What is the Mobile Robot Navigation multi-solver?

Mobile Robot Navigation is a guided workflow that chains 3 individual PolySim solvers into one end-to-end analysis, piping each result into the next step.

Yes. Every step runs entirely in your browser using real numerics — no install, no account, no cloud cost. Custom or private solver packs are available as a paid service.

Controls & Robotics Pack · Multi-Solver

Mobile Robot Navigation

Drive, plan, estimate. This multi-solver chains 3 solvers into a single guided workflow — run each step in order and carry the result forward.

Workflow steps

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Differential Drive

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Differential Drive RobotLive

wheel speeds → path

Controls

Presets

Left wheel speed

Right wheel speed

Speed1×

A differential-drive robot steers by spinning its two wheels at different speeds — like a tank. Equal speeds go straight, a difference curves the path, and opposite speeds spin in place. Its forward speed is the average of the wheels and its turn rate is their difference over the wheelbase.

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Data Inspector

Forward v2.50

Turn rate ω0.030

Turn radius83

Governing equation

v = \frac{v _{r} + v _{l}}{2} = 2.50, ω = \frac{v _{r} - v _{l}}{L} = 0.030

Reading this result: The faster right wheel pushes the robot into an arc that curves toward the slower left side, with radius set by how close the two speeds are.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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RRT Path Planning

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RRT Path PlanningLive

rapidly-exploring random tree

Controls

Presets

Step size

A Rapidly-exploring Random Tree grows toward random points in free space, quickly filling the map and snaking around obstacles to connect start (blue) to goal (yellow). It is a cornerstone of motion planning for robot arms, self-driving cars, and drones — fast even in high dimensions where grid search fails.

▶ Run in Python

Data Inspector

Tree nodes0

Path foundno

Path length0

Governing equation

x_{n e w} = x_{n e a r} + ε \frac{x _{r an d} - x _{n e a r}}{∥ x _{r an d} - x _{n e a r} ∥}, ε = 18, N = 0

Reading this result: No path yet at step 18 — the tree ran out of iterations before reaching the goal. Try Replan or a smaller step.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Kalman Filter

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Kalman FilterLive

optimal state estimation

Controls

Presets

Measurement noise

Process noise

The Kalman filter fuses a motion model with noisy measurements to track a hidden state optimally. Each step it predicts, then corrects using the Kalman gain — trusting the measurement more when the model is uncertain, and vice versa. It smooths the jittery sensor (gray) into a clean estimate (cyan) that hugs the truth. The math behind GPS, radar, and spacecraft navigation.

▶ Run in Python

Data Inspector

Measurement RMSE0.0

Kalman RMSE0.0

Noise reductionNaN%

Governing equation

\overset{x}{^} \leftarrow \overset{x}{^} + K (z - H \overset{x}{^}), K = \frac{P H ^{T}}{H P H ^{T} + R}, R = 900, Q = 1.00

Reading this result: Measurement noise dwarfs process noise, so the Kalman gain stays small — the filter leans on its motion model and smooths the jitter hard.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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More in the Controls & Robotics pack

Robot Arm Controller Drone Autopilot Cart-Pole Control Design Servo Tuning Kit DSP Pipeline State Estimator Motion Profile Generator Vibration Control

Frequently asked questions

What is the Mobile Robot Navigation multi-solver?: Mobile Robot Navigation is a guided workflow that chains 3 individual PolySim solvers into one end-to-end analysis, piping each result into the next step.
Is it free to use?: Yes. Every step runs entirely in your browser using real numerics — no install, no account, no cloud cost. Custom or private solver packs are available as a paid service.