ICRS: Robot Localization

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.

Incremental Trilateration

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:

  1. Movement
  2. Sync Transmit
  3. Signal Transmit
  4. Calculation

Initial Conditions

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.

Steps 1-3

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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.

Movement

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.

Sync Transmit

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.

Signal Transmit

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.

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Robot 1 calculates the distance.
R2
Base station calculates the distance.
R3
Robot 2 calculates the distance.

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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.

Limitations

There are a few limitations to the incremental trilateration method that I’d like to explain and propose solutions to.

Initial Conditions

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

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!

 

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ICRS: Robot Swarm Design

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.

Network Topology

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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.

 

ICRS: Details and Goals

Description

Infrastructure and construction robots are a group of modular robotic agents that can work cooperatively to automate various tasks such as construction, inspection, and repair. Each robot is capable of multiple functions, as capacities can be modified through the installation of different attachments on the unit. Each base unit is designed to be low cost to minimize the time and monetary investment and protect against significant losses due to the failure of a single robot. A group of robots can be teleoperated to increase the efficiency of skilled operators or can be given basic directives to automate simple or repetitive tasks.

Problem A: Infrastructure

  • U.S. infrastructure is currently rated as subpar and is continuously degrading (Graded D+ by ASCE)
  • Part of the issue lies in the extreme costs of repairs necessary across the nation (estimated $4.59 billion by 2025 to fix)
  • This massive increase in spending would cause a drag on the economy ($3.9 trillion GDP loss by 2025)
  • Too many problems, too expensive, takes too long

Problem B: Disaster Relief Management

  • Disasters are difficult to recover from and each disaster has unique requirements and problems that need to be addressed
  • Disasters are inherently unpredictable and so disaster preparation would require addressing all possible problems before it occurs, a prospect that can be prohibitively expensive
  • It’s also expensive to be reactionary and ship required relief materials for every disaster only after the disaster has occurred
  • Loss of life is worse immediately following the disaster before the relief supplies can make it to their destination
  • Massive, repeated shipments can be problematic due to bureaucratic and logistical delays

Problem C: Construction and Landscaping

  • Building homes and structures is expensive and time consuming
  • Machines for construction are expensive and have high specialization sometimes requiring many different machines for a single project
  • Cheaper, mass produced materials result in low levels of customization and customer satisfaction also requiring expensive shipment to the destination
  • Heavy earth moving machinery is often needed for landscaping projects which can be expensive or difficult to acquire
  • Using heavy machinery can be difficult and requires additional skills; alternatively hiring professionals only makes the work more expensive

Problem D: Non-Terrestrial Construction

  • Sending up satellites is limiting in both space and weight
  • After the heavy costs associated with initial construction, there are currently no options for repairs in the case of damage
  • Space junk is a problem without a good method of deorbiting debris
  • Expensive and restrictive to need to send all materials to construct a habitat on another planet
  • Materials to build habitats exist on planets but no machines currently capable of utilizing them or constructing habitats

Solution

  • Teleoperated robots!
  • Remote operated requiring skilled workers or semi-autonomous task completion
  • Perform checking and eventually construction that’s more cost effective
  • Makes workers more efficient and amplifies their capabilities
  • Increase in safety with cheap, expendable robots performing dangerous work
  • Flexible function using modular attachments means each robot is capable of many different actions
  • Small size means robots can work concurrently in the same location, reducing multiple phases of a project into a single, incremental step
  • Swarm methodology for a group of robots means tasks can be accomplished much quicker
  • A combination of a large swarm and the modularity means specific tasks can be dynamically allocated due to shifting requirements

Goals

Tier 1

  1. Robot with modular attachments that enable it to serve various functions
  2. Cheap base model that can be produced in quantity and has basic motion, communication, and sensor capabilities
  3. Assignable roles so that broken robots can easily be replaced by spare units or units can be re-assigned based on need
  4. Teleoperation with visual data so that users can get direct feedback about the status of the project’s target
  5. Limited physical intervention required by users
  6. Ability to direct movement and basics tasks to be performed by the robots

Tier 2

  1. Modular attachments can be swapped autonomously
  2. Dynamic role allocation so that the robots automatically determine the best distribution to get a task done efficiently
  3. Coordinated motion so the robots can be given simple direction to accomplish group behavior

Tier 3

  1. Machine learning can automatically flag problematic inspection data
  2. Augmented reality data so that the users can see the project target and the current progress side by side
  3. Advanced autonomy with the robots having the capabilities to perform a great deal of tasks with little user control required

