5 Robotics Projects for Beginners (No Hardware Needed)

One of the biggest barriers to learning robotics is the assumption that you need expensive hardware to get started. You do not. Modern simulators are good enough that you can learn and practice nearly every fundamental robotics concept without spending a cent on motors, sensors, or microcontrollers.

Here are five projects you can build today using only your laptop. Each one teaches a core robotics skill, and they are ordered from easiest to most challenging.

Project 1: Line-Following Robot

Difficulty: Beginner | Time: 1-2 hours | Key Concept: Sensor-based reactive control

What You Will Build

A differential-drive robot that follows a colored line on the ground using simulated color sensors. The robot reads sensor values on its left and right sides, then adjusts its wheel speeds to stay centered on the line.

What You Will Learn

• How differential drive steering works (varying left and right wheel speeds)
• Reactive control: reading a sensor and immediately computing a motor command
• The basics of proportional control (your first taste of PID)

How to Build It

You can build this directly in the Learn Robotics Playground. The playground provides a 2D environment with a differential drive robot and configurable ground patterns, including lines and curves.

Start simple: use just a proportional controller that turns left when the right sensor sees the line and turns right when the left sensor sees it. Once that works, add a derivative term to smooth out the steering and handle sharp curves.

def follow_line(left_sensor, right_sensor):
    error = left_sensor - right_sensor
    steering = Kp * error
    left_wheel = base_speed - steering
    right_wheel = base_speed + steering
    return left_wheel, right_wheel

Challenge extension: Make the robot handle intersections by adding logic to choose a direction when both sensors see the line.

Project 2: SLAM with Simulated LiDAR

Difficulty: Intermediate | Time: 4-6 hours | Key Concept: Simultaneous Localization and Mapping

What You Will Build

A robot that explores an unknown environment, builds a 2D occupancy grid map, and tracks its own position within that map -- all using simulated LiDAR data.

What You Will Learn

• How LiDAR sensors work (measuring distance with laser beams at multiple angles)
• Occupancy grid mapping: converting range measurements into a grid of free, occupied, and unknown cells
• Scan matching: aligning consecutive LiDAR scans to estimate how the robot moved
• The core SLAM loop: predict motion, observe the environment, update the map and pose estimate

How to Build It

Use any 2D robotics simulator that provides LiDAR simulation. Gazebo (with a 2D LiDAR plugin), Webots, or our playground all work well.

Start with mapping only -- assume the robot knows its exact position (ground truth) and just build the occupancy grid from LiDAR scans. Once that works, remove the ground truth position and implement a simple scan matcher (like ICP) to estimate the robot's movement from consecutive scans.

The key data structure is the occupancy grid:

class OccupancyGrid:
    def __init__(self, width, height, resolution):
        self.grid = [[0.5] * width for _ in range(height)]
        self.resolution = resolution  # meters per cell

    def update(self, robot_x, robot_y, scan):
        for angle, distance in scan:
            # Trace a ray from robot to hit point
            # Mark cells along the ray as free
            # Mark the hit cell as occupied
            self.trace_ray(robot_x, robot_y, angle, distance)

Challenge extension: Implement loop closure detection -- when the robot revisits a previously mapped area, correct the accumulated drift in its position estimate.

Project 3: PID-Tuned Balancing Robot

Difficulty: Intermediate | Time: 3-5 hours | Key Concept: Feedback control for an unstable system

What You Will Build

A simulated two-wheeled balancing robot (like a Segway) that stays upright using a PID controller. The robot reads its tilt angle from a simulated IMU and adjusts its wheel speed to maintain balance.

What You Will Learn

• Controlling an inherently unstable system (the robot falls over without active control)
• The critical importance of PID tuning -- bad gains mean the robot falls immediately
• How sample rate affects control quality (try running the controller at 10 Hz vs 100 Hz vs 1000 Hz)
• Cascaded control: using an inner loop for balance and an outer loop for position/velocity

How to Build It

This project requires a physics simulator that models rigid body dynamics. Webots, PyBullet, or MuJoCo all work well for this. You need a robot model with two wheels and a tall body that can tip over.

The core control loop is straightforward:

while running:
    tilt_angle = read_imu()
    control_signal = pid.compute(
        setpoint=0.0,          # target: perfectly upright
        measured=tilt_angle,
        dt=loop_period
    )
    set_wheel_speed(control_signal)

The challenge is tuning. Start with proportional-only control and increase Kp until the robot oscillates. Then add Kd to dampen the oscillations. Only add Ki if there is a persistent lean.

