ROBOTICS ENGINEERING DEGREE OBJECTIVES
ROBOTICS ENGINEERING DEGREE OBJECTIVES
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1 Design and complete robotic and embedded systems
solutions that address real-world situations and challenges.
INDOOR ROVER.pdfIndoor Rover
Indoor Rover
The Indoor Security Rover meets this objective by applying robotic and embedded-system design to the real-world challenge of monitoring indoor spaces without requiring a person to remain physically present. I integrated an Arduino-based motor controller, ultrasonic sensing, servo-directed scanning, autonomous obstacle avoidance, and an ESP32 wireless camera into one completed mobile platform. The rover continuously evaluates distance measurements, stops when it detects an obstacle, scans the surrounding area, and selects a clearer path before continuing. Testing allowed me to identify and correct problems involving sensor response, movement timing, route selection, wiring, and wireless stability. The completed system demonstrates how mechanical hardware, embedded programming, sensing, and wireless vision can work together to provide practical mobile monitoring for homes, offices, and other indoor environments.
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GitHub:
DOOR PROJECTION SYSTEM.pdfDoor Projection Security System
Door Projection Security System
The Door Projection Security System meets this objective by applying embedded computing, sensing, video processing, and display technology to the real-world challenge of safely viewing activity outside an entrance. I designed and completed a system that uses an exterior camera, motion and alert states, an NVIDIA Jetson controller, and a projector to display a live, nearly life-size view directly on the inside of a door. This allows the user to see who or what is outside without approaching a window, looking through a narrow peephole, or relying on a small handheld screen. Developing the system required testing camera connectivity, network reliability, video orientation, image scaling, projector placement, and response timing. The completed system demonstrates how embedded technologies can be integrated into a practical security solution that improves visibility, awareness, and user safety.
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2 Demonstrate embedded microprocessor systems and circuit skills.
Battery.pdfBattery-Powered Smart Vision Module
Battery-Powered Smart Vision Module
The Battery-Powered Smart Vision Module meets this objective by demonstrating the design, fabrication, and testing of an embedded microprocessor system centered on the XIAO ESP32S3 Sense. I selected this device because its processor supports camera operation, wireless communication, and embedded web streaming within a compact, low-power platform. I developed the schematic and designed a custom printed circuit board to connect the processor, battery supply, power switch, camera system, status output, and external components. The PCB was prepared for CNC manufacturing, milled, assembled, and tested for correct voltage, polarity, continuity, and signal connections. Firmware was then loaded to verify camera capture, Wi-Fi operation, and live video streaming. This project demonstrates practical circuit skills through component selection, power distribution, PCB routing, soldering, electrical testing, troubleshooting, and the integration of hardware with embedded software.
PanTilt .pdfAI Pan-Tilt Camera
AI Pan-Tilt Camera
The AI Pan-Tilt Camera meets this objective by demonstrating an embedded microprocessor system that controls a two-axis electromechanical platform. The Arduino Uno’s ATmega328P processes the programmed movement sequence and generates precisely timed control signals for the pan and tilt servo motors through digital pins 10 and 11. I developed and tested the circuit connections between the Arduino, servos, external regulated power supply, and shared ground. Using a separate supply protected the Arduino from the servos’ higher current demand while the common ground provided a reliable reference for the control signals. I verified the circuit by moving each axis independently, checking movement direction and safe mechanical limits, and returning both servos to their 90-degree center positions. This project demonstrates microprocessor programming, signal interfacing, power-distribution planning, grounding, wiring, circuit testing, and troubleshooting.
YouTube:
https://youtube.com/shorts/R1tTflTdejw?feature=share
GitHub:
https://github.com/trent229/Autonomous-Pan-Tilt-Tracking-Camera
3 Develop mechanical control systems by implementing
transducers, actuators, feedback, vision, and sensing systems, and other mechanical systems into robotic platforms.
PanTilt .pdfAI Pan-Tilt Camera
AI Pan-Tilt Camera
The AI Pan-Tilt Tracking Camera meets this objective by integrating visual sensing, electromechanical actuation, position control, and a custom mechanical structure into one robotic platform. The camera serves as the vision sensor, while two servo motors act as the pan and tilt actuators. Each servo uses internal position feedback to move to and maintain the angle commanded by the Arduino Uno. The custom CNC-machined aluminum bracket converts the servos’ rotational movement into controlled two-axis camera positioning. I designed, manufactured, assembled, and tested the mechanism by operating each axis independently, verifying movement direction and mechanical limits, and returning the camera to its centered position. This completed stage demonstrates how sensors, actuators, feedback, embedded control, and mechanical components work together to create a functional vision-positioning platform and establish the hardware foundation for future autonomous tracking.
