Project

# Title Team Members TA Documents Sponsor
17 Safety Suite for Electric Longboards
Best Overall Project (SP21)
Alexander Krysl
Patrick Stach
Pouya Akbarzadeh
Evan Widloski design_document1.pdf
design_document12.pdf
final_paper3.pdf
presentation1.txt
presentation2.pdf
proposal1.pdf
Alexander Krysl [krysl2], Patrick Stach [stach2], Pouya Akbarzadeh [pa2]

# Problem

Electric Skateboards and Longboards have skyrocketed in popularity for personal transportation in urban cities & towns. Their nimble and speedy characteristics allow users to easily navigate long distances of congested vehicle or foot traffic, yet are small and lightweight enough to be carried around indoors.

Despite their value as a useful transportation and recreational tool, nearly all consumer electric boards lack seemingly paramount safety features. Only a small subset of electric boards include what is termed a 'dead man switch', a button on the remote control that must be pressed at all times in order to engage the motors. And some electric boards force braking power unless remote control-commanded motion is occurring. These two features are the only safety features that are commonly offered on popular consumer electric boards, and we find these very minimal at ensuring the safety of the user and of passing pedestrians.

Furthermore, the motor control design of consumer electric skateboards is arguably simple and can cause dangerous conditions. Nearly all electric skateboards power the rear wheels with individual motors, but the throttle control given to the user via the remote applies power to both wheels identically. While suitable for most straight-line, even-terrain travel, this design can cause wheel-slip under uncertain conditions - specifically, while performing harder turns, or traveling across uneven terrain. This wheel-slip can easily cause users to lose control of the electric board, and can cause injury to both the user and to passerbys.

Additionally, the dead man switch does not mitigate every situation where a user falls off of the board. If the user happens to hold the dead man switch while falling off (which is entirely possible in the shock of the moment), this can allow the skateboard to accelerate towards pedestrians - undoubtedly a serious safety hazard.

# Solution Overview

What we propose as a solution to these safety concerns is a suite of safety features.

- Firstly, we wish to develop an electronic differential for electric skateboards that can sense whenever wheel-slip occurs and proportionally reduce motor power to mitigate it. This, we hope, will greatly improve the stability of our electric board when travelling across uneven terrain or under severe turning conditions.

- Secondly, in tandem with the electronic differential, we wish to develop a sensing device that can detect when the user has fallen off of the board, and to heavily reduce motor power when this occurs. This would prevent the board from accelerating towards pedestrians once the user has fallen off.

- Thirdly, we wish to develop another sensing device that can audibly warn the user of potential obstacles that are directly in the electric board's way. This would be supremely useful in nighttime environments, where users have difficulty identifying obstacles (such as curbs, walls, or large rocks) that are fast approaching while in motion.

We intend to initially build a rear-wheel drive Electric Longboard out of existing, commercially available components, and then develop our safety suite upon that foundation.

We believe these aforementioned features are necessary to ensure the safety of electric skateboards & longboards, not only for users but for pedestrians as well.

# Solution Components

## Safety Microcontroller
We will have a microcontroller as a main control unit that reads and processes the sensor data across the electric longboard and remote, and adjust the motor characteristics in accordance with the safety features we have described earlier. (More on the various sensors/data in the following components.) To clarify, this unit is separate from the electronic speed controllers (ESCs) that individually power the rear electric motors. Instead, this unit will manage the states of the ESCs, primarily tasked with determining the maximum output that should be achievable by each motor at any given point in time. If wheel-slip is sensed, the wheel & motor at fault will receive reductions in power until the wheel-slip is mitigated. If user ejection is sensed, all motor power will immediately be reduced down to a walking speed or less.

## Wheel RPM Sensing
To reiterate, losing static friction traction of the powered wheels of the skateboard poses a significant safety risk, as otherwise the user’s control of the board’s motion / inertia is severely compromised. We want the wheels to be rolling such that the rotational velocity of the outside of the wheel is equal to the translational velocity of that wheel; specifically, we will need to sense when this is no longer the case. To accomplish this, we wish to incorporate sensors that can detect the revolutions per minute for each of the four wheels of the longboard. The most likely sensor type we are considering at this time are infrared sensors. We will then send this data to the Safety Microcontroller, where our algorithm, using the front, non-powered wheels as reference, will determine whenever wheel-slip is occurring.

## User Ejection Sensing
In order to detect when a user is physically on top of and actively using the board, we plan to use a pressure-sensitive conductive sheet under each of the two trucks of the longboard. The particular material we are currently considering is velostat, as velostat’s electric resistance decreases under increased weight / pressure. We hope to send this resistance measurement to the Safety Microcontroller, where a simple algorithm on that data can determine whether the longboard is under the weight of a person or not.