Challenge extension: Add a velocity controller on top of the balance controller. The outer loop sets a target tilt angle (leaning slightly forward to accelerate), and the inner loop maintains that tilt angle.

Project 4: Path Planning Visualizer

Difficulty: Beginner-Intermediate | Time: 3-4 hours | Key Concept: Graph search and sampling-based planning

What You Will Build

An interactive tool that visualizes how different path planning algorithms find routes through obstacle-filled environments. You will implement at least three algorithms and watch them search in real time.

What You Will Learn

• A search:* How heuristic-guided graph search finds optimal paths efficiently
• Dijkstra's algorithm: The foundation of graph-based planning (A* without the heuristic)
• RRT (Rapidly-exploring Random Trees): How sampling-based planners explore high-dimensional spaces
• The tradeoffs between optimal and probabilistically complete planners

How to Build It

You can build the visualizer in the Learn Robotics Playground, which provides a grid environment with placeable obstacles, or create a standalone app using Python with pygame or matplotlib.

The most educational part is visualizing the search process, not just the final path. Color-code cells or nodes as they are explored so you can see how each algorithm behaves:

• Dijkstra expands in a uniform circle outward from the start.
• A* expands in an ellipse biased toward the goal.
• RRT grows a tree of random samples that rapidly covers the space.

def a_star(grid, start, goal):
    open_set = PriorityQueue()
    open_set.put((0, start))
    came_from = {}
    cost_so_far = {start: 0}

    while not open_set.empty():
        _, current = open_set.get()
        if current == goal:
            return reconstruct_path(came_from, start, goal)

        for neighbor in get_neighbors(grid, current):
            new_cost = cost_so_far[current] + 1
            if neighbor not in cost_so_far or new_cost < cost_so_far[neighbor]:
                cost_so_far[neighbor] = new_cost
                priority = new_cost + heuristic(neighbor, goal)
                open_set.put((priority, neighbor))
                came_from[neighbor] = current
                visualize(neighbor, "explored")  # show the search

Challenge extension: Implement D Lite* or Theta* and compare their paths to A*. Or add dynamic obstacles that appear after planning and require replanning.

Project 5: Multi-Robot Formation Control

Difficulty: Advanced Beginner | Time: 4-6 hours | Key Concept: Multi-agent coordination

What You Will Build

A team of three to five simulated robots that maintain a geometric formation (triangle, line, diamond) while moving through an environment. Each robot knows only its own position and the positions of its neighbors.

What You Will Learn

• Decentralized control: Each robot runs the same algorithm independently, with no central coordinator
• Graph-based formation: Defining desired inter-robot distances and using a virtual structure
• Consensus algorithms: How robots agree on a shared heading and speed
• Collision avoidance: Preventing robots from crashing into each other while maintaining formation

How to Build It

Use any multi-robot simulator. Gazebo, Webots, or CoppeliaSim all support multiple robots out of the box. For a simpler setup, use Python with a custom 2D simulator or a multi-agent framework.

The formation controller for each robot computes a velocity that balances two objectives:

def formation_control(my_position, neighbor_positions, desired_distances):
    formation_force = [0.0, 0.0]

    for i, neighbor in enumerate(neighbor_positions):
        # Vector from me to neighbor
        dx = neighbor[0] - my_position[0]
        dy = neighbor[1] - my_position[1]
        actual_distance = math.sqrt(dx * dx + dy * dy)

        # Error: difference between actual and desired distance
        distance_error = actual_distance - desired_distances[i]

        # Attract if too far, repel if too close
        if actual_distance > 0:
            formation_force[0] += (dx / actual_distance) * distance_error
            formation_force[1] += (dy / actual_distance) * distance_error

    return formation_force

Each robot adjusts its velocity to maintain the correct distance from its neighbors. The formation emerges from these local interactions -- no robot needs to know the global plan.

Challenge extension: Add obstacle avoidance that temporarily breaks and reforms the formation. Or implement leader-follower behavior where one robot leads and the others maintain formation relative to it.

Getting Started

Pick the project that matches your current skill level and interests. Projects 1 and 4 are great starting points if you are new to robotics -- and both work directly in the Learn Robotics Playground with no installation required.

The most important thing is to actually build something. Reading about robotics is useful, but writing the code and watching a simulated robot execute your algorithm is where the real learning happens.

If you get stuck, check out the relevant module in our lessons section. Each project maps directly to concepts covered in our curriculum:

Happy building.