YouTube:
https://youtube.com/shorts/R1tTflTdejw?feature=share
GitHub:
https://github.com/trent229/Autonomous-Pan-Tilt-Tracking-Camera
INDOOR ROVER.pdfIndoor Rover
Indoor Rover
The Indoor Security Rover meets this objective by integrating sensing, feedback, vision, actuation, and mechanical control into a mobile robotic platform. The ultrasonic sensor acts as a transducer by converting reflected sound into distance measurements, while a servo rotates the sensor to scan the available paths on each side of the rover. The drive motors and motor-control circuit act as the primary actuators, converting the Arduino Uno’s commands into forward, reverse, and turning movement. Sensor measurements create a feedback loop in which the software continuously evaluates the rover’s surroundings and changes its mechanical behavior when an obstacle is detected. An ESP32S3 camera provides a live visual feed for remote observation. By combining the chassis, motors, scanning mechanism, sensor feedback, embedded controller, and camera system, the rover demonstrates how multiple mechanical and electronic systems work together to support responsive robotic movement.
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4 Examine and assess a variety of applications within the field of robotics.
Battery.pdfBattery-Powered Smart Vision Module
Battery-Powered Smart Vision Module
The Battery-Powered Smart Vision Module meets this objective by examining how compact, wireless vision systems can support robotic monitoring, environmental observation, and remote situational awareness. I assessed the XIAO ESP32S3 Sense as a processing and camera platform by testing its image capture, wireless streaming, power requirements, physical size, and ability to operate from a battery. I also evaluated how a custom circuit board and enclosure could make the module portable enough to function as a standalone sensor or be integrated into a larger mobile robotic platform. Testing identified practical tradeoffs involving battery capacity, video quality, network reliability, component placement, and enclosure space. This project demonstrates my ability to examine a robotics application, evaluate the hardware and design requirements, and assess the advantages and limitations of using embedded vision in portable robotic systems.
INDOOR ROVER.pdfIndoor Security Rover
Indoor Security Rover
The Indoor Security Rover meets this objective by examining the use of mobile robotics for autonomous indoor monitoring and security. I assessed whether a small robotic platform could navigate an indoor environment, avoid obstacles, and provide a wireless camera view without continuous human control. Testing evaluated ultrasonic sensor accuracy, obstacle-detection distance, scanning angles, movement speed, turning duration, route selection, and Wi-Fi stability. The results revealed limitations involving repeated scanning, inconsistent movement, sensor placement, and temporary network loss, which led me to adjust the control thresholds, motor commands, and scanning sequence. This project demonstrates how mobile robots can extend security monitoring into areas that fixed cameras cannot cover while also showing the practical limitations that must be considered when applying autonomous navigation and wireless vision to a real environment.
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GitHub:
Touch Screen UI.pdfTouch Screen UI
Touch Screen UI
The Touch Screen UI meets this objective by examining the use of a human-machine interface for monitoring and controlling robotic and embedded security systems. I assessed whether a Raspberry Pi Zero W and capacitive touchscreen could provide one accessible interface for viewing camera feeds, accessing rover functions, testing touch input, and monitoring system conditions such as processor temperature, memory, network status, and storage use. Development required evaluating touch response, screen readability, video-stream performance, network latency, navigation layout, and physical placement inside the enclosure. The completed interface demonstrates how touchscreen technology can improve interaction with a robotic platform by presenting system information and controls in one location. It also allowed me to assess the benefits and limitations of using a compact embedded computer as a dedicated robotics control panel.
