surfaces based on sensor data. A framework for the identification of drivable surfaces is provided for an autonomous machine to facilitate it to autonomously detect the presence of a drivable surface and to estimate, based on sensor data, attributes of the drivable surface such as road condition, road curvature, degree of inclination or declination, and the like. In certain embodiments, at least one camera image is processed to extract a set features from which surfaces and objects in a physical environment are identified, and to generate additional images for further processing. The additional images are combined with a 3D representation, derived from LIDAR or radar data, to generate an output representation indicating a drivable surface.

Drivable surface identification techniques

The present disclosure relates generally to the operation of autonomous machinery for performing various tasks at various industrial work sites, and more particularly to the volumetric estimation and dimensional estimation of a pile of material or other object, and the use of multiple sensors for the volumetric estimation and dimensional estimation of a pile of material or other object at such work sites. An application and a framework is disclosed for volumetric estimation and dimensional estimation of a pile of material or other object using at least one sensor, preferably a plurality of sensors, on an autonomous machine (e.g., robotic machines or autonomous vehicles) in various work-site environments applicable to various industries such as, construction, mining, manufacturing, warehousing, logistics, sorting, packaging, agriculture, etc.

Techniques for volumetric estimation

In one aspect, In one aspect, a method for data transfer and processing communications is provided. The method includes the step of providing a machine-to-everything (M2X) application layer on each machine of the plurality of machines. The method includes the step of providing a plurality of communication nodes on each machine for communication between the plurality of machines with every other machine, the plurality of machines and any infrastructure at a work site, and a plurality of communication nodes communicating using the at least one application layer. The method includes the step of providing a communication processing system for receiving a data transfer and processing communications. The communication processing system includes a plurality of processing stations, one or more multiple data management protocols, a plurality of network protocols, a plurality of databases and plurality of data processing network architectures.

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Santa Clara, CA, United States of America

Lalin Theverapperuma