06/02/2018 at 17:14 •
The design for the board to supply power to both the Pi Zero and robot modules is fairly simple. It's two components are a single-cell, LiPo charging circuit based on the MCP73831 and the Pololu adjustable step-up regulator. The charging circuit is fairly straightforward so I'll just explain the step-up circuit which takes the output voltage from the single cell battery, passes it to the four pin input side of the step-up regulator, and gets back a boosted voltage from the second four pin output side of the regulator.
I also added four beefy diodes between the regulator and the 5V supply for the system. My reasoning behind this was to provide the ability to use multiple power boards, and therefore multiple LiPo batteries, in the system. Each regulator is limited to roughly three amps of current with the multiple boards, Arduinos, and the Pi Zero taking up a fair chunk of that. I was worried that the regulator wouldn't be able to supply enough current to the motors and so I added the diodes so that multiple power boards could be safely put in parallel. I also decided to use an adjustable voltage regulator so that the output voltage of the regulator could be set higher than 5V so that the actual voltage seen after the diodes would be 5V after the diode forward voltage.
06/01/2018 at 23:56 •
The first board I'll go over is the Motor Board prototype. It's fairly simple; excluding the Arduino that handles communication and control it only consists of encoder connectors and a dual H-bridge. My planned use for this prototype will be to control the main drive motors of the robot base module. The schematic and PCB are shown below.
I'm using an L293D chip for the motor driver. As shown in Figure 10 of the datasheet, each half of the chip can function as a bidirectional motor controller. By driving PWM signals to the control inputs of the chip, the speed of the motors can also be controlled. Unfortunately, each half of the bridge can only handle up to 600mA, which is relatively low, but it'll sufficient for controlling the basic motors I plan on using on the prototype robot.
The encoders I'm using are KY-040 Rotary Encoders which will measure the number of rotations of the motors and provide feedback to the motor controller. The linked description explains how the encoders work better than I can, but essentially the Arduino will measure the speed and number of rotations that the motor is travelling at and apply more or less current to the motor to get it to the desired end position. I plan on using a basic PID loop for this which I will cover in a later post.
In addition to the encoder connectors on the left, I've also added a basic debounce circuit in the top left of the schematic. Because these encoders use mechanical switches, they're subject to mechanical bouncing of the switch contacts and so I plan on using a capacitor to act as a low pass filter and absorb these bounces and clean up the encoder signal.
06/01/2018 at 01:57 •
Before doing the circuit design and PCB layout I wanted to briefly outline the architecture of the core robot modules and the communication between them. The core modules are the ones that will be present on every robot in the swarm and provides the critical functionality that's required for the robot to operate. To make programming easier and reduce load on the main processor I've decided that each module will have a processor that will intelligently communicate with the main board and abstract away as many unnecessary details as possible.
The main board will be a single board computer that will do all of the "thinking" for the whole robot. It will handle communication to the swarm master and pass information between each of the modules within a robot. Due to the large community and low cost I've decided to use the Pi Zero W as the main processor. I'll be using the GPIO header as the main connector and each module will be connected together through this common header. Each of the submodules will be I2C slaves and will be directed by the Raspberry Pi on the I2C bus. The Pi Zero W also has the benefit of built-in WiFi which further reduces the cost and complexity of the robot.
The power module will be fairly dumb with it's only task being to supply power to the whole module stack. For the first iteration I only plan on measuring battery voltage/percentage via the onboard processor but I may expand these capabilities in the future to include things like power usage, current, battery health, etc.
The motor controller board will be responsible for the actual movement of the robot. My initial design will have the ability to control two motors as well as encoders to close the loop and verify that the robot has moved where expected. As I mentioned in my previous post, encoders are subject to drift so these encoders will verify that the robot movement is in the right ball park and will only be used for a single move. This means that the encoder position will be reset after each move is completed.
I plan on having this module be intelligent enough to handle all coordinated motion without intervention from the main processor board. The Raspberry Pi should be able to send the board an XY position, say "Go here," and have the motor board handle the rest. It will also be able to query this submodule for certain status such as current position, status of the move, etc.
Due to the relative complexity of the localization algorithm, as outlined in my previous post, the localization board will be the most complicated. It required IR LEDs for transmitting the sync signal, IR receivers for receiving another robot's sync signal, and ultrasonic transducers for sending and receiving localization pings.
