Hardware

It is useful to briefly describe hardware used for vehicle map-less navigation and its integration with network framework. In this section all the sensors used are described, highlighting the main features that make them fit to this application.

Vehicle

Vehicle used for this work is ATR Orbiter-v1, a fully radio controlled mobile crawler robot. It is able to tackle any type of terrain and suitably used for a wide variety of applications.

Figure 5.15: Atr Orbiter-v1. A fully radio controlled mobile crawler robot. In this figure it is equipped with all sensors.

Thanks to ease to use, this robot suits perfectly these kind of applications. Figure below shows fully equipped vehicle and some features. It is useful for this work to specify that engines accept values in range[0, 65535] and stop value is equal to the middle value.

GPS Module

As GPS module the u-blox C94-M8P Application Board Package is used. It provides the means for efficient integration and evaluation of NEO-M8P, u-blox’s M8 high pre-cision GNSS module.

Figure 5.16: C94-M8P Application Board Package.

This module series introduces the concept of a “Rover” and a “Base Station”. By using a correction data stream from the Base Station, the Rover can output its relative position with stunning cm-level accuracy in good environments. The two boards are identical.

It is possible to select one of the boards to act as a “Base” and one as a “Rover”.

Base and rover could be used in Moving Baseline (MB) configuration that allows both boards to move while computing a centimeter-level accurate position between them.

The rover position solution provides the user with a vector describing the difference between the base and rover locations.

Figure 5.17: RTK configuration between Base and Rover modules. They communicate with an UHF antenna.

This vector has the structure shown in Figure 5.18. For this application rover module is mounted on vehicle, while base module works as target. In this way operator with target can move within the environment and vehicle with rover receives components of target distance vector in NED frame. The MB Base-Rover configuration perfectly suits the task agent seeks to accomplish.

Figure 5.18: NAV-RELPOSNED message provided by rover position solution.

Inertial Measurement Unit (IMU)

In order to provide network with informations about target in terms of distance and heading error, data from IMU are used in addition to GPS. IMU used here has 6-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer, as shown in Figure 5.19.

With IMU module it is possible to get the vehicle asset in NWU frame. What is actually

Figure 5.19: IMU module.

required is the position angle of vehicle, that is yaw angle. These data combined with GPS data provide component of state needed to reach the target position.

Laser Scanner

The laser scanner provides an array of distances in meters of the surfaces of object laying in covered area. This laser has a detectable angle of 190^◦ that suits with the angle used in training. Moreover, it sends data via UDP protocol using Ethernet, thanks to its built-in programmable software. Laser acts as client and wait for server request. Once data request is received from client, it sends measures to server using UDP protocol.

Figure 5.20: Keyence Laser Scanner.

Camera

The ZED camera provides many kind of data, including depth map. This map is an image that contains information relating to the distance of the surfaces of scene objects from a viewpoint. This camera stores a distance value for each pixel in the image. The distance is expressed in metric units (meters for example) and calculated from the back of the left eye of the camera to the scene object. In this case depth map shows luminance in proportion to the distance from the camera. Nearer surfaces are darker; further surfaces are lighter. Depth map captured by the ZED is 1280x720 image encoded on 32 bits with range from 0.7m up to 20m. To display the depth map, a monochrome (grayscale) 8-bit representation is necessary with values between [0, 255], where 0 represents the closest possible depth value and 255 the most distant possible depth value.

Figure 5.21: ZED camera for depth map acquisition.

High-Level board and Low-Level board

There are two different boards on the vehicle that manage all the sensors equipment.

The high-level board is an Nvidia Jetson TX2; this platform features a variety of

standard hardware interfaces that make it easy to integrate it into a wide range of products. It comes with Developer kit to maximize ad make easy the use of all in-terfaces, as Figure 5.22 shows. Nvidia TX2 offers solutions to meet the AI computing needs of intelligent machines across the board.

Figure 5.22: Nvidia Jetson TX2 Developer Kit. It works as high-level board managing all high-level processes.

Deep learning is the ideal field for this module, thanks to its high-performance GPU architecture that takes advantage of multiple cores. Indeed, trained actor network is loaded on this board to make inference during evaluation.

The piCORE board works as low-level board, since it directly linked with drivers that control the electric engines. Figure 5.23 shows this board. This board is based on STM32 microcontroller integrated circuit, with 1 MB flash memory and 256 kB ram.

It provides multiple kinds of standard connection as CAN-bus and allows to power on the high-level board using a GPIO connection.

All the elements described above are connected with two different boards. In par-ticular, GPS Rover module and ZED camera are connected via USB to the High-level board, while IMU module to the low-level board. As said before, laser is connected via Ethernet to a switch to which the high-level board is also connected. The two boards

Figure 5.23: Low-level board piCORE based on STM32 microcontroller IC.

are linked via USB. How data are extrapolated from sensors, how boards act to link all the hardware parts and how trained network infers is explained in the next section.