Blue White Robotics Ltd

Ben Alfi

Aviram Shmueli

Alon Ascher

Shmuel Ur

Paz Aranyi

Dor Halperin

Ron Berenstein

Disclosed herein is a Real Time Kinematics (RTK) brokering system for distributing RTK positioning data to one or more vehicles, comprising an RTK server adapted to receive positioning data of a plurality of vehicles, dynamically define, based on the positioning data, a plurality of dynamically defined zones each associated with a respective group of vehicles located within borders of the respective dynamically defined zone and contained within a coverage area of one or more RTK base stations, receive RTK positioning correction data relating to each dynamically defined zone from one or more of the RTK base stations covering the respective dynamically defined zone, and distribute the RTK positioning correction data relating to each dynamically defined zone to the respective group of vehicles associated with the respective dynamically defined zone. Wherein one or more of the vehicles computes its location with improved accuracy based on the related RTK positioning correction data.

Brokering real time kinematics (RTK) positioning data for dynamically defined zones

Disclosed here are methods and systems for automatically operating automated vehicles moving through vegetation obstacles with minimal damage, comprising receiving image(s) depicting vegetation obstacle(s) blocking at least partially a path of an automated vehicle executing a mission, analyzing the image(s) to extract one or more obstacle attributes of the vegetation obstacle(s), computing a plurality of movement patterns for operating the automated to cross the vegetation obstacle(s) based on one or more vehicle attributes of the automated vehicle with respect to one or more of the obstacle attributes where each movement pattern defines one or more movement parameters of the automated vehicle, selecting one of the movement patterns estimated to reduce a cost of damage to the automated vehicle and/or to the one or more vegetation obstacles, and outputting instructions for operating the automated vehicle to move through the vegetation obstacle(s) according to the selected movement pattern.

Advanced movement through vegetation with an autonomous vehicle

Described herein are methods and systems for automatically operating autonomous vehicles to accomplish a coverage task by receiving a plurality of task parameters defining a coverage task in a certain geographical area, calculating and outputting instructions for operating first autonomous vehicle(s) to cover the certain geographical area according to a first movement path computed according to operational parameters of the first autonomous vehicle with respect to the task parameters, identifying uncovered segment(s) in the certain geographical area by analyzing coverage of the certain geographical, and calculating and outputting instructions for operating second autonomous vehicle(s) to cover the uncovered segment(s) according to a second movement path computed according to operational parameters of the second autonomous vehicle. The first autonomous vehicle(s) and the second autonomous vehicle(s) are selected to optimally accomplish the coverage task. The second autonomous vehicle having increased coverage precision and reduced coverage rate compared to the first autonomous vehicle.

Multi-dimension operation of autonomous vehicles

Presented herein are systems and methods for automatically evaluating detection accuracy of dynamic objects by equipment under test, comprising receiving a first record generated by an evaluated equipment under test and a second record generated by a validated reference equipment both deployed in a vehicle, the first record comprising a plurality of attributes of dynamic object(s) detected by the evaluated equipment and the second record comprising a plurality of attributes of dynamic object(s) detected by the reference equipment, correlating between dynamic object(s) detected by both the evaluated equipment and the reference equipment according to matching spatial and temporal attributes of the dynamic object(s) in the first record and in the second record, analyzing at least some of the attributes of the respective dynamic object in the first record compared to the second record, and outputting an indication of differences identified between the first record and the second record.

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Tel Aviv, Israel

Ben Alfi