Mobile Robotics Module Guide

Meet the Loop

Mobile robotics turns an IoT system into a participant in the physical world. A sensor reading no longer ends at a dashboard: it changes an estimate, the estimate changes a command, and the command changes what the sensors observe next. That closed perception-action loop is why a robot can look convincing for ten seconds yet fail at the first slippery floor, blocked corridor, delayed packet, or disagreeing teammate.

This module teaches you to reason through that loop rather than memorise a gallery of robots. You will begin with motion models and coordinate frames, decide what a platform can physically command, then compare the controller architectures that turn estimates into behaviour. The final chapter adds other robots and asks the harder question: which observations may be shared, combined, or trusted without quietly counting the same evidence twice?

Keep one small mission in mind as you read: a differential-drive inspection robot must leave a charging point, follow a corridor, avoid a temporary obstacle, and report its pose to a partner. Each chapter gives you a new part of the argument needed to make that mission credible.

Learning Objectives

By the end of the route, you should be able to trace a robot from sensor observation to state estimate, feasible motion, actuator command, and fresh observation. You should be able to distinguish differential-drive, car-like, omnidirectional, and legged motion constraints; translate wheel speeds into body motion and a desired body motion back into feasible wheel commands; and explain why open-loop motion becomes unreliable when the environment pushes back.

You should also be able to place reactive, deliberative, hybrid, subsumption, potential-field, and motor-schema control on a common map of latency and internal representation. For a robot team, you should be ready to describe the communication topology, preserve uncertainty and dependency history during collaborative estimation, and select flocking, task allocation, formation, or consensus only after naming the coordination problem.

Before You Start

You do not need a robotics kit or advanced control course. You do need a working feel for sensors, actuators, feedback, and coordinate axes. If PWM, motor load, or closed-loop control is unfamiliar, first visit PWM Control and PID Control Theory. For the shortest bridge from a sensor directly to motion, study Braitenberg Vehicles; it gives you a complete reactive loop that the architecture chapter will later place in context.

Basic algebra and trigonometry are enough for the main route. When a rotation matrix or state update appears, read it as a contract between coordinate frames before worrying about symbolic manipulation. If probability and uncertainty are new, keep Kalman Filtering nearby for the final chapter. The boundary is deliberate: this module explains how robotics decisions fit together, not how to derive every estimator from first principles.

A Worked Route Through the Module

First, make one robot's motion honest

Open Mobile Robot Kinematics and the Perception-Action Loop with the inspection robot at its charger. Begin by separating proprioceptive evidence, such as wheel encoders and motor current, from exteroceptive evidence, such as range or vision. Then identify the platform's degrees of freedom and the controls it actually possesses. A differential-drive base can command forward speed and yaw rate; it cannot slide sideways merely because a planner draws a sideways arrow.

Follow the worked kinematics until left and right wheel speeds become a body twist and then a pose change in the world frame. Pause at inverse kinematics and ask whether every requested motion is feasible. The chapter's motor-control simulator belongs here: use it to expose the gap between an ideal wheel-speed request and what inertia, load, saturation, and the actuator can deliver. Finish the chapter only when you can say where feedback closes the loop and which measurement would reveal drift.

Next, decide where behaviour comes from

Continue to Classical Control Architectures for Mobile Robots. Give the inspection robot two simultaneous demands: keep moving toward its waypoint and avoid a trolley left in the corridor. Read the architecture map as a set of responsibility and latency choices. A deliberative controller can reason over a richer model but pays for it in sensing and planning delay; a reactive controller responds quickly but may have little memory of why it chose the turn.

Build the avoid-obstacle and seek-goal behaviours separately, then compare potential fields, motor schemas, and subsumption as different answers to arbitration. Use the PID interactive as a scale check, not as a substitute for the chapter: PID closes a numerical process loop, while these architectures decide which behaviour or objective owns the next command. The interrupt-to-actuator lab then makes the route physical by asking you to measure the delay and evidence path from an event to a safe actuator response.

Finally, add a teammate without duplicating confidence

Take Multi-Robot Systems: Taxonomy, Estimation, and Coordination only after the single-robot loop is stable. Add a second inspection robot and name the team along three independent axes: composition, communication, and control. Draw who can exchange observations with whom. A central coordinator, neighbour-to-neighbour graph, and intermittent encounter network do not merely move packets differently; they create different failure and dependency risks.

Step through the particle-filter and Kalman-filter interactives while tracking which robot originally contributed each observation. That habit prevents two robots from exchanging a shared estimate and later treating it as independent evidence. End with the flocking and consensus examples, but resist choosing an algorithm by reputation. First state whether the team must agree on a value, maintain a formation, allocate tasks, or share a map; then select the update rule and the evidence that would show convergence or failure.

Labs and Interactives

Treat the interactives as checkpoints inside the route, not entertainment saved for the end. In the first chapter, change motor and load conditions in the motor-control simulator before committing to a kinematic command. In the second, drive the PID loop immediately before the interrupt-to-actuator lab so you can separate process feedback from behaviour arbitration. In the team chapter, use both estimation animations on the same imagined observation exchange and narrate what is predicted, what is measured, and what correlation must be preserved.

Each chapter also contains three short knowledge checks. Answer them before expanding the feedback, then return to the nearby diagram or worked trace when the answer surprises you. A useful lab notebook records the mission state, requested command, actual observation, controller decision, latency, and failure response. That one record links all three chapters better than screenshots of a robot merely completing the happy path.

Quick Route Check

Related Practice

To deepen the physical side, continue with Actuator Labs and compare the measured motor response with the kinematic model you assumed. To deepen reactive control, revisit Braitenberg Transfer Functions and predict how a small mapping change alters emergent motion. For an aerial application of the same ideas, pair UAV Trajectory Control with UAV Swarm Coordination and identify which constraints are platform-specific and which team contracts survive the change.

When you are ready to design rather than only study, write a one-page mission review for the inspection robot: coordinate frames, controllable motions, sensor evidence, local controller, arbitration rule, team topology, estimator ownership, safe-stop behaviour, and one failure test. If every line points to something measurable, the robot has become an engineering system rather than a moving demo. Return to Browse Modules when you want the wider course route.