This submodule won't be as independent as the motor controller board due to the inter-robot communication requirements of the localization algorithm, but the interface should be simple. The board will only take one command, to send out a ping, so it knows to trigger the sync signal and localization signal on the IR LED and ultrasonic transmitter. Otherwise the board will constantly wait to receive an IR sync signal and will calculate distance from the transmitting robot based on how long it is until the localization signal arrives. The main processor board will then be able to query the localization board for the distance to the transmitting robot.
With the basics of the submodules laid out the next step is designing the circuits and PCBs!
04/14/2018 at 21:26 •
One of the biggest problems with precise motion in robotics is localization. Encoders are only so accurate and tend to drift over time. Absolute positioning systems like GPS have error margins measured in meters. The solutions to this is often complicated (e.g. SLAM) or expensive (e.g. RTK GPS). As a cheap and simple alternative to this, I'm going to attempt to use incremental trilateration to enable the robots to determine their own position relative to their siblings.
Why It's Important
An accurate map of where the robots are is critical for proper planning of their movements. Without it they could get in each other's way or fall off of a cliff! It's also necessary for them to perform tasks that require precise motion such as moving debris or laying brick. While the robots will also be teleoperated and have an onboard camera, it's hard for the human eye to properly determine depth and estimate measurements from a 2D image.
The most common method for robot positioning is using encoders to measure the distance the wheels have traveled by counting rotations. There are some caveats to this method, however. If a wheel slips the encoder's accuracy will suffer and it will think it's traveled farther than it has. Over time the encoder's measurement will drift farther and farther from reality. This problem will only be exacerbated by the rough and slippery terrain that these robots will eventually be operating on. However, with incremental trilateration the robot will recalculate an absolute position every time it moves. This absolute measurement won't be susceptible to location drift.
The method I'm proposing for robot localization is based on trilateration, which is the same method GPS satellites use to determine position. This method, however, will be able to attain greater accuracy with cheaper and less complex hardware. Rather than the speed-of-light radio signals that GPS satellites use for trilateration, I plan on using much slower sound waves to do the same calculations. This makes it possible to do signal detection and calculation with a relatively slow microcontroller instead of high speed DSPs and custom silicon. I also plan on having an IR blast in the beginning to synchronize all of the robots before each sound pulse is sent. This sync signal will provide a trigger to the robots acting as base points so that they don't always need to be waiting for sound pulses.
Incremental trilateration consists of four steps:
- Sync Transmit
- Signal Transmit
For this method to work the robots need to start off at set points. This means that the robot master and two of the robots need to start at known locations. Trilateration requires the knowledge of three base points for calculating the fourth point and so absolute positions of the three base points need to be known.
The animation above covers the movement, sync transmit, and signal transmit steps of the process. The big square and two smaller circles on the sides represent the base station and two of the robots at known points.
In this step the robot that's currently active moves, either towards a target or to explore. In the example above the robot in front moves along the Y axis from its position directly in front of the base station to an indeterminate position in front and to the right of where it was before.
The expanding, red circle sent out from the robot represents a sync signal that will be sent from the robot at an unknown position to the three other robots at known locations. In my planned implementation this will be a 40Khz IR signal. Once the three receiving robots receive this signal they know to start counting and waiting for the signal transmit. It should be noted that I'm ignoring the travel time of the IR pulse because the speed of light is so fast that it can be considered insignificant over the small distances that the robots will be moving.
Once the sync IR signal has been received, the robots will start counting until they see the sound pulse. I plan on using 40Khz ultrasonic transducers to handle this and generate an inaudible sound wave. Once the robots see the sound pulse they will stop counting and save the difference in time between the sync and signal transmissions. Using the speed of sound they can then calculate the distance to the robot that sent the transmissions.
Step 4: Calculation
Once each of the base robots calculates the distance to the moving robot they can then effectively draw a circle around themselves with a radius of the distance they've calculated. The base robots can definitively know that the moving robot is somewhere on the edge of the circle.
Robot 1 calculates the distance.
Base station calculates the distance. Robot 2 calculates the distance.
All three overlapping circles show the location of the moving robot.
Using the circles drawn by each of the base robots and the known direction that the robot traveled in, it can then be determined that the moving robot is sitting at the point where each of the three circles overlap, as displayed in the image above.
There are a few limitations to the incremental trilateration method that I'd like to explain and propose solutions to.
The first and most inconvenient is that the robots will require known initial conditions. This means that at least three nodes in the localization network need to be at predefined positions, otherwise it's impossible to calculate the position of the moving robot. This makes setup a little harder and introduces accuracy problems if the initial conditions aren't perfect.
