https://github.com/nhathout/pira
Personal Indoor Robot Assistant.
https://github.com/nhathout/pira
ir-sensors obstacle-avoidance obstacle-avoidance-robot optitrack robot streamlit
Last synced: 2 months ago
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Personal Indoor Robot Assistant.
- Host: GitHub
- URL: https://github.com/nhathout/pira
- Owner: nhathout
- Created: 2024-12-19T07:06:34.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-12-23T06:16:27.000Z (4 months ago)
- Last Synced: 2025-02-17T06:41:53.293Z (2 months ago)
- Topics: ir-sensors, obstacle-avoidance, obstacle-avoidance-robot, optitrack, robot, streamlit
- Language: C
- Homepage:
- Size: 1.33 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PIRA - Personal Indoor Robot Assistant
**Date:** December 10, 2024
## Overview
This project centers on building a personal indoor robot assistant capable of autonomous navigation and real-time obstacle avoidance in an indoor environment. The system supports both fully autonomous waypoint traversal, guided by an indoor positioning system (Optitrack), and manual driving via WASD inputs. A Node.js server coordinates data exchange among multiple robots on the same Wi-Fi network, while a Streamlit interface provides a live 2D visualization of each robot’s position and waypoints. TingoDB stores positional data for real-time monitoring and historical analysis.
---
## Key Features
- **Autonomous Navigation**
- Uses feedback control (PID) to follow predefined waypoints within a ±10cm margin.
- Leverages Optitrack for accurate (x, z, θ) positioning updates.
- **Obstacle Avoidance**
- Sharp-IR sensors detect nearby obstacles.
- Priority-based control ensures the robot diverts from its path to avoid collisions.
- **Multimodal Control**
- **Automatic Mode:** Robot autonomously moves between waypoints, using PID corrections and sensor feedback.
- **Manual (WASD) Mode:** Users can override autonomy and steer the robot via keyboard inputs sent through the Node.js server.
- **Real-time Monitoring & Visualization**
- Live 2D graphics in a Streamlit dashboard show robot positions, waypoints, and obstacle triggers.
- TingoDB logs each position update, enabling real-time data tracking and simple analytics.
- **Scalable Networking**
- Node.js server handles multiple robots simultaneously.
- Each robot maintains continuous Wi-Fi connectivity and exchanges UDP messages for minimal overhead.
---
## System Requirements and Architecture
### Hardware Integration
1. **ESP32 Microcontroller**
- Central controller for motor actuation, sensor readings, and Wi-Fi communication.
- Configured with FreeRTOS tasks to manage concurrency.
2. **Sharp-IR Sensor**
- Analog distance sensor (ADC-based) providing obstacle detection.
- Calibrated to detect objects within a configurable distance threshold.
3. **Optitrack System**
- Provides precise localization of the robot in indoor environments.
- Streamed position/heading data is used for navigation and control.
4. **Motors & Driver**
- Controlled via PWM (LEDC on the ESP32) for linear and rotational motion.
- Ensures fine-tuned speed adjustments, crucial for PID feedback loops.
5. **Wi-Fi Networking**
- All robots connect to the same local network.
- UDP sockets facilitate efficient real-time messaging between robots and the server.
---
## Solution Design
### Core Application Initialization (app_main)
- **Wi-Fi Setup:**
Configures SSID, password, and reconnect parameters. Ensures each robot remains online to receive commands and transmit location updates.
- **Motor Control Configuration:**
Initializes PWM channels (LEDC) for forward, backward, turning, and stopping states.
- **Sharp IR Sensor & ADC:**
Sets up an ADC channel to read distance values. Calibrates the voltage-to-distance curve for obstacle detection.
- **Timers and Tasks Setup:**
Creates periodic timers for tasks like checking sensor readings, computing PID corrections, and sending position requests.
### Key FreeRTOS Tasks
1. **Network Listener Task**
- Listens for UDP messages (e.g., WASD commands, mode toggles, new waypoint definitions).
- Parses incoming data and updates the robot’s operating state (manual vs. autonomous).
- In autonomous mode, processes next waypoint instructions and speed/direction updates.