Specifications

Sensors

  • Camera for teleoperation and visual data collection
  • GPS
  • IMU
  • Attachment identification

Communication

  • Central communication node to route mesh network packets
  • Master node compiles debug information and swarm state
  • Master establishes swarm requirements and passes them on to individual agents
  • Handled over WiFi with the master node providing the central access point

User Interface

  • Communication through a web interface hosted on the central node
  • Accessible by connecting to the swarm network
  • High level control abilities to direct general motion of the group and assign tasks
  • Lower level control also available for finer motion control
  • Individual robots are selectable so that debug information, robot state, and individual command interfaces are available

Job Allocation

  • Jobs are dynamically allocated and passed on to the individual robots via communication with the master node
  • Robots automatically connect to the attachments required to perform their assigned tasks

Potential Attachments

  • Gripper: Movement of structural material
  • Dumper: Transportation and removal of debris, earth, sand, etc.
  • Arm: Fine manipulation of objects
  • Screwdriver
  • Drill

Milestones

1. First Functional Prototype

  • Single robotic unit
  • Direct control basic interface (command line, GPS coordinates, etc.)
  • Basic motion and movement commands with low accuracy

2. Attachment Prototypes

  • Several basic, modular attachments for the prototype robot so it can perform various functions (e.g. gripper, arm, digger, screwdriver, drill, material transportation)
  • Automatic identification and control of the different attachments

3. Prototype Swarm

  • Multiple units controllable via a single interface
  • Different attachments to show multi-use cooperation
  • Direct control basic interface for individual units as well as group movements
  • Central communication hub to route communication between robots and provide single access point that distributes commands to individual swarm members

4. Survey and Analysis Demo

  • Demonstration of basic survey capabilities using direct control interface and teleoperated swarm
  • Robots coordinate with each other to map out a structure and provide detailed pictures
  • Optional additional sensors for measuring other useful data (e.g. radiation, temperature, vibration)

5. Repair Demo

  • Demonstration of basic repair capabilities using direct control interface and teleoperated swarm
  • Robots are capable of moving repair materials into place and performing the repairs without physical operator intervention
  • Robots can cooperate as a cohesive group to transfer repair materials and/or remove broken material

6. Construction Demo

  • Demonstration of basic construction capabilities using direct control interface and teleoperated swarm
  • Robots are capable of assembling an entirely new structure without physical operator intervention
  • Robots are capable of working together to prepare construction area and build a structure

Infrastructure and Construction Robot Swarm

New project alert! I was throwing around some multidisciplinary project ideas with two Mechanical Engineering friends and we talked about having some sort of robotic swarm for construction, disaster relief, infrastructure inspection, etc. Right now it’s not really a fully formed idea but I did submit it to the Hackaday Prize contest anyway since they’re currently running an “idea” seed funding phase. Here’s the project link. I think this idea has a lot of potential and I’m hoping this can grow into something that’s useful and beneficial to society. More details to follow!

OpenADR: Mop Module v0.1

For the sake of design simplicity and ease of assembly, the mop module is broken up in to two main parts based on the module base design.  The front of the module (the 150mm^2 square) is devoted almost entirely to the water storage tank and the rear is where all of the electronics and mechanics are.

IMG_0636

The picture above is a failed print of the front part of the mop module.  Rather than just tossing this piece, I ended up using it to test out the waterproofing capability of the XTC-3D print sealant.  It ended up working perfectly.

Despite the failed nature of the above print, it still demonstrates the main sections of the front of the mop module.  The main water tank is bounded by two walls, the left in the picture being the back wall of the water tank and the right wall being the front.  The small gap between two walls on the right side of the picture is the location of some holes in the base of the module that will allow for the water to be evenly dripped onto the floor.

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This bottom view of the part gives  a better view of the holes

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Two holes in the back of the water tank provide an input to the pumps.  Because combining electronics and water is a big no no, I added some holes in the bottom of the module so that any leaks around these holes would drip onto the floor rather than flooding the electronics section.

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This is the back of the mop module where all of the magic happens.  The holes in the bottom provide mounting points for the two motors that will drive the pumps.

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The two pillars in the very back provide a point to mount the base of the pump.

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The two, dual-shaft motors have one output shaft extending out of bottom that will be connected to the scrubber and one extending upwards that will be driving the pump.

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A picture of the downwards facing shafts.

