Abstract: The embodiments herein disclose a switchover asymmetric H-Bridge circuit for series and parallel mode operation of a switched reluctance motor (SRM) motor. In an embodiment, the proposed switchover asymmetric H-Bridge circuit is used to achieve higher inductance and torque at lower speeds and lower winding inductance to reach higher speeds of operation. In an embodiment, an asymmetric H-Bridge topology that has been modified to support series-parallel switch-over by using only two extra devices (MOSFET switches). Further, the ability to switch between higher inductance/torque production and lower inductance/high speed operation.

Abstract: The embodiments herein provide a multitude of single-phase Switch Reluctance Motor (SRM) arranged in a suitable fashion in order to achieve uniform torque with complete material utilization. The embodiment herein also provides a series of single-phase Switch Reluctance Motor (SRM) arranged in appropriate manner for better utilization of material leading to better efficiencies, reduced cost, reduced size or weight and reduction in torque ripple and noise. In addition, the embodiments herein also provide a two-slice SRM system and method, which correspondingly resolves the starting problem.

Abstract: Systems and methods enable an autonomous vehicle to perform an iterative task of transferring material from a source location to a destination location, such as moving dirt from a pile, in a more efficient manner, using a combination of reinforcement learning techniques to select a motion path for a particular iteration and visual servo control to guide the motion of the vehicle along the selected path. Lifting, carrying, and depositing of material by the autonomous vehicle can also be managed using similar techniques.

Abstract: The present disclosure relates generally to the operation of autonomous machinery for performing various tasks at various industrial work sites, and more particularly to the volumetric estimation and dimensional estimation of a pile of material or other object, and the use of multiple sensors for the volumetric estimation and dimensional estimation of a pile of material or other object at such work sites. An application and a framework is disclosed for volumetric estimation and dimensional estimation of a pile of material or other object using at least one sensor, preferably a plurality of sensors, on an autonomous machine (e.g., robotic machines or autonomous vehicles) in various work-site environments applicable to various industries such as, construction, mining, manufacturing, warehousing, logistics, sorting, packaging, agriculture, etc.

Abstract: The present disclosure relates generally to the operation of autonomous machinery for performing various tasks at various industrial work sites, and more particularly to the volumetric estimation and dimensional estimation of a pile of material or other object, and the use of multiple sensors for the volumetric estimation and dimensional estimation of a pile of material or other object at such work sites. An application and a framework is disclosed for volumetric estimation and dimensional estimation of a pile of material or other object using at least one sensor, preferably a plurality of sensors, on an autonomous machine (e.g., robotic machines or autonomous vehicles) in various work-site environments applicable to various industries such as, construction, mining, manufacturing, warehousing, logistics, sorting, packaging, agriculture, etc.

Abstract: Systems and methods enable an autonomous vehicle to perform an iterative task of transferring material from a source location to a destination location, such as moving dirt from a pile, in a more efficient manner, using a combination of reinforcement learning techniques to select a motion path for a particular iteration and visual servo control to guide the motion of the vehicle along the selected path. Lifting, carrying, and depositing of material by the autonomous vehicle can also be managed using similar techniques.