Project

# Title Team Members TA Documents Sponsor
27 Supernumerary Robotic Limbs
 Haotian Jiang
Xuekun Zhang
Yichi Zhang
Yushi Chen
 design_document2.pdf
final_paper2.pdf
proposal2.pdf
 Liangjing Yang
# TEAM MEMBERS
Haotian Jiang (hj24)
Yushi Chen
Yichi Zhang
Xuekun Zhang(xuekunz2)

# PROBLEM
Supernumerary Robotic Limbs (SRLs) have emerged as a fascinating advancement in the field of robotics, offering the potential to augment human capabilities by providing additional robotic limbs. However, a significant current problem plaguing the implementation of SRLs revolves around integration challenges. The seamless coordination between these robotic limbs and the user's natural limbs remains a complex issue. Achieving intuitive and synchronized control over the supernumerary limbs, ensuring they move in harmony with the user's intended actions, poses a considerable technological hurdle. Additionally, the current state of SRLs faces limitations in adaptability to various tasks and environments, hindering their practicality.

# SOLUTION OVERVIEW
1. Seamless Coordination and Control: One of the main challenges is achieving intuitive and synchronized control between SRLs and the user's natural limbs. This requires advanced sensor technologies and algorithms capable of interpreting human intent and translating it into precise robotic movement.

Solution Ideas:
Advanced Sensory Feedback: Implementing a sophisticated sensory feedback system that can accurately detect and interpret the user's movements and intentions. This could involve a combination of technologies like electromyography (EMG) to read muscle signals, motion sensors, and perhaps even neural interfaces. Machine Learning Algorithms: Developing algorithms capable of learning and adapting to the user's movement patterns. Machine learning can help in predicting and synchronizing the movements of the robotic limbs with the user's natural limbs. Haptic Feedback: Integrating haptic feedback into the SRL system can provide the user with tactile information about the limb's position and the forces it encounters, enhancing control.

2. Adaptability to Various Tasks and Environments: SRLs need to be versatile enough to perform a wide range of tasks in different environments, which is a challenging aspect of their design and functionality.

Solution Ideas:
Modular Design: Creating a modular SRL system where different types of limbs or tools can be attached and detached as needed could provide the versatility required for different tasks. Environment Sensing and Adaptation: Incorporating sensors that allow the SRL to understand and adapt to different environments. This could involve visual recognition systems, lidar for spatial awareness, or other environmental sensors. User-Defined Customization: Allowing users to customize the settings and behavior of the SRLs for specific tasks could enhance their practicality in various scenarios.

3. User Training and Interface Design: Another critical aspect is how users interact with and control the SRLs. The learning curve and ease of use are important for wide adoption.
Solution Ideas:
Intuitive User Interfaces: Designing user interfaces that are intuitive and easy to learn can significantly enhance the user experience. This could involve gesture-based controls, voice commands, or even direct brain-computer interfaces for more advanced implementations. Simulation and Training Programs: Providing simulation-based training tools can help users learn to control the SRLs effectively, ensuring they can be used efficiently in real-world tasks.

4. Safety and Compliance: Ensuring the safety of both the user and those around them is paramount, especially in environments where human-robot interaction is frequent.
Solution Ideas:
Real-time Safety Protocols: Implementing real-time monitoring and safety protocols that can prevent accidents or injuries. This includes collision avoidance systems and emergency stop mechanisms. Compliance with Regulations: Adhering to existing robotic and workplace safety regulations, and contributing to the development of new standards specifically for SRLs.

