Controle de escorregamento baseado em modelo neuromimético embarcado adaptativo

Abstract

The ability to dexterously manipulate objects is a fundamental human skill based on fast reflex loops and tactile feedback. In upper-limb amputees, the lack of sensory perception in commercial prostheses is one of the leading causes of user rejection and device abandonment, with clinical abandonment rates reaching up to 30%. Mimicking the motor grasp reflex in real time (latency below 100 ms) is a major challenge in rehabilitation robotics due to the high computational cost of conventional methods and constraints of wearable hardware. This thesis proposes and validates the hypothesis that an embedded, bio-inspired event-driven architecture based on neuromorphic coding and fed by a high-density piezoresistive tactile array can detect and suppress slip locally and autonomously within timeframes compatible with the human reflex arc. The developed system integrates a 4 × 4 taxel piezoresistive sensor array covered by a 2 mm layer of EcoFlex 00-30 silicone on the fingertips of a commercial robotic prosthesis (COVVI Hand) coupled to an industrial manipulator (DOBOT CR10A). Analog pressure readings are locally converted into action potentials (spikes) using the Izhikevich neuron model to emulate slowly adapting (SA-I) and fast-adapting (FA-I) mechanoreceptors. Active slip suppression is executed using a Monotonic Proportional-Integral (MPI) controller operating at 500 Hz. Under abrupt perturbation (Experiment A: 20 g step load), the average suppression time was 70 ± 22 ms (FA-I) and 80 ± 25 ms (SA-I). Under progressive perturbation (Experiment B: water flow at 50 ml/s), the reaction times were 82 ± 28 ms (FA-I) and 95 ± 34 ms (SA-I). The main finding of this work is the demonstration that local event-driven processing successfully circumvents the non-linearities and hysteresis of the silicone, enabling low-cost microcontrollers to run fast and robust closed-loop reflex controls. In conclusion, the proposed embedded neuromorphic architecture enables energy-efficient wearable solutions, which are essential for real functional upper-limb rehabilitation.