Robot Path Planning 101: From Algorithms to Real‑World Navigation

Ever wondered how a robot decides whether to take the scenic route through the office carpet or sprint straight across the conference room like a caffeinated squirrel? That decision is made by path planning, the art and science of finding a route from point A to point B while dodging obstacles, obeying constraints, and maybe even dancing a little. In this post we’ll unpack the key algorithms, walk through a real‑world example, and sprinkle in some humor because who says robotics has to be all circuits and no laughs?

What Is Path Planning, Anyway?

Think of path planning as a GPS for robots. But instead of just giving you the next turn, it also checks if your lawn mower can fit through a narrow door, whether your warehouse robot can avoid the freshly spilled coffee cup, and if your autonomous car can obey traffic rules. In short: it’s the robot’s “I’m going to do that, but I won’t crash!”

Why Should You Care?

• Safety: Avoiding collisions keeps humans and robots alive.
• Efficiency: Shorter routes mean less battery drain and faster deliveries.
• Adaptability: Robots can re‑plan on the fly when the world changes.

The Classic Algorithm Toolkit

Below is a quick‑reference table of the most common path planning algorithms. Grab a coffee, you’ll need it.

Algorithm	Use Case	Key Idea
Grid‑Based A*	2D maps, simple robots	Heuristic search on a grid; finds optimal path if cost is additive.
Probabilistic Roadmap (PRM)	High‑dimensional spaces, robots with many joints	Randomly sample configurations; connect them into a graph.
Rapidly‑Exploring Random Tree (RRT)	Real‑time, dynamic environments	Grow a tree from start to goal; good for high‑dimensional spaces.
D* Lite	Changing maps (e.g., moving obstacles)	Incremental A* that updates quickly when the map changes.

Diving Into A* on a Grid

Let’s start simple: imagine a robot vacuum navigating your living room. We’ll use A* because it’s the bread‑and‑butter of path planning.

The Recipe

1. Define the grid: Each cell is either free or occupied.
2. Create a heuristic: Usually Euclidean or Manhattan distance to goal.
3. Initialize: Start node in the open list with cost g=0.
4. Loop:
   1. Pop node with lowest f = g + h.
   2. If it’s the goal, reconstruct path.
   3. For each neighbor: compute tentative g; update if better.

Here’s a quick pseudo‑code snippet:

def astar(grid, start, goal):
  open_set = PriorityQueue()
  open_set.put(start, 0)
  came_from = {}
  g_score = defaultdict(lambda: inf)
  g_score[start] = 0

  while not open_set.empty():
    current = open_set.get()
    if current == goal:
      return reconstruct_path(came_from, current)
    for neighbor in neighbors(current):
      tentative_g = g_score[current] + dist(current, neighbor)
      if tentative_g < g_score[neighbor]:
        came_from[neighbor] = current
        g_score[neighbor] = tentative_g
        f = tentative_g + heuristic(neighbor, goal)
        open_set.put(neighbor, f)
  return None

Why A* Is Great (and Not)

• Pros: Guarantees optimal path on a grid; simple to implement.
• Cons: Can be slow on huge maps; assumes a static environment.

When the World Gets Chaotic: RRT in Action

Suppose our robot vacuum now has a pet cat that decides to jump on the carpet at random intervals. We need an algorithm that can handle dynamic, high‑dimensional spaces: RRT.

The High‑Level Flow

1. Start a tree at the robot’s current configuration.
2. Randomly sample a point in the space.
3. Find the nearest node in the tree to that sample.
4. Steer from the nearest node toward the sample, adding a new node if the path is collision‑free.
5. Repeat until the goal is within a certain radius of any node.

The beauty? It’s embarrassingly parallel—just ask your GPU to try thousands of samples at once!

Real‑World Example: Warehouse Robot

A 10‑meter tall warehouse robot with six joints (think a giant, angry octopus) must pick items from shelves and avoid forklifts. The configuration space is 6‑dimensional. A* would be hopelessly slow, but RRT can quickly carve a feasible path.

We run RRT-Connect, an optimized variant that grows two trees (from start and goal) simultaneously. The resulting path is good enough, even if not the absolute shortest, which is fine when your robot has a generous battery budget.

Optimizing for Real‑World Constraints

Algorithms are only part of the puzzle. Robots live in the messy, unpredictable world.

1️⃣ Sensors & Perception

• LIDAR: Accurate distance measurements; perfect for static obstacles.
• Cameras: Detect dynamic objects like people or pets; require computer vision.
• Ultrasonic: Cheap but noisy; good for short‑range detection.

2️⃣ Motion Constraints

A robot can’t turn on a dime. We model kinodynamic constraints (velocity, acceleration limits) in the planner or post‑process the path with a trajectory optimizer.

3️⃣ Re‑Planning on the Fly

Enter D* Lite. When a new obstacle appears, D* Lite updates the cost map in O(log n) time rather than recomputing from scratch.

Meme‑worthy Moment

Because every great tutorial needs a meme video to illustrate the chaos of real‑world robotics:

Putting It All Together: A Mini‑Project

Let’s build a tiny demo in Python using the ompl library (Open Motion Planning Library). We’ll plan a path for a 2‑DOF robotic arm around a rectangular obstacle.

import ompl.base as ob

import ompl.geometric as og

def main():

  # State space: 2D joint angles

  space = ob.RealVectorStateSpace(2)
  # Set bounds: 0 to pi for both joints

  bounds = ob.RealVectorBounds(2)

  bounds.setLow(0, 0.0)

  bounds.setHigh(0, math.pi)

  bounds.setLow(1, 0.0)

  bounds.setHigh(1, math.pi)

  space.setBounds(bounds)
  # Simple setup

  ss = og.SimpleSetup(space)
  # Start & goal states

  start = ob.StateSpaceType(state)

  start.setX(0.1)

  start.setY(0.2)

  goal = ob.StateSpace