Real-Time Locomotion Process Control in a Biomimetic Quadruped Robot Using Sensor Fusion and Hierarchical State-Machine Architecture

Introduction: Biomimetic quadruped robots replicating mammalian locomotion are emerging as autonomous platforms for process monitoring and inspection in unstructured environments. Reproducing the multi-gait, dynamically stable movement of the red fox (Vulpes vulpes) requires a tightly integrated control architecture capable of real-time sensing, adaptive gait switching, and obstacle avoidance within embedded hardware constraints.

Methods: The mechanical platform consists of FDM 3D-printed components (PLA/ABS, 0.15 mm layer height) with embedded cable routing, assembled around eight SG90 servo actuators—two per limb—driven by an Arduino Uno microcontroller. Joint-angle profiles for the knee (110–130°), hip (90–100°), and shoulder (~120°) were derived from biomechanical analysis of Vulpes vulpes locomotion. The control software implements four hierarchical layers: a hardware abstraction layer for PWM servo control; a sensor acquisition layer processing dual HC-SR04 ultrasonic rangefinders and a photoresistor (LDR); a proximity-based perception and planning layer; and a behavioural decision layer encoding four hierarchical finite-state machine (HFSM) states—stand, walk, trot, and obstacle avoidance—with sensor-triggered transitions. Pseudo-parallel limb motion is achieved through 1° incremental servo stepping at 15 ms intervals.

Results: Evaluation on a 4×6 m multi-surface arena using an OptiTrack motion-capture system (12 cameras, 120 Hz) over 100 standardised cycles yielded: positioning accuracy ±2 cm (hard surface) and ±5 cm (loose substrate); end-to-end sensor response latency 120 ms; obstacle-avoidance success rate 98%; peak gait-control efficiency 68% in trot mode; autonomous runtime 3 h per charge; and lateral disturbance tolerance up to 3 N. The system satisfies 83% of design targets.

Conclusion: A biologically-grounded hierarchical control architecture produces stable, energy-efficient locomotion on a low-cost microcontroller platform and provides an extensible foundation for future integration of inertial sensing, computer vision, and machine-learning gait adaptation, with direct applicability to autonomous process-monitoring systems in terrain-variable environments.