MARC: Mobile Autonomous Robotic Cart for Automated Material Transport
Mobile Autonomous Robotic Cart (MARC) represents a significant advancement in automated material handling within indoor environments such as warehouses, hospitals, manufacturing plants, and retail logistics. MARC combines autonomous navigation, obstacle avoidance, load-handling capabilities, and IoT integration into a compact, efficient, and intelligent robotic platform.
Material transport is a key logistics function that often consumes substantial time and labor. Traditional Automated Guided Vehicles (AGVs) require predefined paths and significant infrastructure changes. MARC eliminates these limitations through full autonomy, adaptability, and intelligent routing, supporting Industry 4.0 goals and smart facility automation.
MARC embodies the convergence of robotics, AI, and IoT to deliver a transformative solution for autonomous material handling. With flexible deployment, rich sensor integration, and robust software architecture, MARC systems promise to redefine how materials are transported in modern facilities.
Mechanical Design
MARC is built on a compact, low-profile chassis with a modular design allowing payload flexibility. It typically features:
- Chassis Frame: Aluminum alloy for strength and lightweight characteristics.
- Drive System: Differential drive or omnidirectional wheels powered by brushless DC motors.
- Payload Interface: Configurable shelving or docking mechanisms for containers or trays.
- Battery System: Lithium-ion battery pack supporting 8–12 hours of operation.
2.2 Hardware Components
| Component | Description |
|---|---|
| Onboard Computer | ARM or x86-based processor (e.g., NVIDIA Jetson) |
| Motor Controllers | PID-tuned ESCs with feedback loops |
| Power Management Unit | Battery monitoring, power distribution |
| Charging Dock | Auto-docking capability for charging |
3. Sensing and Perception
MARC relies on a fusion of sensors for environment awareness:
3.1 Sensor Suite
- LIDAR: 2D or 3D scanning for SLAM and obstacle detection.
- Depth Cameras: Real-time obstacle tracking and human detection.
- Ultrasonic Sensors: Close-range obstacle avoidance.
- IMU (Inertial Measurement Unit): Stabilization and orientation tracking.
- Wheel Encoders: Odometry for accurate position estimation.
3.2 Sensor Fusion
Using an Extended Kalman Filter (EKF), MARC integrates data from LIDAR, IMU, and encoders to compute robust and accurate pose estimation in real-time.
4. Navigation and Autonomy
4.1 Simultaneous Localization and Mapping (SLAM)
MARC uses SLAM algorithms (e.g., GMapping, Cartographer, or RTAB-Map) to build and update a map of its environment while simultaneously localizing itself within that map.
4.2 Path Planning
It uses a layered approach:
- Global Planner: A* or Dijkstra for long-range routing.
- Local Planner: Dynamic Window Approach (DWA) or Timed Elastic Band (TEB) for real-time path adjustment.
- Collision Avoidance: Reactive behaviors using sensor feedback.
5. Software Stack
MARC is powered by an open-source, modular software architecture:
5.1 Middleware
- ROS 2 (Robot Operating System): Core communication framework providing topics, services, and action interfaces.
5.2 Key Packages
nav2: Navigation stack for localization, planning, and control.move_base_flex: Flexible navigation backend.rplidar_ros: LIDAR driver and data publisher.rtabmap_ros: Visual SLAM integration.tf2: Frame transformations for sensor alignment.
5.3 User Interface
- Web-based dashboard for monitoring and task assignment.
- RESTful API for third-party integration with WMS or hospital logistics systems.
6. Communication and Cloud Integration
- Wireless Networking: Wi-Fi 6 / 5G support for cloud connectivity and fleet management.
- IoT Integration: MQTT or HTTP-based telemetry for data logging, diagnostics, and remote control.
- Over-the-Air (OTA) Updates: Secure firmware and software upgrades.
7. Applications
7.1 Industrial Warehouses
- Autonomous inventory movement between storage and packaging stations.
- Real-time coordination with ERP and warehouse management systems.
7.2 Hospitals
- Safe and autonomous delivery of medications, specimens, or supplies across departments.
7.3 Manufacturing
- Just-in-time delivery of parts to assembly lines using dynamic job scheduling.
7.4 Retail and E-commerce
- Efficient order fulfillment and restocking in dynamic retail environments.
8. Safety and Compliance
- Fail-safe Mechanisms: Emergency stop, redundant sensors, and watchdog timers.
- Standards Compliance: ISO 3691-4, ANSI/RIA R15.06 for mobile robotics.
- Human Interaction: Audio/visual alerts, adaptive speed control in crowded areas.
9. Future Developments
Ongoing R&D aims to enhance MARC’s:
- Swarm Intelligence: Cooperative behavior among multiple carts.
- AI-Based Scene Understanding: Using deep learning for semantic navigation.
- Energy Optimization: Smart route planning to conserve power and extend range.
Typical Use Case
- Manufacturing — As products are manufactured, MARC is loaded and operator presses 2 to send it autonomously to inventory area.
- Inventory — Products are pulled from stock and loaded onto MARC and puller presses 3 to send to final inspection.
- Final Inspection — Once products are ready for shipment, inspector presses 4 to send loaded MARC to shipping area.
- Shipping — Shipment is loaded onto truck and once the cart is empty, loader presses button for the destination where they would like to send MARC for next task.