Renu Robotics Corporation

Southwest Research Institute

Scythe Robotics, Inc.

Marc Christopher Alban

Kristopher Charles Kozak

Michael Odell Blanton, Jr

Tim A Matus

John Gordon Morrison

Zachary Austin Goins

Christopher I Mentzer

Samuel E Slocum

Jason C Gassaway

Ryan D Lamm

Richard D Garcia

Systems and techniques for generating a set of connected segments for a device or system to traverse in order to reach every point of the region (a coverage plan). Nodes defining the region to be traversed define a polygon. The polygon is decomposed into a mesh and a graph of the mesh is generated. The graph may be used to determine a longest funneled path which, in turn, may be used to either optimize for a longest path or to divide the polygon for eroding sides. The longest path and/or erosions are used to define a set of segments. The segments are connected, which in some examples is done via an optimization to minimize an amount of time or energy to traverse all segments and connections. The resultant coverage plan is sent to a system configured to receive the plan and traverse the region.

Coverage planner

Disclosed are solutions for an autonomous vehicle to perform an initial navigational setup at an operating site by: receiving initial location and orientation data pertaining to the autonomous vehicle; iteratively sensing a plurality of recognizable objects and determining a plurality of fixed reference locations corresponding to each of the plurality of recognizable objects relative to the initial location and orientation data; and developing an operational path plan for the autonomous vehicle to traverse the operating site based on the plurality of fixed reference locations. The autonomous vehicle may be an autonomous mower, the dynamic object may be a solar panel and/or solar panel post, and the sensing may be performed using light detection and ranging (LIDAR).

Autonomous vehicle site setup

Disclosed are solutions for proactively sensing the condition of the ground in the path of the autonomous vehicle in order to mitigate the risks of an autonomous vehicle encountering troublesome terrain, navigation obstacles, and other potential risks to the continued uninterrupted operation of said autonomous vehicle. Accordingly, various implementations disclosed herein are directed to the use of ground analysis sensors to assist in the detection of navigationally difficult terrain including, for example, the use of cantilevered sensors operating well in front of the autonomous vehicle's front wheels (drive wheels or otherwise).

Autonomous vehicle terrain prediction and detection

Disclosed are solutions for an autonomous vehicle to real-time detect and determine dynamic and/or obscured obstacles to support navigation and services to within a desired proximity of said obstacles. Certain such implementations are specifically directed to autonomous mowers, for example, capable of real-time object detection to determine location and orientation of solar panels in a solar farm, for example, and based on the location and orientation of such solar panels further determine the location of their corresponding posts that may be otherwise obstructed from direct detection by the autonomous mower's other sensing systems possibly due to vegetation growth around the posts or other reasons.

Autonomous vehicle panel and post detection

Disclosed are solutions for autonomous vehicle navigation that, in the context of an exemplary solar farm, enable the autonomous vehicle to detect solar panels directly, estimate the centerline of the panels regardless of the tilt angle, and then determine the distance and/or direction (globally or relative to the vehicle) from the centerline to the autonomous vehicle in order to then estimate the position of the latter (that is, where the centerline of the solar panel becomes a surrogate for a static, fixed feature for purposes of navigation.).

Autonomous vehicle navigational fallover

An autonomous mowing system comprising: a memory configured to hold a set of path data defining a set of paths, the set of paths comprising a transit path to a maintenance area and a set of mow paths to cover the maintenance area; a processor coupled to the memory; a computer-readable medium coupled to the processor, the computer-readable medium storing a set of instructions executable by the processor, the set of instructions comprising instructions for: using the set of path data, determining a plurality of candidate routes that traverse the transit path and fully cover the maintenance area and a total cost for each of the plurality of candidate routes; determining a least cost route from the plurality of candidate routes; and configuring an autonomous mower to follow the least cost route to mow the maintenance area.

Autonomous vehicle systems and methods

An unmanned vehicle system, having at least one autonomous ground vehicle (A-GV) and at least one remote controlled aerial vehicle (RC-AV). The A-GV autonomously navigates across the ground, and on-board perception sensors, whose field of view that contains at least a portion of the ground ahead of the direction of travel of the ground vehicle as well as the RC-GV. The RC-AV flies in response to commands received from the control system of the A-GV. The RC-AV has on-board in-sky perception sensors having a field of view that contains at least a portion of the ground path to be followed by the ground vehicle. The control system of the A-GV locate the aerial vehicle, receives perception data from the on-ground perception sensors and the in-sky perception sensors, and uses combined perception data to determine a ground path for itself and for the RC-AV.

Unmanned ground/aerial vehicle system having autonomous ground vehicle that remotely controls one or more aerial vehicles

A system and method for estimating location and motion of an object. An image of a ground surface is obtained and a first set of features is extracted from the image. A map database is searched for a second set of features that match the first set of features and a geo-location is retrieved from the map database, wherein the geo-location is associated with the second set of features. The location is estimated based on the retrieved geo-location. The motion of the object, such as distance travelled, path travelled and/or speed may be estimated in a similar manner by comparing the location of extracted features that are present in two or more images over a selected time period.

Growing community of inventors

San Antonio, TX, United States of America

Marc Christopher Alban