Company: OTSA
Filing Date: 2025-07-16
Form Type: F-1/A
Source: 0001213900-25-064434
Chunk: 173

Company: OTSAW Ltd
Filing Date: 2025-07-16
Form: F-1/A
Chunk 173
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layeredsynergy of our advanced sensors and cameras, 3D -SLAMtechnology, and Sensor Fusion software enables our AMRs to achieve human -likevision. The AMRs capture a 360° range of stereoscopic vision: in front, behind, above, and beside the AMR. This provides an in -depthand expansive three -dimensionalview of the world similar to human vision. However, to be fully AI -Enabledand to safely, reliably, and efficiently navigate in unstructured and dynamic environments, the AMRs must also have the perceptive ability to analyze and comprehend their visual world and detect and classify objects at ranges to enhance their decision making. Our machine perception technology powers the AMRs with a human -likeperceptive ability to identify and distinguish between objects of varying motion, shape, reflectivity, and material composition. The machine perception technology enables our AMRs to collect the real -worldlive data points, which are then simultaneously combined with vision -baseddata, providing the AMR with information for an accurate understanding of their immediate surroundings. Continuous real -timedata analysis allows the AMRs to understand what is constant and what is changing in their environment, enhancing task comprehension and situational awareness. Thus, the AMRs can perform obstacle -avoidancemovements to evade any obstacles or humans in the AMRs’ path or proximity, even where multiple obstacles may be moving at different velocities without a pre -knownmotion profile. This capability allows the AMRs to reliably and safely navigate in busy, ever -changingdynamic outdoor and indoor environments. Fully AI -enabledAMRs must be able to learn, adapt, remember, and constantly improve their movements. Our proprietary AI principles and machine learning algorithms empower AMRs with the advanced ability to interact with their surrounding environments. Our AMRs can respond, adapt, learn, and recover from failures and unpredictability. The machine learning algorithm is designed to gather as much information as possible. Through repeated learning and adaption in proportion to the hours they operate, the AMRs can continuously enhance their understanding of their operating environment. The more information an AMR learns, the more intelligent it becomes. Over time, AMRs can more efficiently prioritize and filter collected data throughout its deployment and operation, improving its decision -makingability across various situations to ensure safety, efficiency and reliability. Fleet Management and Control System We developed a software platform, the Fleet Management and Control System, for our AMR users and operators. The platform easily integrates into the user’s existing system, offering high compatibility and rapid implementation