3 Classical Control Architectures for Mobile Robots

3.1 Overview: Put Every Controller on Two Axes

Before applying the specification, inspect the real industrial robot installation below: its package, terminals, scale, and installation context are part of the engineering evidence.

Carry those visible constraints into the surrounding analysis; the abstract symbol or capability name does not capture mounting, wiring, protection, or service access.

You already know one classical robot controller. A Braitenberg vehicle wires sensed intensity to motor response, producing behavior without a map or explicit plan. This chapter does not re-teach that mature treatment. It places Braitenberg’s proximal, reactive design inside a wider architecture map and then compares the paradigms around it.

The source lecture classifies robot control along two axes:

1. Sensing distance: proximal versus distal. A proximal controller operates close to sensor input. A distal controller assembles named behavioral blocks such as avoid-obstacle, follow-wall, or move-to-goal.
2. Planning horizon: reactive versus deliberative. A reactive controller applies time-invariant rules to its current world estimate. A deliberative controller predicts future states and plans an action sequence that minimizes a metric such as collision risk, energy, or path length.

The axes are independent. “Reactive” does not mean “unstructured,” and “distal” does not automatically mean “deliberative.” A motor-schema controller can combine named behaviors at every cycle without planning minutes ahead.

3.2 Practitioner: Build Behaviors, Then Define Their Arbitration

The simplest inspectable reactive controller is a rule set:

if left proximity sensors are active:  turn right
if right proximity sensors are active: turn left
if no proximity sensors are active:    move forward

The pseudocode hides the most important implementation question: what happens if both left and right sensors are active? Production rules need explicit priority, mutual exclusion, timeouts, and a safe default. Otherwise source order becomes accidental arbitration.

3.3 Lab: Interrupt-To-Actuator Feedback Loop

Build the smallest closed loop that exposes every timing and safety boundary. A digital sensor interrupt records an edge and timestamp. The interrupt handler does not drive the motor; it places a compact event in a bounded handoff slot. The control task consumes the newest valid event, computes a bounded command, updates a motor driver or an LED stand-in, and records the input time, decision time, output time, saturation state, and failsafe reason.

// Interrupt top half: no logging, allocation, delay, or motor command.
void sensor_isr(void) {
  pending_edge = true;
  edge_time_us = monotonic_us();
}

// Control bottom half: one explicit owner of actuator state.
void control_step(void) {
  if (sensor_stale() || watchdog_expired() || driver_fault()) {
    motor_command(0);       // neutral / de-energized safe state
    led_alarm(true);
    record_failsafe();
    return;
  }
  command = clamp(kp * (setpoint - measurement), -MAX_CMD, MAX_CMD);
  motor_command(command);
  record_timing(edge_time_us, monotonic_us(), command);
}

Use the LED first so an incorrect output cannot move hardware. Then connect a current-limited motor driver with an independent enable and emergency stop. Inject a stuck-high sensor, missing sensor events, an overrun, a driver fault, and a reset. Every fault must produce the declared safe state without depending on the dashboard, network, or a future sensor update.

The acceptance record contains the wiring diagram, interrupt source and edge policy, debounce rule, control period, command clamp, motor-driver electrical limits, LED stand-in result, four timing markers, watchdog owner, stale threshold, safe output state, and recovery authority. The lab passes only when normal feedback is observable and every injected fault reaches the same bounded failsafe.

3.4 Under the Hood: From Weighted Neurons to Safe Arbitration

A small neural reactive controller maps sensor inputs $I_j$ into each neuron’s activation:

The source example uses $f(x)=\tanh(x)$ and connects eight sensor inputs to two motor outputs. The architecture is proximal: learned or selected weights operate close to readings. It is reactive when the current input vector alone determines the current output.

The transfer function bounds each neuron output, but it does not by itself bound the physical command. A safe deployment still needs output scaling, rate limits, saturation handling, stale-input behavior, and tests at the corners of the sensor domain.

3.5 Summary

Classify a controller in two passes: first locate its distance from raw sensing, then identify how far it reasons into the future. That separation prevents a named behavior from being mistaken for a plan and keeps the engineering review focused on the path from evidence to motor command. The comparison also exposes the real trade: fast local response, inspectable composition, predictive capability, and safe arbitration must fit the same physical timing envelope.

• Proximal versus distal describes distance from raw sensing; reactive versus deliberative describes planning horizon.
• Neural and Braitenberg controllers can create fast proximal reactions, while rules and motor schemas expose named behaviors.
• Potential fields compose attractive and repulsive vectors but can become trapped in local minima.
• Subsumption makes behavioral priority explicit through suppression and inhibition between layers.
• Every architecture needs a physical command envelope, observable arbitration, and tested failure recovery.

3.6 Key Takeaway

3.7 See Also

Continue according to the boundary you need to examine. Revisit kinematics when the issue is whether a requested command is physically feasible, and use the Braitenberg material when the sensor-to-motor transfer itself needs inspection. PID control supplies the actuator-level feedback beneath an architecture, while the multi-robot chapter extends arbitration and evidence flow beyond one platform. Together, these links move outward from individual signals to mechanisms, controllers, and coordinated teams.

• Mobile Robot Kinematics and the Perception-Action Loop — the motion and feedback contract these architectures control.
• Braitenberg Vehicles — a mature proximal/reactive controller with hands-on examples.
• Braitenberg Transfer Functions — how sensor-to-motor mappings change emergent behavior.
• PID Control Theory — classical closed-loop feedback at the actuator/process level.
• Multi-Robot Systems: Taxonomy, Estimation, and Coordination — the next chapter extends control from one robot to a networked team.