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GitHub:
PanTilt .pdfAI Pan-Tilt Camera
AI Pan-Tilt Camera
The AI Pan-Tilt Camera meets this objective by examining active camera positioning as an application within robotics. I assessed whether a two-axis servo mechanism could expand a fixed camera’s viewing area by providing controlled horizontal and vertical movement. The evaluation included the Fusion 360 mechanical design, CNC manufacturing process, aluminum bracket assembly, servo power requirements, movement direction, positioning range, mechanical limits, and ability to return both axes to a centered position. Testing confirmed that the completed platform could reposition the camera accurately through programmed Arduino commands while identifying the mechanical and software requirements needed for future autonomous target tracking. This project demonstrates how an active vision platform can support robotic observation and monitoring while also showing the current limitations of operating the mechanism through programmed positions rather than computer-vision feedback.
YouTube:
https://youtube.com/shorts/R1tTflTdejw?feature=share
GitHub:
https://github.com/trent229/Autonomous-Pan-Tilt-Tracking-Camera
Model, analyze, and design systems or processes that
integrate hardware and software to control autonomous
mechanical systems
AI Security Robot Data Platform.pdfAI Security Robot Data Platform
The AI Security Robot Data Platform meets this objective by modeling, analyzing, and controlling the flow of information between the Indoor Security Rover’s hardware and the Jetson-based software platform. I modeled the process as a sequence in which the rover collects ultrasonic measurements, transmits them to the Jetson, filters the readings, classifies the surrounding area, records state changes, and provides autonomous-start and emergency-stop controls. The integrated hardware includes the rover’s ultrasonic sensor, Arduino controller, motors, camera, and wireless communication system. The software includes asynchronous data collectors, a rolling median filter, proximity-state logic, event logging, application programming interfaces, and the control dashboard. By comparing raw and filtered measurements with the rover’s movement states, I could analyze sensor reliability and autonomous behavior. This project demonstrates the design of a coordinated hardware-and-software process for monitoring, evaluating, and safely controlling an autonomous mechanical platform.
YouTube:
GitHub:
https://github.com/trent229/ai-security-robot-data-platform
INDOOR ROVER.pdfIndoor Security Rover
Indoor Security Rover
The Indoor Security Rover meets this objective by integrating sensors, embedded software, motor-control hardware, and mechanical components into an autonomous mobile system. I modeled the rover’s operation as a sense-decide-act process: the ultrasonic sensor measures distance, the Arduino software compares the measurement against programmed thresholds, and the motor-control circuit changes the rover’s physical movement. When an obstacle is detected, the rover stops, reverses, scans to the left and right with its sensor-mounted servo, compares the available routes, and turns toward the clearer path. I analyzed its behavior through repeated testing of sensor distance, scanning angles, motor speed, turning duration, and route-selection logic. Test results were used to adjust the software and improve the rover’s mechanical response. This project demonstrates how coordinated hardware and software can be designed and analyzed to control an autonomous mechanical system.
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GitHub:
Implement artificial intelligence and data systems into robotic platforms.
INDOOR ROVER.pdfIndoor Security Rover
Indoor Security Rover
The Indoor Security Rover meets this objective by implementing a behavior-based intelligent control system that uses sensor data to make autonomous movement decisions. The rover continuously collects ultrasonic distance measurements and applies programmed thresholds to determine whether its path is clear or blocked. When it detects an obstacle, the software stops the motors, reverses the rover, scans both sides with the sensor-mounted servo, compares the measurements, and selects the route with more available space. This sense-decide-act process provides an explainable, rule-based form of artificial intelligence within a mobile robotic platform. The ESP32S3 camera also produces a live wireless video stream for observation. Together, the sensor data, decision logic, camera feed, and motor commands demonstrate how intelligent processing and data can be implemented to support autonomous robotic behavior.
YouTube:
GitHub:
AI Security Robot Data Platform.pdfAI Security Robot Data Platform
The AI Security Robot Data Platform meets this objective by integrating intelligent data processing with the Indoor Security Rover and the larger security system. The Jetson-based platform receives ultrasonic measurements, rover movement states, camera information, and security events from multiple connected devices. A rolling five-sample median filter reduces noise in the rover’s distance measurements before the software classifies the surrounding area as clear, caution, or obstacle. State logic prevents duplicate records, while persistent event logging creates a history that can be used to evaluate the rover’s behavior. The platform also provides autonomous-start and emergency-stop controls through its application programming interface. This project demonstrates how robotic sensor data can be collected, filtered, classified, stored, displayed, and used to support intelligent monitoring and control of a robotic platform.
YouTube:
GitHub:
https://github.com/trent229/ai-security-robot-data-platform
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