Litter and debris accumulation in outdoor pedestrian areas poses significant environmental, aesthetic, and economic challenges, driving up municipal maintenance costs while degrading public spaces used by communities daily. Zpotless addresses this problem by designing a fully autonomous, solar-powered cleaning robot capable of detecting, retrieving, and storing trash in outdoor public spaces with minimal human intervention. The system is built to be entirely self-sustaining by leveraging photovoltaic (solar) energy, eliminating dependence on external power infrastructure and enabling extended operation. This project matters because it targets a recurring, labor-intensive problem that affects residents, pedestrians, and facility managers in both residential and urban commercial environments.
Zpotless solves the debris collection problem by integrating computer vision, proximity sensing, and a servo-actuated mechanical arm to identify and pick up objects up to 100g and deposit them into an onboard storage bin. A 20W solar panel paired with a 12V 10Ah LiFePOâ‚„ battery and a solar charge controller forms the self-sustaining power system, with an active light-tracking algorithm dynamically orienting the robot toward the sun to maximize energy harvesting. The robot's firmware is developed using Arduino IDE, with KiCad handling electrical schematics and MATLAB used to model the power budget and simulate motor torque requirements. Navigation and obstacle-avoidance logic runs through an offboard computer vision pipeline transmitted via Wi-Fi (IEEE 802.11n) from the onboard ESP32 microcontroller, enabling real-time path planning around pedestrians and urban obstacles.
During the Fall Quarter, the team successfully translated project requirements into concrete engineering specifications, procured core components (including the ESP32, DC gear motors, and charge controller), and validated critical subsystems through breadboard prototyping. The servo-actuated pick-up arm achieved a 90% success rate in grasping and depositing rigid cylindrical debris such as plastic bottles and soda cans within the 100g payload limit. The solar power system demonstrated measurable improvements in energy harvesting efficiency after implementing the active light-tracking algorithm, reducing the need for manual battery recharges and increasing autonomous operational time. The object-avoidance system reliably triggered motor halts or rerouting when obstacles entered a 1-meter safety radius under field test conditions, validating the vision-based navigation approach for future full system integration.