We’re quite used to multitasking computer systems today. Our desktops run email, a couple of browsers in different workspaces, a word processor, and a few other applications, apparently all at once. Looking behind the scenes using a system monitor or task manager program reveals a multitude of other programs running in support of our activities. Of course, any given CPU is running a maximum of one program at a time. Multitasking is simply the practice of switching between active processes fast enough to give the illusion of simultaneity.
The roots of multiasking go way back. In the early days, when computers cost tons of money, the thought of an idle system was anathema. Teletype IO was slow compared to the processor, and leaving the processor waiting idle for a card reader to slurp in the next card was outrageous. The gurus of the time worked to fill that idle time with productive work. That eventually led to systems that would run multiple programs at one time, and eventually to more finely grained multitasking within a program.
Modern multitasking depends on support from the underlying API of an operating system. Each OS uses its own techniques, making it difficult to write portable code. The C++ 2011 standard increased the portability of the language by adding concurrency routines to the Standard Template Library (STL). These routines use the API of the OS. For instance, the Linux version uses the POSIX threading library, pthread. The result is a minimal, but useful, capability for building upon in later standards. The C++ 2017 standard development activities include work on parallelism and concurrency.
In this article, I’ll work through some of the facilities for and pitfalls in writing threaded code in C++.
Daughter boards for microcontroller systems, whether they are shields, hats, feathers, capes, or whatever, are a convenient way to add sensors and controllers. Well, most of the time they are until challenges arise trying to stack multiple boards. Then you find the board you want to be mid-stack doesn’t have stackable headers, the top LCD board blocks the RF from a lower board, and extra headers are needed to provide clearance for the cabling to the servos, motors, and inputs. Then you find some boards try to use the pins for different purposes. Software gets into the act when support libraries want to use the same timer or other resources for different purposes. It can become a mess.
The alternative is to unstack the stack and use external boards. I took this approach in 2013 for a robotics competition. The computer on the robots was an ITX system which precluded using daughter boards, and USB ports were my interface of choice. I used a servo controller and two motor controllers from Pololu. They are still available and I’m using them on a rebuild, this time using the Raspberry Pi as the brain. USB isn’t the only option, though. A quick search found boards at Adafruit, Robotshop, and Sparkfun that use I2C.
I know you’ve heard of both synchronous and asynchronous communications. But do you really know the differences between the two?
Serial communication was used long before computers existed. A predecessor is the telegraph system using Morse Code, one of the first digital modes of communication. Another predecessor is the teletype, which set standards that are still used today in your Arduino or Raspberry Pi.
All you need is two wires for serial communications, which makes it simple and relatively robust. One wire is ground and the other the signal. By interrupting the power with predefined patterns, information can be transferred over both short and long distances. The challenge is receiving the patterns correctly and quickly enough to be useful.
I was a bit surprised to find out the serial port on the Arduino Uno’s ATmega328P microcontroller is a Universal Synchronous Asynchronous Transmitter Receiver (USART). I’d assumed it was only a UART (same name, just leave out synchronous) probably because my first work with serial communications was with the venerable Intel 8251 “Programmable Communication Interface”, a UART, and I didn’t expect the microcontroller to be more advanced. Silly me. Later I worked with the Zilog 8530 Serial Controller Chip, a USART, the term I’ll use for both device types.
All these devices function in the same way. You send a byte by loading it into a register and it is shifted out one bit at a time on the transmit (TX) line as pulses. The receiver accepts the pulses on a receive (RX) input and shifts them into a register, which is then read by the system. The transmitter’s job is pretty easy it just shifts out the bits at a known clock rate. The receiver’s task is more complex because it needs to know when to sample the incoming signal. How it does this is the difference between asynchronous and synchronous communications.
Last fall, I grabbed a robot arm from Robot Geeks when they were on sale at Thanksgiving. The arm uses servos to rotate the base and move the joints and gripper. These work well enough but I found one aspect of the arm frustrating. When you apply power, the software commands the servos to move to home position. The movement is sufficiently violent it can cause the entire arm to jump.
This jump occurs because there is no position feedback to the Arduino controller leaving it unable to know the positions of the arm’s servos and move them slowly to home. I pondered how to add this feedback using sensors, imposing the limitation that they couldn’t be large or require replacing existing parts. I decided to try adding accelerometers on each arm section.
Accelerometers, being affected by gravity when on a planet, provide an absolute reference because they always report the direction of down. With an accelerometer I can calculate the angle of an arm section with respect to the direction of gravitational acceleration.
Before discussing the accelerometers, take a look at the picture of the arm. An accelerometer would be added to each section of the arm between the controlling servos.
Intel made an appearance at the recent summer X Games in Austin, TX with the Curie, a gadget for sensing the motion and position of skateboarders and BMXers. The Curie, attached to the bikes or helmets, measured the dynamics of the tricks performed by the participants.
An Intel 32 bit Quark SE system on a chip sent the telemetry data in real-time using Bluetooth. The module contains an accelerometer and gyroscope to capture all the twists, turns, and tumbles of the athletes. An analysis of the data was presented as part of the on-screen graphic displays of the events.
Between Tesla Motors’ automobiles and SpaceX’s rockets, Elon Musk’s engineers just have to be getting something right. In part, SpaceX’s success in landing their first stage rockets is due to analysis of telemetry data. You can see some of the data from their launch vehicles on the live videos and there is surely a lot more not shown.
An article in MIT Technology Review provides similar insights in how Tesla came from behind in autonomous vehicle operation by analyzing telemetry from their cars. Since 2014 their Model S received an increasing number of sensors that all report their data over the vehicle’s always-on cellular channel. Sterling Anderson of Tesla reported they get a million miles of data every 10 hours.
The same approach can help us to improve our systems but many believe creating a log of key data is costly in time and resources. If your system is perfect (HA HA!) that would be a valid assessment. All too often such data becomes priceless if analysis explains why your drone or robot wanted to go left into a building instead of right into the open field.
Wireless is easier today than ever, with many standards to choose from. But you don’t need anything elaborate if you simply want to cut the cord. A few years back, [Roman Black] experimented with the cheap RF modules you can find on auction sites and from surplus electronics vendors for only a few dollars, and wrote up his findings. They’re well worth a look.