Robocar: Unmanned Ground Robotics
Every robot relies on actuators to act upon its world. Robocar has three of these:
Steering control is provided via a CAN-AMP. The steering CAN-AMP is one of three nodes on our CAN (Controller Area Network). The other two are an encoder wheel and the CAN-PC controller card. Servo behavior can be completely controlled; for example, we can tell it to turn a certain distance within a certain period of time and to decelerate gently before it gets there. Two years ago, we used one of these to turn a single camera rapidly from side to side without damage, because of the great number and flexibility of the parameters to the CAN servo.
Motor control is achieved through pulse width modulation (PWM) from a computer to two DC drive motors. These 24 volt motors are extremely powerful and have great torque. One afternoon, we took turns riding on the car, and the motors easily pulled the car and a heavy (185 pound) human passenger up a steep hill. We generate a PWM signal from two cascading counter/timers that receive the same clock signal. The first is set up to periodically generate a rising edge on its output and determines the frequency of the PWM signal. The period of the signal does not change. The output of the first counter/timer is connected to the gate on the second counter/timer. The second counter/timer determines the duty cycle of the PWM signal. A short count on this timer maps to a longer fraction of the PWM period that is high and, thus, to more power being sent to the motor.
Shadow-reducing head lamps are switched with a computer-controlled relay. These lights improve the vision sensors' ability to spot the course boundaries.
To perceive its environment, Robocar needs sensors. We have given it cameras for detecting the track boundary lines painted in the grass, a scanning sonar for obstacle avoidance and an encoder wheel for speed detection. Robocar has some additional sensors for side projects which are not used during the competition.
Vision is supplied from two standard video cameras fed through two Matrox Meteor frame grabbers. We have two different Matrox cards: the Meteor and the Meteor/RGB. Both can read from multiple cameras and grab high-resolution 24-bit color images. The only difference is that the Meteor/RGB can grab frames from a split-RGB source, whereas the regular Meteor cannot. Even though we could plug two cameras into a single Meteor board, we are using two boards to get 30 frames per second per camera. Matrox's Meteor boards are inexpensive, reliable and well supported.
A single Panasonic sonar sensor mounted on top of a Futaba RC servo acts as an obstacle detection device. It scans the area in front of the car, rotating back and forth to cover a wider area. Using a single sonar has the advantage of removing any possibility of cross-talk and of being able to look in any direction. Using multiple statically-mounted sonar sensors would not give us this much flexibility. The Futaba servo, like the drive motors of the vehicle, is controlled using PWM.
An encoder wheel returns data to a speed sensor indicating how far it has turned. Since we know the diameter of the wheel, we know how far it has turned since last we checked. Thus, this sensor can compute our average speed during that time. The sensor's interface to the encoder wheel is through a CAN-PC board on our main computer. Robocar uses this sensor to ensure that it stays under the 5 MPH speed limit.
In addition to being a competition vehicle, Robocar acts as a test bed for Kevin Gifford's Ph.D. thesis, which is to develop an efficient navigation algorithm for (possibly off-world) autonomous rovers. An additional set of sensors has been added for this option: a GPS sensor and a “map” sensor. Using these, Robocar always knows exactly where it is and where it wishes to go; it can also plan the cheapest way of getting there.
The Trimble Series 4000 uses differential GPS and can make extremely accurate measurements—+ or - 10 centimeters—compared to normal civilian GPS. It comes with a base station, a receiver and radio modems. GPS information is supplied over a serial line.
During Kevin's research, Robocar knows about its environment by using a map sensor in addition to the competition and GPS sensors. The map sensor is basically a topological map of the research field. With this knowledge, Robocar can calculate the most efficient path to a set of destination coordinates.
In addition to the above sensors, we have a joystick for manually driving Robocar to and from the course (or around the test field just for fun). Without this, we would have to push or carry the heavy beast around—something we prefer to avoid. The joystick is plugged into a generic sound card on our main machine.
Practical Task Scheduling Deployment
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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|The Firebird Project's Firebird Relational Database||Jul 29, 2016|
|Stunnel Security for Oracle||Jul 28, 2016|
|SUSE LLC's SUSE Manager||Jul 21, 2016|
|My +1 Sword of Productivity||Jul 20, 2016|
|Non-Linux FOSS: Caffeine!||Jul 19, 2016|
|Murat Yener and Onur Dundar's Expert Android Studio (Wrox)||Jul 18, 2016|
- The Firebird Project's Firebird Relational Database
- Stunnel Security for Oracle
- My +1 Sword of Productivity
- Non-Linux FOSS: Caffeine!
- SUSE LLC's SUSE Manager
- Managing Linux Using Puppet
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- Doing for User Space What We Did for Kernel Space
- Google's SwiftShader Released
- SuperTuxKart 0.9.2 Released
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide