AI Glossary / Reinforcement learning and robotics / Intermediate
Robot navigation
Definition
Robot navigation is the process of guiding a mobile robot through an environment to reach a destination or accomplish a movement task. It combines information about the robot's location, surrounding space, feasible routes, and motion constraints. Successful navigation requires executing and updating decisions as conditions change, rather than merely drawing a path on a map.
What it means
A robot needs answers to several connected questions: where am I, where can I move, where should I go, and how should I move next? Localization estimates position and orientation. Environmental representation describes relevant space and obstacles. Planning chooses a route or path, while control turns that plan into motion.
Navigation may use a pre-existing map, build a map while moving, or rely more directly on local observations. It does not inherently require reinforcement learning or a language model. The appropriate methods depend on the robot, its sensors, and the operating environment.
How it works
Sensors provide measurements such as ranges, images, wheel motion, or inertial signals. State estimation combines suitable information to track the robot's pose and motion. Odometry supports smooth local tracking but can drift, while localization against external references can correct accumulated error.
A planner evaluates possible routes or motions using the available environmental model and task objective. A controller then generates commands consistent with the robot's capabilities and the local situation. The robot's physical footprint matters: a route that is clear for a point may be too narrow for the actual body or load.
Navigation is a feedback process. New observations can reveal a blocked corridor, a moved obstacle, or an uncertain position estimate. The system may slow, stop, replan, or request assistance according to its design. Arriving near a goal may also require a specified orientation or docking condition.
Performance depends on the whole chain. A good planner cannot compensate for every sensing error, stale map, or incorrectly calibrated motion model. Tests should therefore examine localization quality, path feasibility, obstacle response, and task completion together.
Three practical examples
- Indoor delivery: move between pickup and drop-off locations while responding to corridor changes.
- Inspection: follow a route through an industrial site and reach designated observation positions.
- Cleaning: cover an accessible area while accounting for the robot's size and remaining reachable space.
Think of it this way
Think of traveling with a map, awareness of your surroundings, and control of your own movement. The map suggests a route, but you still need to know where you are and respond when a doorway is blocked. A robot must perform corresponding functions through sensors, estimates, planning, and control.
Worked example
Consider a fictional mobile robot choosing between two indoor routes. Route A is 18 meters long and permits travel at 0.60 meters per second. Route B is 24 meters long and permits 0.80 meters per second. Ignoring acceleration and other delays, both take 30 seconds: 18/0.60 = 30 and 24/0.80 = 30.
Route A includes two doors, each with a specified expected wait of 5 seconds. Route B has no door delay. Under this simple expected-time model, A takes 40 seconds and B takes 30. The shortest route is therefore not the quickest under the stated assumptions.
Now check geometric feasibility. The robot and its carried load occupy a width of 0.70 meters. The planning rule requires 0.15 meters of clearance on each side, giving a required straight-passage width of 0.70 + 2 times 0.15 = 1.00 meter. A doorway on A is only 0.95 meters wide, so A fails this rule regardless of its time estimate. B's narrowest straight passage is 1.20 meters wide and passes this simplified check.
During travel on B, the robot detects an obstacle 1.00 meter ahead along its motion direction. At 0.80 meters per second, a simplified stopping model with 0.20 seconds of reaction delay and constant deceleration of 0.80 meters per second squared gives stopping distance 0.80 times 0.20 + 0.80 squared divided by (2 times 0.80) = 0.56 meters.
Adding a hypothetical 0.25-metre allowance gives 0.81 meters, below the observed 1.00 meter. This arithmetic illustrates how motion speed and delay affect a navigation decision. It is not a validated stopping specification: real deployment requires measured braking, sensing uncertainty, load effects, surface conditions, and appropriate protective measures. Turning geometry and orientation also require checks beyond the straight-passage calculation.
Illustrative case study
Fictional case: OakDelivery passes route-planning tests in a static office map but repeatedly stops after furniture is rearranged. Investigation shows that local obstacle observations reach the controller late, while the global route continues to use outdated clear-space assumptions.
The team tests the complete navigation pipeline under deliberate layout changes, including time synchronization and replanning behavior. It measures completed trips, intervention frequency, and delays rather than reporting only planned path length. This invented case illustrates integration testing and makes no claim about an actual product.
Common misconceptions and mistakes
Common mistakes include equating navigation with GPS, assuming a map is always current, and evaluating paths without the robot's footprint or motion limits. Reaching the correct coordinates may still be insufficient when the task requires a particular orientation. A successful simulation run also does not establish reliable behavior under different lighting, floor friction, or sensor conditions.
Business use
Businesses should evaluate navigation in the actual kinds of spaces and disruptions the robot will encounter. Useful measures include task-completion rate, travel-time distribution, assistance requests, blocked-route recovery, and availability. These measures help distinguish a compelling demonstration from a system that consistently supports the intended workflow.
People also ask
Does navigation require a map?
Many systems use one, but navigation approaches vary.
Is path planning enough?
No. The robot must estimate its state, execute motion, and respond to new information.
Can a longer route be better?
Yes, if it offers lower delay, better clearance, or other required operating conditions.
Check your understanding
Why does Route B win the initial time comparison?
Answer: Route A adds ten seconds of expected door delay.
What width does the example's clearance rule require?
Answer: One meter.
Why is the stopping calculation only illustrative?
Answer: It omits real sensing, braking, and environmental uncertainties.
Related terms
- Localization
- Odometry
- Path planning
- Costmap
- mapping
- motion control
- robot footprint
- obstacle avoidance
- SLAM
- replanning
Learn next
Next, follow one simulated trip through pose estimation, planning, and control. Introduce a blocked passage and inspect how each component responds before assessing the final task outcome.
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Sources
Nav2 documentation, Navigation Servers: https://docs.nav2.org/rolling/getting_started/navigation_concepts/navigation_servers/ Nav2 documentation, State Estimation: https://docs.nav2.org/rolling/getting_started/navigation_concepts/state_estimation/
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