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The above picture shows the back of the module with all of the hardware mounted.  Unfortunately, I didn’t give enough space for bolt heads that hold the motor in place.  The pumps can’t pushed down as far as I intended and so they don’t line up with the holes I left in the mounting pillars.  Luckily the mounts are sturdy enough to mostly hold the pumps in place and so I don’t need to mount them for testing purposes.

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These are the two halves the the scrubber that will hold the microfiber cloth that will be used to scrub the floor and soak up excess water.  The two halves are made to be pressed together with the cloth sandwiched in between them.

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This picture shows the cloth and scrubber assembled.  I underestimated the thickness of the cloth, so two won’t currently fit side by side.  I’ll need to either make the cloth smaller or move the scrubbers farther apart.

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Above is an overall picture with all of the pieces put together.

 

OpenADR: New Controller Options

One of the reasons for my hiatus from OpenADR is due to some uncertainty when it comes to the main processor for the robot.  My current implementation uses a Raspberry Pi for general control and data logging with an Arduino Mega handling the real-time code.  However, these two boards result in a combined price of $80 ($35 for the Raspberry Pi and $45 for the Arduino).  Requiring these parts would make the total cost of the navigation chassis much higher than I’d like.  These parts might also make it more difficult to manufacture OpenADR if I ever decide to turn it into a product.  While I could always create a custom board based on the Arduino Mega, the Raspberry Pi isn’t exactly manufacturing-friendly.

These reasons are why I’m exploring alternative options for the main controller for OpenADR and through my research I’ve discovered two options that I’m pretty excited about.  Both the C.H.I.P. Pro and ESP32 are powerful processors that could potentially be used for OpenADR, with the former being similar to the Raspberry Pi and the latter being closer to an Arduino.  Below is a comparison of specs and a description of how they could be used.

C.H.I.P. Pro

The C.H.I.P. Pro is an embedded Linux module produced by Next Thing Co. and is advertised as a solution for products requiring embedded Linux.  It has onboard WiFi and Bluetooth, and has an ARM Cortex-A8 processor with 256MB or 512MB of RAM running at 1GHz.  An added benefit is the Gadget ecosystem that Next Thing Co. announced.  They haven’t released too many details, but the impression I got is that it’s a Linux package that allows for easy and secure updating of products running on the C.H.I.P. Pro system.  My expertise starts to fuzz when it comes to product software management, and I know very little about security, so having an ecosystem that would make this easier would help me a lot.

One possible downside is that embedded Linux isn’t always good enough for real time applications.  While the board might have enough GPIO to connect to the robot’s peripherals, they might not be able to update outputs and read data fast enough for what I need the robot to do.  For example, any timing delays in the reading of the ultrasonic sensors could lead to incorrect distance data that would inhibit the robot’s ability to understand and map its environment.  This is something I can experiment with when I receive my development kit.

ESP32

The ESP32 is the other side of the embedded systems coin.  It doesn’t run Linux, but instead uses a Tensilica LX9 microprocessor with 520KB of RAM running at 240MHz.  It also has WiFi and Bluetooth built-in.  The plus side of a bare metal system is that there’s less concern about delays and real time control with the robot’s peripherals.  The downside is that this makes it much harder to write software for the robot.  A lower level language would need to be used and without Linux or the features of a more powerful processor, certain features, such as real time data logging, may be harder to manage and implement.

While different processor architectures aren’t always directly comparable, the ESP32 does run a 15x the speed of the Arduino Mega I’ve been using so far.  So while it might not be able to compete with a Raspberry Pi or C.H.I.P. Pro in terms of data processing, it’s way more powerful the Arduino and it will probably still be possible to implement the more complex features.  I currently have SparkFun’s ESP32 Thing development board on my desk and look forward to testing it out with OpenADR!

 

OpenADR: Pump Test

While I’m putting the design for the OpenADR mop module together, I decided to do a quick test of the 3D printed pump I’ll be using to move the water/cleaning solution from the internal reservoir to the floor.  The pump I am planning to use is a 3D printed peristaltic pump from Thingiverse.

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For my test setup, I used the another of the cheap, yellow motors that I powered the wheels on the main chassis and the brushes on the vacuum module to drive the pump.  I threaded some surgical tubing from a full glass of water, through the pump, and into an empty glass.  I then ran the motor off of 5V.

Overall the pump ran great, albeit a little slower than I anticipated.  The next step is integrating it into the mop!