# CRITERION FOR SUCCESS
For our Supernumerary Robotic Limbs (SRLs) project, success is contingent upon meeting specific high-level criteria. Stability is a paramount goal, demanding that signals are received seamlessly, without any loss, especially within the confines of a room, even when there is a gap of two chairs. Affordability is a key criterion, emphasizing the importance of keeping costs low to enable widespread adoption and accessibility. Efficiency is critical; the process from user input to signal collection and transmission should operate with minimal delay. Aesthetic considerations are not overlooked; the design should be widely accepted and easily producible through technologies like 3D printing. Feedback mechanisms are crucial for user satisfaction; users should receive prompt and meaningful feedback from the system, enhancing their experience and trust. Additionally, the system's concurrency is vital; it must adeptly handle signals from multiple limbs in real-time, ensuring seamless integration and coordination. These high-level goals collectively define the success of our Supernumerary Robotic Limbs project.

# DISTRIBUTION OF WORK

Yichi Zhang: Part of the code work and electronic control system design and equipment selection

Yushi Chen: Part of the code work and electronic control system design and equipment selection

Xuekun Zhang: Progress major code work and overall design work

Haotian Jiang: 3D print structure design and physical setup for the hardware part.

ML-based Weather Forecast on Raspberry Pi

Xuanyu Chen, Zheyu Fu, Zhenting Qi, Chenzhi Yuan

Featured Project

#Team Members

Zheyu Fu (zheyufu2@illinois.edu 3190110355)

Xuanyu Chen (xuanyuc2@illinois.edu 3190112156)

Chenzhi Yuan (chenzhi2@illinois.edu 3190110852)

Zhenting Qi (qi11@illinois.edu 3190112155)

#Problem

Weather forecasting is crucial in our daily lives. It allows us to make proper plans and get prepared for extreme conditions in advance. However, meteorologists always get it wrong half of the time and still keep their job :) To overcome the limitations of traditional weather forecasting, machine learning models have become increasingly important in weather forecasting. Building our own weather forecast ML system is a perfect idea for us to analyze vast amounts of area data and generate more accurate and timely weather predictions on the go in our surrounding areas.

#Solution Overview

A weather forecast system can be created by using a few different hardware components and software tools. Our solution mainly consists of two parts. For weather measurement and data collection, temperature, humidity, and barometric pressure sensors are considered the main components. A machine learning-based algorithm is to be applied for data analysis and weather predictions.

#Solution Components

##Hardware Subsystem

Due to the complexity of weather conditions, our system incorporates the following weather indicators and their corresponding collectors:

-a barometric pressure sensor, a temperature sensor, and a humidity sensor

-a digital thermal probe for heat distribution

-an anemometer for wind speed, wind vane for wind direction, and rain gauge for precipitation

The aforementioned equipment would be integrated into a single device, and weatherproof enclosures are needed to protect it. Plus, a Raspberry Pi, either with built-in wireless connectivity or a WiFi dongle, is required for conducting computations.

##Software Subsystem

A practically usable weather forecast system is supposed to make reliable predictions for real-world multi-variable weather conditions. We apply Machine Learning techniques to suffice such generalization to unseen data. To this end, a high-quality dataset for training and evaluating the Machine Learning model is required, and a specially designed Machine Learning model would be developed on such a dataset. Once a well-trained system is obtained, we deploy the such model on portable devices with easy-to-use APIs.

#Criterion for Success

1. The weather measurement prototype with sensors should be able to accurately collect the temperature, humidity, and barometric pressure. etc.

2. A machine learning algorithm should be successfully trained to make predictions on the weather conditions: rainy, sunny, thunderstorm, etc.

3. Our system can forecast the weather in Haining, in real-time, and/or longer-period forecast.

4. The forecasted weather information could be demonstrated elegantly through some UI interface. A display screen would be a baseline, and an application on phones would be extra credit if time permitted.

5. Extra: Make our own weather dataset for Haining. If good, make it open-source.

#Work Distribution

**EE Student Zheyu Fu**:

-Design the sensor module circuit

-Development of visualization interface

**ECE Students Xuanyu Chen & Zhenting Qi**:

-Weather data collection and analysis

-Build and test Machine Learning model on Raspberry Pi

**ME Student Chenzhi Yuan**:

-Physical structure hardware design

-Proper distribution of the sensors to collect accurate data on temperature, humidity, barometric pressure, etc.