## Obstacle Detection
In nearly every common riding scenario, an electric longboard user will be primarily focused on the approaching ground, ready to navigate any traffic, obstacles, or small obstructions that are fastly approaching. The user’s ability to do this is greatly suppressed under nighttime/dark conditions. While headlights affixed to the board will undoubtedly help, they do not completely remedy the issue. We hope to implement an approaching obstacle detection system via infrared and/or ultrasonic sensors. We wish to send this information to the Safety Microcontroller for processing. When it is determined that a significant obstacle is detected, we wish to communicate that to the user via the wireless remote (discussed in the following component).

## Wireless Remote with Board-User Feedback
We wish to design and build a remote control that is up to par with the best electric skateboard remotes currently available, including a dead man switch and a spring-loaded thumbwheel. In addition, we find it crucial to communicate to the user whether a safety hazard has been sensed. We are currently considering accomplishing this communication either through a beeping speaker or a haptic rumble feature.

# Criterion for Success

Our criterion for success would be as follows:
- Our Electric Longboard performs all of the basic functions & features of a commercially-available electric longboard, with user control via wireless remote.
- Our Electric Longboard detects when one or both powered wheels are exhibiting wheel-slip / loss of static friction traction
- Our Electronic Longboard mitigates any wheel-slip / loss of static friction traction to either powered wheel
- Our Electronic Longboard detects when the weight upon it is less than the threshold expected for a typical human user.
- Our Electronic Longboard reduces motor power to the minimum when the weight sensed is below threshold
- Our Electronic Longboard detects approaching significant obstacles, even under low light conditions.
- Our Electronic Longboard notifies the user whenever a safety feature has been activated.

Decentralized Systems for Ground & Arial Vehicles (DSGAV)

Mingda Ma, Alvin Sun, Jialiang Zhang

Featured Project

# Team Members

* Yixiao Sun (yixiaos3)

* Mingda Ma (mingdam2)

* Jialiang Zhang (jz23)

# Problem Statement

Autonomous delivery over drone networks has become one of the new trends which can save a tremendous amount of labor. However, it is very difficult to scale things up due to the inefficiency of multi-rotors collaboration especially when they are carrying payload. In order to actually have it deployed in big cities, we could take advantage of the large ground vehicle network which already exists with rideshare companies like Uber and Lyft. The roof of an automobile has plenty of spaces to hold regular size packages with magnets, and the drone network can then optimize for flight time and efficiency while factoring in ground vehicle plans. While dramatically increasing delivery coverage and efficiency, such strategy raises a challenging problem of drone docking onto moving ground vehicles.

# Solution

We aim at tackling a particular component of this project given the scope and time limitation. We will implement a decentralized multi-agent control system that involves synchronizing a ground vehicle and a drone when in close proximity. Assumptions such as knowledge of vehicle states will be made, as this project is aiming towards a proof of concepts of a core challenge to this project. However, as we progress, we aim at lifting as many of those assumptions as possible. The infrastructure of the lab, drone and ground vehicle will be provided by our kind sponsor Professor Naira Hovakimyan. When the drone approaches the target and starts to have visuals on the ground vehicle, it will automatically send a docking request through an RF module. The RF receiver on the vehicle will then automatically turn on its assistant devices such as specific LED light patterns which aids motion synchronization between ground and areo vehicles. The ground vehicle will also periodically send out locally planned paths to the drone for it to predict the ground vehicle’s trajectory a couple of seconds into the future. This prediction can help the drone to stay within close proximity to the ground vehicle by optimizing with a reference trajectory.

### The hardware components include:

Provided by Research Platforms

* A drone

* A ground vehicle

* A camera

Developed by our team

* An LED based docking indicator

* RF communication modules (xbee)

* Onboard compute and communication microprocessor (STM32F4)

* Standalone power source for RF module and processor

# Required Circuit Design

We will integrate the power source, RF communication module and the LED tracking assistant together with our microcontroller within our PCB. The circuit will also automatically trigger the tracking assistant to facilitate its further operations. This special circuit is designed particularly to demonstrate the ability for the drone to precisely track and dock onto the ground vehicle.

# Criterion for Success -- Stages

1. When the ground vehicle is moving slowly in a straight line, the drone can autonomously take off from an arbitrary location and end up following it within close proximity.

2. Drones remains in close proximity when the ground vehicle is slowly turning (or navigating arbitrarily in slow speed)

3. Drone can dock autonomously onto the ground vehicle that is moving slowly in straight line

4. Drone can dock autonomously onto the ground vehicle that is slowly turning

5. Increase the speed of the ground vehicle and successfully perform tracking and / or docking

6. Drone can pick up packages while flying synchronously to the ground vehicle

We consider project completion on stage 3. The stages after that are considered advanced features depending on actual progress.

Project Videos