The Toro Company

Eaton Intelligent Power Limited

Jason Thomas Kraft

Alexander Steven Frick

Ryan Douglas Ingvalson

Michael Berne Rannow

David LaRose

Meng Wang

Aaron Y Curtis

Khari S Clarke

Kevin D Docken

Adam Burghardt Stroud

Adam Richard Williams

Peter M Arendt

Stephen Darrell Smith

Michael Jason Ramsay

Christopher Charles Osterwood

Zachary Irvin Parker

Stephen Paul Elizondo Landers

Brian Daniel Beyer

Gregory S Janey

Timothy Bertram Cleveland

David Arthur Larose

Shelley Allison Nation

Kevin Dale Docken

Khari Sekou Clarke

Aaron Yarrow Curtis

An autonomous machine may be returned to a base station for charging based on remaining battery energy and an estimated travel energy threshold. The estimated travel energy threshold may be determined based on a direct and obstacle-free route from the machine's current position to the base station and an estimated energy consumed per unit distance, which may be updated. The remaining battery energy may be calculated using a battery management system.

Autonomous machine navigation and charging

Apparatus, systems, and methods for directing an autonomous robotic vehicle such as a lawn mower relative to a work region. In some embodiments, the vehicle travels in a random pattern within a travelling containment zone of a lesser size than the work region. The travelling containment zone may move or travel across the work region such that, over time, the travelling containment zone travels over most all of a working surface of the work region.

System and method for operating an autonomous robotic working machine within a travelling containment zone

Operating an autonomous grounds maintenance machine includes determining a travel path for the machine to reach a destination waypoint in a zone of a work region. The travel path is analyzed for problem areas based on a predetermined terrain map. Rotations of the machine may be planned even before the machine begins to travel down the path to proactively prevent the machine from becoming trapped.

Autonomous grounds maintenance machines with path planning for trap and obstacle avoidance

This disclosure provides an autonomous machine system having a scheduling controller configured to determine one or more windows of availability over a time period when the autonomous machine is allowed to operate to perform one or more operational tasks; determine a total operation time over the time period; and determine an operating schedule for a work region that assigns one or more operational tasks to the one or more windows of availability based on the total operation time. The autonomous machine may be commanded to operate in the work region according to the operating schedule.

Smart scheduling for autonomous machine operation

A handle assembly for an autonomous vehicle such as a lawn mower is provided and may include a cradle adapted to receive a mobile computer. The handle assembly may be configured in both an autonomous mode position and a manual mode position. The vehicle may operate autonomously when the handle assembly is in the autonomous mode position, and be adapted to move under manual operator control, e.g., for perimeter training, when the handle assembly is in the manual mode position.

Handle assembly for autonomous vehicle

Autonomous machine navigation techniques may generate a three-dimensional point cloud that represents at least a work region based on feature data and matching data. Pose data associated with points of the three-dimensional point cloud may be generated that represents poses of an autonomous machine. A boundary may be determined using the pose data for subsequent navigation of the autonomous machine in the work region. Non-vision-based sensor data may be used to determine a pose. The pose may be updated based on the vision-based pose data. The autonomous machine may be navigated within the boundary of the work region based on the updated pose. The three-dimensional point cloud may be generated based on data captured during a touring phase. Boundaries may be generated based on data captured during a mapping phase.

Autonomous machine navigation and training using vision system

Systems and methods for estimating the area ratio of an actuator in static and dynamic states are disclosed. In one aspect, a metering valve is connected to each side of the actuator. In one example, one metering valve is held closed while the other metering valve incrementally pressurizes the actuator in discrete steps. The resulting work port pressures can be used to determine the actuator area ratio. Where counterbalance valves are installed in the system, the pressurizing metering valve can be placed in a pressure control mode to obtain the desired pressure values. In one example, the ratio of flows through each metering valve is used to determine the actuator ratio.

Method to automatically detect the area ratio of an actuator

An online method for reconfiguring pressure and position sensors in a hydraulic system is disclosed. In one step, a sensor drift condition, a recalibration request, or an unisolated fault condition is detected. In another step, a system pressure sensor or another sensor, such as a load-sense pressure sensor, is verified as a trusted master reference sensor. Another step includes measuring and recording a first pressure reading at the master reference sensor and first voltage readings associated with first, second, third, and fourth pressure slave sensors at a first pump pressure set point. Another step includes, repeating the previous step at a second pump pressure set point. A new gain and offset for each of the first, second, third, and fourth pressure sensors can be calculated based on a comparison of the recoded first and second pressure readings and the recorded first and second voltage readings.

Online sensor calibration for electrohydraulic valves

Growing community of inventors

Stillwater, MN, United States of America

Jason Thomas Kraft