Solar Rover Path Planning for Sunlight

ISEF Category: Robotics and Intelligent Machines

Subcategory: Other  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A rover can lose power before it reaches its goal, even when the route looks short on paper. Sunlight changes that. If your robot can spot bright ground and avoid shade, it may travel farther on the same battery. That makes this project part coding challenge, part robotics, and part map reading.

What Is It?

This project asks a simple question with a smart twist: should a solar rover always take the shortest path, or should it take the path with the best sunlight? You will build a planner that uses sun position and a shadow map to estimate where the rover can charge as it moves.

Think of it like hiking. The shortest trail is not always the best one if it drops you into deep shade when you need warmth or light. Your rover faces the same tradeoff. A route with more sun may give the robot more energy, even if the route is a little longer.

The core idea comes from two inputs. First, the sun has a predictable position in the sky at a given time and place. Second, a fisheye phone camera can capture a wide view of the sky and nearby obstacles, so you can map where shade falls on the ground. Your planner can combine those inputs and bias the rover toward brighter regions.

Why This Is a Good Topic

This is a strong science fair topic because you can test a clear tradeoff, distance versus energy gain. You can measure real outcomes such as battery use, mission time, path length, and success rate. The project also connects to solar robotics, outdoor navigation, and autonomy, so the real-world link is easy to explain. You can learn path planning, sensor fusion, mapping, and evaluation with controls.

Research Questions

• How does an energy-aware path planner change rover mission endurance compared with a shortest-path baseline?
• What is the effect of shadow density on the planner’s route choice and battery use?
• Does using live sun-position data improve route decisions compared with using a static sun estimate?
• To what extent does a fisheye shadow map predict the rover’s actual energy gain along a route?
• Which weighting between distance and sunlight produces the best mission success rate?
• How does the planner perform when the environment includes partial shade, moving shadows, or both?

Basic Materials

• Laptop or desktop computer with Python support.
• Phone with a fisheye lens or wide-angle camera app.
• Small solar-powered rover kit or RC car with a solar panel.
• Basic obstacle course materials such as cardboard, tape, and cones.
• Digital multimeter for checking panel output and battery voltage.
• Stop watch or phone timer.
• Notebook or spreadsheet for logging route length, energy, and mission outcome.

Advanced Materials

• University lab or maker space access for rover prototyping.
• Differential drive mobile robot base with encoder feedback.
• Embedded computer such as Raspberry Pi or similar single-board computer.
• IMU or GPS module for outdoor ground truth, if needed.
• Light sensor array or irradiance sensor for validation.
• Data logger for voltage, current, and power measurements.
• Calibration target or marked test field for map alignment.
• Computer with GPU support for vision experiments, if needed.

Software & Tools

• Python: Runs the planner, processes map data, and compares route policies.
• OpenCV: Detects image features and helps build shadow maps from fisheye photos.
• ImageJ: Measures brightness patterns in test images and supports simple calibration checks.
• QGIS: Helps you view and compare outdoor maps, paths, and shaded regions.
• GeoGebra: Lets you sketch route geometry and reason about path length versus coverage.

Experiment Steps

1. Define the exact mission goal, such as reaching a target while maximizing energy reserve, not just minimizing distance.
2. Choose the one planning variable you will change first, such as the weight placed on sunlight versus travel distance.
3. Build a baseline route policy so you can compare your idea against a simple shortest-path rule.
4. Create a shadow representation that turns camera images and sun position into a usable brightness map.
5. Design the test course and controls so each planner sees the same terrain, same time window, and same starting energy.
6. Plan your scoring system before testing, so you can compare mission endurance, path efficiency, and energy gain with the same metrics.

Common Pitfalls

• Using a shadow map that does not match the rover’s ground plane, which makes bright zones appear in the wrong place.
• Ignoring time-of-day changes, which causes the sun-position model to drift during repeated trials.
• Comparing routes on different terrain layouts, which mixes map quality with planner quality.
• Measuring only path length, which hides the energy benefit of a slightly longer but sunnier route.
• Failing to calibrate camera brightness, which makes indoor light, cloud cover, and exposure changes look like real shadow changes.

What Makes This Competitive

A competitive version of this project goes past a simple map demo. You would build a clean comparison between multiple planners, then test them across several lighting conditions and obstacle layouts. Strong entries also use careful validation, such as checking whether the shadow map predicts real power output. If you add uncertainty analysis, ablation tests, or a new weighting strategy, your project starts to look like research, not a class exercise.

Project Variations

• Test the same planner on a rooftop course, where sun angle and reflections change the shadow map.
• Swap the phone camera for a drone or overhead camera, then compare how map resolution changes route quality.
• Replace solar charging with thermal or ambient-light sensing, then see whether the same planning idea still improves endurance.

Learn More

• NASA Sun Position Calculator: Check sun angle and solar geometry tools through NASA’s public education and mission resources.
• NOAA Solar Calculator: Find solar position and daylight data through NOAA’s online solar tools and climate pages.
• USGS EarthExplorer: Access aerial and satellite imagery for building outdoor test maps and land-cover context.
• MIT OpenCourseWare, Introduction to Algorithms: Search the course site for path planning, graph search, and optimization basics.
• IEEE Xplore or PubMed: Search for review articles on solar robot navigation, energy-aware planning, and outdoor autonomous systems.