Raven Industries, Inc.

Thomas Antony

Rahul Ramakrishnan

Colin Josh Hurd

Quincy Calvin Milloy

Mark William Barglof

Jared Ernest Kocer

Thomas Dean Chadwick

Caleb Vaughn Friedrick

Andrew David Stelter

Aziz Alibasic

Dean Russell Kertai

Venkata Rama Karthik Paga

Joshua James Dalbert Friedrick

Control systems and methods for coordinating multiple agricultural machines for operation on an agricultural field are provided. Each machine may receive field data for an agricultural field that optionally includes a plurality of pre-defined swaths. Each machine may receive state data from other machine(s). The state data for the other machine(s) may include a next swath and a current swath. Each machine may, after an indication of a start of its current swath, determine a next swath based on open swaths and the state data for the other machine(s). A next swath may also be determined using vehicle kinematic data, and conflicts between machines may be resolved.

Multi-vehicle coordination systems and methods for agricultural field operations

A framework for safely operating autonomous machinery, such as vehicles and other heavy equipment, in an in-field or off-road environment, includes detecting, identifying, and tracking objects from on-board sensors configured with the autonomous machinery as it performs activities in either an agricultural setting or a transportation environment. The framework generates commands for navigational control of autonomously-operated vehicles in response to detected objects and predicted tracks thereof for safe operation in the performance of those activities. The framework processes image data and range data in multiple fields of view around the autonomously-operated to discern and track objects in a deep learning to accurately interpret this data for determining and effecting such navigational control.

Object detection and tracking for automated operation of vehicles and machinery

A framework for safely operating autonomous machinery, such as vehicles and other heavy equipment, in an in-field or off-road environment, includes detecting, identifying, classifying and tracking objects and/or terrain characteristics from on-board sensors that capture images in front and around the autonomous machinery as it performs agricultural or other activities. The framework generates commands for navigational control of the autonomous machinery in response to perceived objects and terrain impacting safe operation. The framework processes image data and range data in multiple fields of view around the autonomous equipment to discern objects and terrain, and applies artificial intelligence techniques in one or more neural networks to accurately interpret this data for enabling such safe operation.

Safety system for autonomous operation of off-road and agricultural vehicles using machine learning for detection and identification of obstacles

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Ames, IA, United States of America

Thomas Antony