Project Overview

Autonomous Mobile Robot for Smart Warehouse Intralogistics

Course: CDE2310 — Fundamentals of Systems Design (AY 25-26) Institution: NUS EDIC Team: Group 1 — Russell, Shandrico, Alex, Moksh Software Version: v1.0.0

Mission Statement

This project delivers a TurtleBot3 Burger-based autonomous mobile robot (AMR) that navigates an unknown warehouse maze, detects delivery stations via ArUco markers, and dispenses ping pong balls into receptacles — all within a 25-minute window with no human teleoperation. The mission comprises three objectives: static delivery at Station A, dynamic delivery at Station B (oscillating receptacle), and an optional elevator traversal to Level 2 via REST API.

System Summary

System Architecture Diagram System architecture showing data and power flows across all four subsystems.

Parameter	Value
Platform	TurtleBot3 Burger + custom launcher payload
Total Mass	1402.24 g (1.40 kg)
Overall Dimensions (L × W × H)	138 mm × 178 mm × 192 mm
Battery	Li-Po 11.1 V, 1800 mAh
Compute	Raspberry Pi 4B + OpenCR 1.0
Sensors	LDS-02 LiDAR, RPi Camera Module V2 (8 MP, IMX219)
Drive	2 × Dynamixel XL430-W250
Launcher	Dual counter-rotating flywheels (2 × RF300 DC motor) + SG90 servo gate
Ball Storage	9 ping pong balls (curved gravity-feed tube)
Mission Window	25 minutes

Subsystem Decomposition

The system is decomposed into four interdependent subsystems:

Navigation Subsystem — Responsible for autonomous exploration, mapping, localisation, and goal-directed path planning. The robot builds a real-time occupancy grid of the unknown maze using LiDAR-based SLAM (Cartographer), explores unmapped regions via frontier-based navigation (explore_lite), and executes obstacle-free paths using the Nav2 stack with DWB local planner.

Sensor Subsystem — Provides environmental perception through two complementary sensors. The LDS-02 LiDAR delivers 360° range data for SLAM map construction and obstacle avoidance. The RPi Camera V2 handles ArUco marker detection and 6D pose estimation, publishing marker poses to ROS 2 for station localisation and docking alignment.

Launcher Subsystem — Handles payload storage, feeding, and delivery. Nine ping pong balls are stored in a gravity-fed curved tube that does not obstruct the LiDAR field of view. A servo-actuated gate controls ball release into a dual counter-rotating flywheel mechanism, which launches balls with consistent velocity and trajectory.

Computation Subsystem — The Raspberry Pi 4B serves as the primary compute platform, running all ROS 2 nodes including SLAM, navigation, marker detection, and mission control. The OpenCR 1.0 board handles low-level motor control, IMU data, and encoder feedback. All inter-subsystem communication occurs through ROS 2 topics and services, and the entire system is deployable from a single launch file.

Key Performance Highlights

Metric	Specification
SLAM Map Resolution	≤ 0.05 m/cell
Obstacle Reaction Latency	< 100 ms
Marker Detection Range	0.3–2.2 m (640×480, 5 cm marker)
Docking Tolerance	Lateral ±2 cm, Angular ±3°, Distance 0.2–1.0 m
Battery Life	~4–5 full mission runs per charge

Documentation Map

Document	Description
Requirements	Functional, non-functional requirements, constraints, and system specifications
Con-Ops	Concept of Operations — mission phases, operational scenarios, and environment
High Level Design	System architecture, subsystem decomposition, and design rationale
Sub System Design	Detailed design for electrical, mechanical, and software subsystems
Interface Control Documents	Interface definitions, communication protocols, and data flows
Software Development	Software architecture, ROS 2 nodes, algorithms, and source code structure
Testing	Testing methodology, results, and system integration test procedures
User Manual	Setup, deployment, operation, troubleshooting, and maintenance

CDE2310 Group 1 — AMR for Smart Warehouse Intralogistics

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