What's the difference between path planning and kinematics?

Kinematics answers how a robot reaches one specific point: given a target pose, what joint angles or wheel motions get the end effector or base there. Path planning answers a different question one layer up: given a start and a goal in an environment full of obstacles, what sequence of intermediate points should the robot pass through to get there safely and efficiently? A path planner produces a list of waypoints or a trajectory; inverse kinematics is then called on each of those points to figure out how to physically move the robot to it.

Is A* or RRT better for robot path planning?

Neither is universally better — they suit different situations. A* (and grid-search methods generally) work well in lower-dimensional, well-discretized spaces like a 2D warehouse floor and guarantee the shortest path on the grid they search. RRT and its variants scale much better to high-dimensional configuration spaces, such as a 6-axis arm's joint space, where a full grid would be computationally impossible, but the raw path they find is usually not optimal and typically needs a smoothing step afterward.

What is configuration space and why not just plan in the robot's physical workspace?

Configuration space (C-space) represents every possible state of the robot — for an arm, one axis per joint angle; for a mobile robot, its x, y position and heading — as a single point, so that planning becomes a search for a path between two points in that space. Planning directly in physical 3D workspace is awkward because a robot's reachability and collision constraints depend on its joint angles, not just the position of its tip; C-space folds the robot's own geometry into the obstacles, so a valid path in C-space is guaranteed collision-free without having to re-check the robot's shape at every step.

Do potential field methods actually get used in real robots?

Yes, but usually for local, reactive obstacle avoidance rather than as the sole global planner. Potential fields are cheap to compute and react quickly to sensor data, which makes them well suited to handling a person or forklift that suddenly appears in a mobile robot's path. Because they can get stuck in local minima and don't reason globally about the environment, most production systems pair a potential-field-style local planner with a global planner (such as A* or RRT) that has already worked out a sound overall route.

Engineering·9 min read·July 12, 2026

🧭 Path Planning & Motion Algorithms for Robots

How robots decide the actual sequence of points to move through — configuration space, A*, RRT, and potential fields explained conceptually, and how path planning differs from kinematics.

📖Robotics & Automation Handbook (Full Access)

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Every industrial robot deployment eventually runs into a question that kinematics alone cannot answer. Inverse kinematics, covered in our industrial robot kinematics guide, tells the controller how to reach one specific point: given a target position and orientation, what joint angles get the end effector there. But a robot rarely moves to just one point in isolation. It has to get from where it is now to where it needs to be next, across a work cell full of fixtures, conveyors, other machinery, and sometimes people — without colliding with any of it. Deciding the actual sequence of intermediate points the robot should pass through to make that trip safely and efficiently is a separate problem called path planning (often used interchangeably with the broader term motion planning, which also accounts for velocity, acceleration, and dynamic constraints along the path). Kinematics answers "how do I reach this point"; path planning answers "which points should I reach, in what order, to get across this environment."

Why Path Planning Is a Distinct Problem

Consider a 6-axis arm reaching into a machine tending cell to pick a part, then placing it on a conveyor a meter away. Naively commanding the controller to move directly from the pick pose to the place pose — a straight-line interpolation in joint space or Cartesian space — might send the arm straight through the machine's door frame, a safety fence, or a fixture sitting between the two points. The robot's inverse kinematics would happily compute a valid joint solution for the destination pose; nothing in that calculation knows or cares that the straight-line path to get there is blocked. Path planning exists specifically to solve that gap: it searches for a route through the space of possible robot configurations that avoids known obstacles, and hands the resulting sequence of waypoints to the motion controller, which then relies on kinematics to execute each individual point along that route. In mobile robotics the same logic applies at a larger scale — an automated guided vehicle (AGV) or autonomous mobile robot (AMR) navigating a warehouse floor needs a route around shelving, parked equipment, and other traffic, not just a bearing and a distance to its destination.

Topics covered

path planningmotion planningconfiguration spaceA* algorithmRRTrapidly-exploring random treepotential field methodobstacle avoidancerobot navigationcollision-free pathNav2MoveItmotion algorithmsrobot trajectorymobile robot navigationC-space