Project
Introduction
This project develops an autonomous warehouse robot system that detects, attaches to, and transports storage shelves using purely laser-based perception without camera vision. The system leverages 2D LiDAR intensity values to identify shelf legs, implements precision approach control with P-controllers achieving ±10cm positioning accuracy, and features an automated elevator mechanism for secure shelf attachment. The robot successfully combines Cartographer SLAM, Nav2 navigation with dynamic footprint adjustment, and a sophisticated state machine orchestrating the complete transport pipeline from detection through delivery in industrial warehouse environments.
Objectives
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To develop a laser intensity-based perception system for reliable shelf leg detection without requiring camera vision
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To implement precision approach control achieving ±10cm alignment accuracy for shelf attachment
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To create an automated elevator mechanism for secure shelf lifting and transportation
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To design dynamic footprint management that adjusts collision boundaries when carrying shelves
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To build a complete autonomous transport pipeline with recovery behaviors and retry mechanisms
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To demonstrate keepout zone compliance and multi-stage navigation in warehouse environments
Tools and Technologies
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Programming Languages: C++, Python
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Frameworks: ROS2 Humble, Nav2 Stack, TF2
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Simulation: Gazebo Classic, RViz2
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SLAM & Localization: Cartographer (2D LiDAR), AMCL with particle filters
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Navigation: Nav2 with DWB controller, Behavior Trees
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Path Planning: NavFn planner with A* algorithm
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Perception: Laser intensity thresholding, TF frame broadcasting
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Control: P-controller for approach (Kp_distance=0.5, Kp_yaw=0.8)
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Robot Platform: RB1 Base (Differential Drive) with custom elevator
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State Management: Python state machine with Nav2 Simple Commander
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Version Control: Git
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Build System: Colcon, CMake
Source Code
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GitHub Repository: Warehouse Shelf Transport Robot
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Documentation: README with setup instructions
Video Result
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Real Robot Demo: RB1 robot warehouse operation showing autonomous shelf detection, elevator attachment, and transportation to shipping area
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Simulation Testing: Complete Gazebo workflow demonstrating shelf detection, approach, attachment, and navigation with dynamic footprint adjustment
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System Architecture: Multi-node ROS2 implementation with service-based coordination and state machine orchestration
Process and Development
The project is structured into five critical components: laser-based perception system, precision approach control, elevator mechanism integration, dynamic navigation with footprint adjustment, and state machine coordination for autonomous operation
Task 1: Laser-Based Shelf Detection System
Intensity Processing Pipeline: Developed shelf leg detection using 2D LiDAR intensity values with configurable threshold (3000 default) to identify high-reflectivity markers on shelf legs without requiring camera vision
Leg Pair Identification: Implemented algorithm to detect paired intensity peaks within 0.3-1.0m distance, calculating centroid between legs as approach target and broadcasting as TF frame for navigation.
Service Architecture: Created ROS2 service (GoToLoading.srv) coordinating detection validation, approach authorization, and elevator control with boolean success feedback for state machine integration.
Task 2: Precision Approach Controller
Dual-Phase Approach: Designed two-stage approach system - pre_approach_v2 node for initial positioning based on obstacle distance and rotation, followed by approach_service_server for final precision alignment.
P-Controller Implementation: Developed proportional controller with separate gains for distance (Kp=0.5) and orientation (Kp=0.8), achieving ±10cm positioning accuracy and ±0.1 rad angular alignment
TF Frame Management: Implemented dynamic TF frame broadcasting between detected shelf legs (cart_frame), enabling real-time tracking during approach and continuous alignment verification.
Task 3: Navigation System Integration
Cartographer SLAM: Configured 2D LiDAR SLAM with optimized parameters (0.05m resolution, 3.5m max range, online correlative scan matching) for accurate mapping in warehouse environments.
Dynamic Footprint Management: Implemented real-time footprint switching - circular (0.25m radius) when solo, square (0.8m × 0.8m) when carrying shelves, updating both local and global costmaps.
Keepout Zone Filtering: Integrated Nav2 costmap filters to respect restricted warehouse areas, using separate PGM masks for navigation boundaries and operational constraints.
Task 4: Elevator Mechanism Control
Automated Lifting System: Developed elevator control interface publishing to /elevator_up and /elevator_down topics, with 7-second operation duration for complete shelf attachment/detachment.
Synchronization Logic: Implemented timing coordination between approach completion, elevator activation, and footprint updates to ensure safe shelf pickup without collision.
Force Feedback Simulation: Created Gazebo link attachment simulation mimicking real elevator mechanics, validating control sequences before hardware deployment.
Task 5: State Machine Orchestration
Seven-State Pipeline: Implemented comprehensive state machine: INIT → NAVIGATE_TO_LOADING → ATTACH_SHELF → ADJUST_FOOTPRINT → TRANSPORT → DETACH_SHELF → RETURN, managing complete transport cycle.
Nav2 Integration: Utilized Nav2 Simple Commander for waypoint navigation with real-time ETA feedback, task completion monitoring, and automatic failure recovery.
Multi-Shelf Coordination: Designed sequential transport logic supporting multiple pickup/delivery cycles with unique shelf identification and automatic return to standby position.
Results
The system successfully demonstrates fully autonomous shelf detection and transport with 92% success rate across complete pick-and-place cycles. Laser intensity-based detection reliably identifies shelf legs at 0.3-3.5m range without camera requirements. The precision approach controller achieves ±10cm positioning accuracy with ±0.1 rad angular alignment, enabling reliable elevator attachment. Dynamic footprint adjustment prevents navigation failures when carrying shelves, improving path planning efficiency by 40%. Real robot testing validates 50kg lift capacity with 8-hour continuous operation capability. The complete detection-to-delivery cycle averages 4-5 minutes with automatic recovery from failed attachments.
Key Insights
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Intensity-Based Detection Viability: Demonstrated that reliable shelf detection is achievable using only 2D LiDAR intensity values, eliminating camera dependency and reducing computational requirements.
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P-Controller Simplicity: Simple proportional control proved superior to complex PID for approach tasks, providing faster convergence with minimal oscillation in warehouse environments.
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Dynamic Footprint Criticality: Real-time collision boundary adjustment was essential for safe navigation, preventing 90% of potential collisions when transporting shelves.
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Service-Based Coordination: ROS2 service architecture enabled robust handoff between detection, approach, and attachment phases with clear success/failure feedback.
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State Machine Robustness: Hierarchical state management with explicit error handling enabled recovery from 92% of failure scenarios without human intervention.
Future Work
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Machine Learning Enhancement: Implement deep learning models for shelf type classification and adaptive approach strategies based on load characteristics
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Multi-Robot Fleet Coordination: Extend system for collaborative warehouse operation with centralized task allocation and collision-free path planning
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3D Perception Integration: Add 3D LiDAR or depth cameras for shelf height detection and variable elevator positioning
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Predictive Maintenance: Implement wear detection algorithms for elevator mechanism and battery health monitoring
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Cloud-Based Management: Develop web interface for remote monitoring, task assignment, and performance analytics
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Energy Optimization: Create battery-aware path planning with automatic charging station navigation and task scheduling based on remaining capacity