Robot module overview
Robotics is one of the fastest growing engineering areas and one of the most challenging. Almost all robots have different operating environments, different behaviors or tasks, and different sensors and actuators. Therefore, people often use different development tools to develop robots on different hardware platforms. The success of an engineer's available control system for one robot is difficult to reuse for another because the application programming interfaces (APIs) for sensing, autonomous, and motor control are syntactically different.
When designing, prototyping, and deploying robotics applications, there are three biggest challenges: integrating sensors and actuators, autonomy, and deploying deterministic control algorithms to embedded hardware. To meet these challenges, LabVIEW RoboTIcs offers a complete new set of robot-specific sensor and actuator drivers, as well as a new code base for complex navigation operations. Moreover, with LabVIEW, developers can design control algorithms, connect real I/O, and deploy to deterministic hardware targets with a single software development environment.
If the designer can get the actual sensor input and control the actual actuators, such as the ability of the motor, they can speed up the prototype development of the robot. LabVIEW RoboTIcs includes a new VI version to configure, control, and acquire data from some of the most commonly used sensors in autonomous vehicles, and the data it receives can be used. Whether it's a low-cost infrared sensor or a high-resolution light orientation and ranging (LIDAR) sensor, LabVIEW RoboTIcs allows users to quickly retrieve sensor data, allowing them to focus on advanced intelligence and control implementations. In addition, whether it is a brushless motor, a brushed DC motor, or a stepper motor, LabVIEW RoboTIcs offers a variety of ways to connect and control the motor.
Figure 1. LabVIEW Robotics includes a complete set of new drivers for the most commonly used sensors in autonomous systems.
The blind driving vehicle designed by Virginia Tech is a typical example of a robot that relies on sensor feedback and actuator control. This semi-automatic vehicle has a tactile-based human-machine interface that allows the blind driver to make driving decisions. The sensor collects important information based on the state of the vehicle, such as collecting speed information through a Hall effect sensor and collecting the steering angle through a string potentiometer. Light Orientation and Ranging (LIDAR) sensors scan the driving environment and then identify obstacles or traffic signs. Students can quickly acquire the data from these sensors and then use LabVIEW for high-speed field-programmable gate arrays (FPGAs) on the NI CompactRIO embedded platform for direct processing.
After processing these sensor data, students used LabVIEW and CompactRIO to control a large number of transistors and relays to drive the motor in the vest worn by the driver at different intensities. The driver can use the tactile vest to adjust the speed so that he can drive freely until the upper limit is reached. When the speed reaches the upper limit, the vest will prompt the driver how much braking force is needed to return the vehicle to a safe speed. Students use LabVIEW on FPGAs to implement this motor control, dramatically reducing the time between detection and obstacles to full-speed vibration of the motor, which is critical for the driver in an emergency.
Figure 2. Virginia Tech's blind driving vehicles use LabVIEW and CompactRIO to communicate with sensors, actuators, algorithm development, and deployment. Adi Hagen, 16 years old, drove a blind car and took a ride with Virginia Tech's designer Greg Ganaman (passenger seat).
In just two semesters, nine college students completed the design and the blind driver could safely perform basic driving tasks.
"LabVIEW has an intuitive graphical user interface, and the sensor drivers for LIDAR are also readily available. This is a team of mechanical engineering students who can quickly and efficiently build custom embedded software." - Gray Ghanaman, a student at Virginia Tech, team captain.
2. Autonomous code baseAchieving robot autonomy is a challenging and important task. For tasks like navigation, algorithms are becoming more complex, which often requires software developers to have background knowledge in computer science. In addition, if you can't achieve code reuse, every time you start a new project, engineers always develop algorithms from the bottom.
An ADSL filter separates the analogue voice-frequency signals from the ADSL (broadband) data signals(Broadband being defined as data transfer greater than 128KBPS.)
If ADSL filter/splitters are not used, the ADSL data signals are heard as "noise" on any equipment connected to the "normal" telephone-line. Apart from being annoying during a telephone conversation, there is the possibility that the additional ADSL signals may cause problems with alarm-units (etc) that may be connected across the line.
This ADSL-interference is illustrated in the diagram below that shows how the unfiltered ADSL data signals appear across the "standard" telephone equipment.
We can supply you ADSL Splitter,ADSL Filter, adsl modem splitter,telephone splitter,adsl2+ splitter,adsl filter splitter, ,adsl dsl filter splitter,adsl in line splitter,adsl filter and splitter, ,rj11 splitter, broadband splitter, mdf splitter, adsl modem filter with a good quality and best price.
ADSL Splitter, 2Wire DSL Filter, ADSL Modem Splitter, Telephone Splitter, ADSL Splitter Filter,VDSL Splitter
NINGBO YULIANG TELECOM MUNICATIONS EQUIPMENT CO.,LTD. , https://www.yltelecom.com