2. **Request Location Task (request_location)**
- Periodically sends requests to the Node.js server to obtain the robot’s latest position from Optitrack.
- Updates internal state with (x, z, θ) data for use in PID navigation.
- In autonomous mode, computes the heading/distance to the target waypoint.
3. **Motor Control Task**
- Implements a finite state machine (FSM) for movement: `STATE_MOVE_FORWARD`, `STATE_GO_LEFT`, `STATE_GO_RIGHT`, `STATE_STOP`, etc.
- Adjusts PWM duty cycle based on commands from manual input or PID output.
- Manages turning maneuvers and speed scaling for smooth directional changes.
4. **Obstacle Detection Task**
- Continuously reads Sharp-IR sensor values.
- Triggers immediate avoidance behavior if an obstacle is within the critical threshold.
- Temporarily overrides PID path corrections, prioritizing collision prevention.
5. **Autonomous Navigation & PID**
- Uses proportional, integral, and derivative terms to stabilize heading toward each waypoint.
- Computes heading error (difference between current angle and target angle).
- Dynamically adjusts motor outputs to minimize error while respecting obstacle avoidance triggers.
---
## Network Communication
### Node.js Server
- **Command Dispatch:**
Receives manual drive commands (WASD) or mode-change signals from a user interface.
Broadcasts these instructions to the specified robot via UDP.
- **Location Management & TingoDB:**
Aggregates position reports from each robot.
Stores each robot’s (x, z, θ) data in TingoDB for real-time updates and historical lookback.
- **Streamlit Dashboard:**
Polls the database, displaying a 2D map of robots, waypoints, and obstacles.
Allows users to observe multiple robots’ positions and statuses simultaneously.
### UDP Protocol
- Minimal overhead ensures fast, near-real-time communication for multi-robot scenarios.
- Periodic acknowledgment or keep-alive checks maintain consistent connectivity.
---
## Results
- **Consistent Waypoint Navigation:**
Achieved ±10cm accuracy in autonomous mode, leveraging PID corrections and accurate Optitrack data.
- **Effective Obstacle Avoidance:**
Sharp-IR sensor integration prevented collisions, with priority-based logic overriding normal path following as needed.
- **User-Friendly Remote Control:**
Seamless toggling between manual (WASD) commands and full autonomy.
Real-time feedback through the Node.js server and the Streamlit interface.
- **Scalable Multi-Robot Setup:**
Multiple robot assistants operate in parallel, each with independent control loops but centralized data coordination.
---
## Challenges
- **Real-Time Communication & Coordination:**
Managing concurrent UDP messages for multiple robots without packet collisions or data delays.
- **PID Tuning:**
Ensuring smooth path tracking without oscillations required iterative parameter tuning under varying speeds and load conditions.
- **Sensor Calibration:**
The Sharp-IR sensor’s accuracy can fluctuate based on environment and lighting, necessitating consistent recalibration.
---
## Potential Improvements
- **Interrupt-Driven Sensor Reads:**
Using hardware interrupts instead of polling for sharper real-time responsiveness.
- **Path Planning Algorithms:**
Incorporate more advanced planning (e.g., A*, RRT*) for complex obstacle layouts.
- **3D Visualization or SLAM:**
Extend the system to build a richer map of the environment for real-time 3D tracking.
- **Security Enhancements:**
Implement encryption for UDP messages or advanced authentication methods for sensitive deployments.
---
## Demonstrations
- [**Functionality Demo Video**](https://drive.google.com/file/d/1VPvRS4MaWk1iu6lj2RG2DcL4Mfvxja_7/view?usp=sharing)
- [**Design Walkthrough Video**](https://drive.google.com/file/d/1IIHNn9ovCAJhkweRPx5khYDsRs2hRVlT/view?usp=sharing)
## Appendix
Circuit diagram:
This personal indoor robot assistant project brings together embedded systems design, real-time networking, and automated control to achieve robust waypoint navigation and obstacle avoidance. Its flexible architecture, modular codebase, and user-friendly interface make it a compelling foundation for further robotic exploration and innovation.