METHOD AND SYSTEM FOR OPERATING AUTOMATED FORKLIFT

1 . A forklift autonomous operation system, the system comprising:

a forklift having a load handling system, the load handling system including a mast and a plurality of forks;

a camera, coupled to the load handling system, for obtaining visual input data of an environment;

a plurality of sensors coupled to the forklift for obtaining sensor data; and

a control system configured to process the visual input data and the sensor data.

2 . The system of claim 1 , further comprising:

an operator's compartment for a manual control of the forklift, the operator's compartment including a manual control system; and

a monitor for displaying the visual input data and the sensor data and manually commanding the forklift.

3 . The system of claim 1 , wherein the plurality of sensors further comprises:

an Inertial Measurement Unit (IMU) measuring a linear acceleration of the forklift, a rotational movement of the forklift, and an orientation of the forklift with respect to Earth's magnetic field;

a plurality of wheel encoders measuring orientation and rotation of a plurality of wheels of the forklift; and

a Light Detection and Ranging (LiDAR) using laser light beams to measure a distance between the forklift and surrounding objects.

4 . The system of claim 1 , wherein the load handling system further comprises:

a plurality of hydraulic tilt cylinders for tilting the mast forward and rearward; and

a plurality of hydraulic lift cylinders for lifting the mast upward and downward; and a plurality of hydraulic or electric cylinders for shifting the plurality of forks side to side.

5 . The system of claim 2 , wherein the operator's compartment includes a seating and a standing setting for the operator.

6 . The system of claim 2 , wherein the manual control system includes an acceleration pedal, a steering wheel, a forward and backward control lever, a lift control lever, and a tilt control lever.

7 . The system of claim 3 , wherein the IMU includes an accelerometer, a gyroscope, a magnetometer, and a pressure sensor.

8 . The system of claim 3 , wherein a map of forklift's environment is generated based on the measured distance between the forklift and the surrounding objects by the LiDAR.

9 . The system of claim 1 , wherein the camera captures a position of the plurality of forks.

10 . The system of claim 1 , wherein the control system further comprises:

a microcontroller operating an autonomy computer and a vehicle controller; and

a battery; and

a communication module supporting a plurality of communication standards for communication between external systems and the forklift.

11 . The system of claim 10 , wherein the autonomy computer further comprises:

a sensing module obtaining the visual input data and the sensor data and time-correlating the obtained data; and

a localization module determining a location of the forklift based on the obtained data;

a perception module analyzing the obtained data and determining surrounding objects within the environment;

a planning module determining an action to be executed by the forklift; and

a validation planning module monitoring the environment to avoid collisions with the surrounding objects.

12 . The system of claim 10 , wherein the vehicle controller further comprises:

a motor controller that controls a plurality of motors integrated into a plurality of wheels, a plurality of hydraulic tilt cylinders, and a plurality of hydraulic lift cylinders; and

a plurality of discrete controllers controlling the mast and the plurality of forks.

13 . The system of claim 12 , wherein the motor controller reports to the vehicle controller using a controller area network.

14 . The system of claim 11 , wherein the perception module further comprises functionality for:

determining a location of an entrance door to a storage using a machine learning model and based on the obtained data; and

determining a plurality of pallets' face-side pockets using the machine learning model based on the obtained data; and

determining a configuration of the plurality of forks using the machine learning model based on the obtained data; and

determining whether a pallet is unsafe to extract due to a presence of load restraints, including dunnage air bags and straps.

15 . The system of claim 14 , wherein the machine learning model is a neural network.

16 . The system of claim 14 , wherein the planning module further comprises functionality for:

adjusting the configuration of a plurality of forks based on the determined configuration of the plurality of fork with respect to the plurality of pallets' face-side pockets; and

determining a final position of the pallet using the machine learning model based on the obtained data.

17 . The system of claim 16 , wherein the final position of the pallet is a first available drop off location.

18 . The system of claim 3 , wherein the plurality of forks is tilted at an angle when a location of a pallet is below a location of the forklift.

19 . A stand-up counterbalanced three-wheeled forklift vehicle configured to:

load a plurality of pallets autonomously by following a first predetermined navigation path to and from a trailer housing the plurality of pallets to a warehouse floor or other staging location and based on a predetermined ordering and load configuration; and

unload the plurality of pallets autonomously by following a second predetermined navigation path to and from the trailer housing pallets to the warehouse floor or the other staging location.