Some of this can be mitigated by the fact that each of the nodes can determine the location to the master node using the sync and signal method. If each robot is placed along a single straight line (which can be considered a line perpendicular to the Y axis at a known value of Y), it can send sync and signal transmissions to the master to determine its X offset.
Another possibility would be adding three IR and ultrasonic receivers to the master at predefined locations so that the master itself can act as the three reference points for the moving robot. This introduces some complexity but may ultimately be worth it.
Turn based movement
Another limitation is that in the above scenario with four nodes, only one robot can move at a time as it needs the three reference points to be stationary. This is less limiting in larger networks as the necessity of three reference points means (N - 3) robots can be moving with N being the number of robots in the network. For large values of N the limitation is less. However, because the IR and ultrasound are using the air as a common bus the actual transmissions will need to be kept to one at a time to prevent collisions.
Accuracy is the biggest concern with this system. Until I test this I won't be sure the exact accuracy of the system but there is a lot of variability that can cause problems. The reason I'm using ultrasound for the signal transmission instead of light is because the speed of light is much too fast and the clock speed of microprocessors is much too low to properly detect the signal over small distances. However the ultrasound still has the same limitations, albeit to a lesser degree.
The Arduino micros timer measurement has a resolution of four microseconds. Since the speed of sound is 343 m/s, the ultrasonic pulse will be able to travel 0.001372 meters (343 m/s * 4e-6 seconds) or 1.372 millimeters for one increment of the micros() counter. This is only the maximum theoretical resolution, however, since it doesn't take into account things such as digital read or sensor latency. Ultimately the actual resolution is something I'll have to determine experimentally. I'm hoping for a 1cm accuracy for my initial implementation and will have to search for optimizations should that not be immediately achievable.
Another thing to take into account is that the speed of sound changes with temperature. However this can be fixed by using a temperature sensor to more accurately calculate the speed of sound as shown here.
Next up is planning out and designing the actual hardware!
03/28/2018 at 02:07 •
Before diving into the nitty-gritty of the robot design I wanted to take a moment and lay out a brief description of the network and base robot architecture. Below is a block diagram and description of both the network topology and the configuration of the robots.
The pure swarm approach for a fully modular network would involve a mesh network with with no centralized control source. Instead of doing this I'm opting for a robot swarm with central control node, much like how a bee hive has a queen. And, since robot dancing has not reached bee levels of communication, I'm going to leverage existing technology and use WiFi. This will make it easier to use existing single board computers (e.g. the Raspberry Pi) for robot control instead of making a homebrew control board. Having all of the robots on a single WiFi network will also make it easy to remote login to the individual robots for telepresence control.
I also plan on having a single, central node to handle the complex control and collate all of the data provided by the individual swarm robots. This central point will also have the WiFi access point. Having a single master may seem counter-intuitive for a robot swarm but it will make development and control much easier. A powerful central computer can do complicated operations such as image processing and parsing the most efficient paths for each robot to take. The central node can also handle delegation by directing each robot to assume a role in heterogeneous swarms when different tasks need to be handled by the robots. Another benefit of making a single master node is debugging. Having the central node keep track of all of the data will make it easier to access the swarm's status and provide a clear picture of how the system is operating.
Base Robot Configuration
I've tried my best to limit the robot's base design (i.e., the components that will be common between all robots no matter their role or attached modules) to the very minimum. The block diagram above is what I came up with.
Each robot will have three core modules built in to the base design: one for power control and distribution, one for motor control, and one for localization. The only exception will be the central node which won't have the motor control module. The modules will all report to a central CPU. Each of these modules will be intelligent with its own microcontroller for real time control and calculations. Having a microcontroller for each module abstracts away the processing required for each module and lets the CPU retrieve processed data and send commands without having to manage every single component.
I decided on using a full SoC rather than just a microcontroller to help speed up development and to potentially allow for some image processing and other calculations to be done locally. The Pi Zero W seems like the best bet for this at the moment due to its native camera support and on-board WiFi (and the large support community is a huge plus!). Using a full Linux system will also make software design easier without requiring constant retrieval and firmware flashing every time the software changes. It will be simple enough to remote in to each robot for control and status update over SSH.
I plan on defining the interface and functionality of each module in the next few posts. I also want to outline and explain my method for robot localization in detail. That will be my next post.