2. Technical Specifications
2.1 Robot Platform
Hardware platform: TurtleBot 4 (Create 3 differential-drive base) with integrated sensing stack.
- Create 3 base: wheel encoders, IMU, hazard sensors (bump, cliff, wheel-drop, etc.)
- 2D LiDAR: for obstacle detection and 2D mapping inputs
- RGB-D camera: for OOI detection/tracking and depth-based range estimation
- Compute: onboard (Raspberry Pi / platform default), with optional offboard workstation if explicitly documented
Simulation environment (for early integration):
- TurtleBot 4 simulation in Gazebo/Streamlit (used for early pipeline verification and regression tests)
2.2 Kinematic Model
Declared model: Differential Drive (unicycle-equivalent planar model)
Let robot pose be x = [x, y, θ]ᵀ and control inputs be u = [v, ω]ᵀ:
- ẋ = v cos(θ)
- ẏ = v sin(θ)
- θ̇ = ω
Wheel mapping (for reference; r = wheel radius, L = wheel separation):
- v = (r/2)(ω_R + ω_L)
- ω = (r/L)(ω_R − ω_L)
2.3 Perception Stack (Sensors Used)
Required baseline sensors:
- 2D LiDAR: obstacle geometry, local collision checking, 2D mapping support
- RGB-D camera: OOI detection/tracking + depth-based range to target
- IMU: angular rate/orientation aiding for stable heading estimation
- Wheel encoders/odometry: incremental motion estimation for navigation and tracking stabilization
Safety and hazard sensing:
- Bumper switches: contact detection → immediate stop / back-off logic
- Cliff sensors: edge detection near docks/ramps → stop and retreat
- Wheel-drop / stall / slip / kidnap detection: triggers safe halt and recovery protocol
2.4 Additional Sensors / Safety Additions
- Fiducial markers (AprilTag) on OOI:
-
Justification: robust ID + relative pose for reliable follow control under occlusion/lighting variability [1].
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Justification: non-line-of-sight ranging cues to improve reacquisition in shelving occlusions.
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- Short-range ToF/proximity sensors around blind spots (optional):
- Justification: mitigates single-plane LiDAR limitations (low/high obstacles, near-base blind regions).
- Operator annunciation (LED/beeper) (operational safety enhancement):
- Justification: improves human awareness when robot enters follow/chase modes.
References
[1] E. Olson, “AprilTag: A robust and flexible visual fiducial system,” Proc. IEEE ICRA, 2011.