Abstract: The present invention relates, in general, to systems and methods for controlling autonomous forklifts, and specifically, for maneuvering autonomous forklifts in environments with hanging objects such as plastic sheets, curtains, and other suspended objects. The invention determines whether or not a detected object is located on a pallet carried by the autonomous forklift, and subsequently navigates the autonomous forklift in a manner that either avoids the object if it is not located on the pallet, or if the object is larger than a threshold size which could impede safe travel.

Abstract: The present invention relates, in general, to systems and methods for controlling autonomous forklifts, and specifically, for maneuvering autonomous forklifts in environments where there is low overhead clearance or overhead obstacles. The invention determines the height of an overhead obstacle as well as the height of a pallet carried by the autonomous forklift, and also determines if the overhead obstacle is deformable or non-deformable. The autonomous forklift is commanded to either traverse through the overhead obstacle if the height of the pallet is smaller than the height of the overhead obstacle, or if the overhead obstacle is deformable, such as in the case of weather guards surrounding a dock door.

Abstract: The present invention relates, in general, to systems and methods for controlling autonomous forklifts, and specifically, for maneuvering autonomous forklifts in environments with hanging objects such as plastic sheets, curtains, and other suspended objects. The invention determines whether or not a detected object is located on a pallet carried by the autonomous forklift, and subsequently navigates the autonomous forklift in a manner that either avoids the object if it is not located on the pallet, or if the object is larger than a threshold size which could impede safe travel.

Abstract: A method and a system for deep learning-based perception are disclosed. The method includes obtaining input data using a plurality of cameras and a plurality of sensors, the input data including a plurality of images and a plurality of sensor data and training, using a machine learning algorithm, a trunk-head machine learning model. Further, the method includes generating an intermediate representation data using the trunk-head machine learning model and determining a plurality of information recognized in the intermediate representation data using the trunk-head machine learning model and based on the obtained input data. A configuration of a forklift is adjusted based on the determined plurality of information.

Abstract: A forklift autonomous operation system is disclosed. The forklift autonomous operation system includes a forklift having a load handling system, the load handling system including a mast and a plurality of forks and a camera, coupled to the load handling system, for obtaining visual input data of an environment. Further, the system includes 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.

Abstract: Systems, methods, and computer program products for programming a controller to control a process. A training module is trained to provide predicted responses of the process to control signals using a generic simulator to model the process. Once the training module has been trained, the controller is programmed to control the process by coupling the controller to the training module and using the training module to generate predicted responses of the process to the reception of control signals from the controller. As part of the programming process, one or more parameters of a control program resident on the controller may be adjusted to reduce errors between the responses predicted by the training module and the desired responses intended to be caused by the control signals.

Abstract: Systems, methods, and computer program products for programming a controller to control a process. A training module is trained to provide predicted responses of the process to control signals using a generic simulator to model the process. Once the training module has been trained, the controller is programmed to control the process by coupling the controller to the training module and using the training module to generate predicted responses of the process to the reception of control signals from the controller. As part of the programming process, one or more parameters of a control program resident on the controller may be adjusted to reduce errors between the responses predicted by the training module and the desired responses intended to be caused by the control signals.