Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 43 | Autonomous Transport Car |
Xubin Shen Yiqi Tao |
design_document1.pdf proposal1.pdf |
Chushan Li | |
| Project: Autonomous Transport Car Team Members: Ma Jingyuan(674072315) Xubin Shen (677258677) Tao Yiqi(670182981) Zhang Haotian(676598571) Problem Overview: Recent years, the demand for autonomous goods transport systems is growing and people are seeking ways to improve efficiency in logistics. Traditional retrieval methods are generally manual. Workers need to deliver the packages by hand, which is very exhausting and time-consuming with low reliability. Existing solutions often highly depend on human operations, lacking automation in identifying, selecting and transporting items. This limits the further development of logistics industry. Besides, there are also big challenges to train the transport devices to find and follow proper path accurately. Additionally, a convenient way for users to issue instructions and get the feedback is also necessary. For these problems, we propose an autonomous transport car that can grab goods and deliver, with intelligent object recognition based on color and accurate navigation systems. This project aims to design an autonomous system of searching, grabbing, and transporting designated items, improving efficiency and reducing the dependence on human. Solution Overview: The Autonomous Transport Car project aims to develop an intelligent vehicle capable of autonomously searching for and transporting specified goods. This solution integrates advanced technologies such as autonomous driving, motor systems, mechanical manipulation, and computer vision to achieve efficient and reliable operation. Autonomous Driving System: The design includes an autonomous driving system that navigates through the environment using sensors and algorithms. It follows a preset ground trajectory, including obstacle detection and avoidance features, to move to the designated platform. Motor Power System: The vehicle is equipped with an efficient and stable motor power system, utilizing power electronic components and control algorithms。 Gripping Structure: The mechanical structure is designed and assembled to pick up goods from shelves. It can adjust the gripping force according to the size and shape of the items, and this structure is controlled by motors and actuators. Camera Recognition: The vehicle is equipped with a camera recognition system that can identify the types and colors of goods on the shelves, locate and select the specified items. Solution Components: 1.Autonomous Driving System oFollows preset trajectories via IR sensors and PID control. oAvoids obstacles using ultrasonic sensors and reroutes dynamically. 2.Camera Recognition System oRaspberry Pi Camera Module V2 with OpenCV for color-based item detection. 3.Gripping Mechanism o2-DOF servo-driven gripper with pressure feedback, tailored for lightweight boxes. 4.Communication & Control oArduino Mega handles motor control and sensor data. oHC-05 Bluetooth module enables app-based commands (e.g., “return to base”). 5.User Interface oMobile app with minimalistic UI for issuing commands and receiving status updates. Project Goals Successful outcomes will include: 1.Functional Hardware Prototype with Technical Specifications oA 4-wheel modular chassis using 12V geared DC motors, controlled by an L298N motor driver. oComputational Units: Raspberry Pi 4B (4GB RAM) for computer vision (OpenCV-based color detection) and high-level navigation logic. Arduino Mega for low-level motor control, sensor interfacing (e.g., ultrasonic, IR), and gripper actuation. oSensors: 3x TCRT5000 IR sensors for line following. 2x HC-SR04 ultrasonic sensors for obstacle detection. FlexiForce pressure sensors on the gripper for force feedback. oActuators: 2-DOF servo-based gripper (SG90 servos) optimized for lightweight (≤500g), box-shaped items. oPower: Dual 7.4V LiPo batteries (separate power supply for motors and logic units). oCommunication: HC-05 Bluetooth module for app integration. 2.Reliable Software Implementation oAutonomous Navigation: PID-controlled line tracking using IR sensors. Obstacle avoidance via ultrasonic sensors with dynamic path recalculation. oObject Recognition: Color-based identification (targeting specific HSV ranges) using Raspberry Pi Camera Module V2. Localization within a 1m x 1m shelf area. oApp Integration: Basic command interface (e.g., “retrieve red item”) with real-time status feedback via Bluetooth/Wi-Fi. 3.Scope and Success Metrics oFunctional Limitations: Gripping mechanism designed for standardized, rigid items (no fragile/irregular shapes). Navigation restricted to flat indoor environments with clear line markings. oDemonstrated Outcomes: End-to-end operation in a 5m x 5m test area: Identify target item → Plan path → Grasp → Transport → Deliver. ≥85% success rate across 20 trials. Expectations for Team Members Attend all meetings prepared (e.g., review agendas, complete assigned tasks). Communicate progress, blockers, or delays proactively (no “radio silence”). Respect deadlines; renegotiate timelines early if conflicts arise. Provide constructive feedback during reviews and respond openly to critiques. Document work thoroughly for seamless handovers. Escalate risks (e.g., technical hurdles, miscommunication) immediately. |
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