Moscow

RoboCV

System for automation of floor movements of pallet cargo

automation

driverless

driverless_cars

robotics

RoboCV X-MOTION NG

Yandex

Yandex_Self_Driving_Group

This autonomous vehicle can drive on the sideways at average walking speed. Yandex.Rover can recognize objects around him, plan the route, avoid objects, people and animals walking in its path, and drive in the dark due to a lidar. During the creation of the robot, unmanned control experiences were used. In the future the robot can be implemented in Yandex.Food or Yandex.Lavka, for goods delivery for Beru online store or in warehouse logistics.

computer_vision

artificial_intelligence

Yandex.Rover

Cognitive_Pilot

Cognitive_Technologies

The autonomous control system based on artificial intelligence is designed for installation on agricultural machinery – farm tractors, combine harvesters. The automated system, in addition to the AI system, includes a camera and a calculator with additional interface units with combine harvester’s onboard systems.

The Cognitive Agro Pilot is based on the technology of deep learning for neural networks. The Cognitive Technologies’ team of developers in real-life conditions worked on collecting datasets (the images that contain the main elements of a field environment used for neural network training) and taught neural networks to perceive objects and situations a smart combine harvester will confront during the process of its work and which should be recognized by the artificial intelligence.

AI systems can see different types of boundaries of a field (edge, cutting), eliminating the need in installing extra sensors on the equipment. The solution also allows for adding new types of boundaries (rowing – the shape of planting certain crops, for instance, corn, as well as that of tilled and untilled parts of a field).

Autonomous control system for agricultural machinery

farming

Cognitive Agro Pilot

C-pilot comes with a suite of sensors: TOF cameras, radar, lidars, other sensors for detecting objects, and a GLONASS/GPS based positioning sensor.

The data is processed by an onboard minicomputer. The sensors enable 360-degree &nbsp;environment perception. SLAM (simultaneous localization and mapping) technologies convert the geographic coordinates collected by positioning sensors into the car’s local coordinate system. Once the objects detection data and cartographic information is collected, road scene objects and the vehicle become linked to each other.

Static and dynamic objects’ position and movement tracking, near collision calculation, collision warning occurs at the next module.

The calculation of the optimal movement trajectory is made possible by an intelligent system that uses RRT (Rapidly Exploring random three) and an A search algorithm modification. The programs of the next module’s controller can turn the steering wheel at the required angle with the required speed, bring the car to a halt in time and increase the speed of the vehicle.

The system was used to develop the LDWS solution to warn about a car’s veering from a traffic lane, TSR – to identify road signs, FCW – to warn about a collision when a car rides with traffic, PPS – for the pedestrian protection system.

Autonomous control system for ground transportation

smart_vehicles

GLONASS

Cognitive Pilot

The system automates the horizontal pallets movement within the FMCG distribution centres and warehouses.

The solution consists of a regular electrical forklift equipped with an autopilot system by RoboCV and a cloud-based service which optimizes task distribution among robots in real time. It allows for implementation of a client-server module to enable centralized management of robots’ tasks and monitoring of their state. It can also be integrated with external IT systems.

The solution automates up to70% of all warehouse processes.

A system designed to automate horizontal pallets movement 

Yandex.Taxi

Yandex self-driving cars are equipped with a hardware and software system that includes cameras, all-round lidar and radar. The lidar uses a light emitter to scan the environment, converting the information about ray reflections into a 3D map and calculating the precise distance to the obstacles in front of a car. The radar with a smaller angle of view tracks the distance to farther objects, which is crucial for cars running on higher speeds.

In addition, Yandex driverless car is equipped with sensors that track its location, speed and movement direction: GPS/GLONASS receivers, IMU and sensors that measure the rotation speed of certain wheels. The sensors use all the data they collect to enable a complex algorithm to produce a model of the environment that allows for the car to make its riding decisions. The system uses a variety of Yandex technologies, building from computer vision.

During year-round rides the self-driving car collects the data about the traffic environment to use it for further training of the technology. Before autonomous cars are allowed to ride on public roads, they undergo multiple tests in a closed-off testing site. A car using an autonomous vehicle technology has already ridden 780 km from Moscow to Kazan. A safety engineer was in the driver seat to take control of the car at any moment.

Tech Solutions

RoboCV X-MOTION NG

Yandex.Rover

Cognitive Agro Pilot

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Cognitive Pilot

RoboCV X-MOTION NG

Yandex self-driving car

Tech Solutions

RoboCV X-MOTION NG

Yandex.Rover

Cognitive Agro Pilot

add your project!fill out the form

Cognitive Pilot

RoboCV X-MOTION NG

Yandex self-driving car

add your project!
fill out the form