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Tesis de Grado · UFC

Diseño e Implementación de Vehículo Teleoperado con Transmisión Simulada y Force Feedback

Tesis de Grado en Ingeniería en Computación — Universidad Federal de Ceará

Autor
Inácio Rodrigues de Matos Galvão
Director
Prof. Dr. José Marques Soares
Codirector
Ing. Mtr. Artur Rodrigues Rocha Neto
Código fuente en GitHubPóster académico

Hipótesis

UDP simple sobre Wi-Fi doméstico, combinado con force feedback calculado desde una IMU embebida, es suficiente para teleoperación responsiva en hardware de bajo costo.

Objetivo

Diseñar, implementar y validar una arquitectura de teleoperación para vehículo a escala 1:5 estilo Fórmula 1, evaluando latencia, pérdida de paquetes, fidelidad del modelo del motor, respuesta háptica, autonomía y comportamiento térmico.

Contexto y motivación

Los sistemas de teleoperación son relevantes en minería, exploración espacial y operaciones militares — donde latencia y retorno háptico determinan la precisión de la tarea. Las soluciones comerciales existentes cuestan más de R$ 50.000 y son complejas. Este trabajo demuestra que arquitectura equivalente es viable en hardware abierto de R$ 1.300.

Prototipo F1 a escala 1:5 utilizado en las 13 sesiones experimentales.
Prototipo F1 a escala 1:5 utilizado en las 13 sesiones experimentales.

Arquitectura

Tres capas: vehículo embebido (Raspberry Pi 4, cámara, IMU, motor, servos), cliente PC (decodifica video MJPEG, calcula force feedback, muestra telemetría) y simulador Logitech G923. Comunicación vía tres puertos UDP independientes — video, telemetría y comandos — para que frames de video no bloqueen los 100 Hz exigidos por el retorno háptico.

Arquitectura de tres capas y tres puertos UDP independientes (video, telemetría, comandos).
Arquitectura de tres capas y tres puertos UDP independientes (video, telemetría, comandos).

Stack y hardware

Embebido (vehículo)

Raspberry Pi 4 (8 GB) · Cámara OV5647 5MP CSI · BMI160 IMU · Motor DC 775 · Puente H BTS7960 · 3× Servo MG996R · PCA9685 · Python

Cliente (PC)

Python · OpenCV · Tkinter · evdev · UDP raw

Simulador

Logitech G923 · Volante 900° · Pedales progresivos · Paddle shifters · Force feedback evdev

Monitoreo de energía

Arduino Pro Micro · 2× ACS758 Hall effect · INA219 · Divisor de voltaje 3S LiPo

Modelo del motor

Motor DC 775 con transmisión de cinco marchas modelado como sistema de primer orden G(s) = K / (τ_eff · s + 1), donde τ_eff = τ_base(g) · M_zona. τ_base crece de 2 s (1ª marcha) a 10 s (5ª); M_zona vale 1,0 (IDEAL), 10,0 (SUBÓPTIMA) o 25,0 (POBRE). Operar en marcha equivocada para el rango de RPM penaliza la respuesta, como en una transmisión real.

Zonas de eficiencia por marcha y constantes de tiempo τbase.
Zonas de eficiencia por marcha y constantes de tiempo τbase.

Force feedback (G923)

Siete tipos de efecto en ocho instancias paralelas: condicionales (centrado, amortiguación, fricción, inercia) actualizados a 1 kHz en el firmware del volante, fuerza lateral basada en la G lateral del BMI160 a 100 Hz, topes virtuales en los límites de curso, y vibración de impacto sincronizada con sensores. Caché por efecto redujo el bloqueo de la thread de sensores de 12–60 ms a 0–15 ms por paquete.

Logitech G923 — volante 900°, pedales progresivos y force feedback nativo vía Linux evdev.
Logitech G923 — volante 900°, pedales progresivos y force feedback nativo vía Linux evdev.

Experimentos

13 sesiones experimentales — 9 indoor (ruedas libres, 27±2 °C) y 4 externas en pista de 55,81 m — totalizando ~165 min y ~983 mil muestras agregadas.

Indoor · 9 sessões · 136,5 min
~814 mil muestras
  • S01Motor escalonado 1ª → 3ª18.0 min
  • S02Direção + freio isolados12.0 min
  • S03Resolução × filtros de PDI15.3 min
  • S04Uso representativo completo15.0 min
  • S05Curva de descarga da bateria47.4 min
  • S06Trocas de marcha rápidas4.4 min
  • S07Filtros de PDI combinados7.0 min
  • S08Latência fim a fim5.0 min
  • S09Calibração do BMI16012.4 min
Externa · 4 sessões · 28,1 min
~168 mil muestras
  • S1-extRolamento unidirecional2.0 min
  • S2-extDireto sem rolamento15.7 min
  • S3-extDireto reforçado (falha térmica)5.7 min
  • S4-extValidação do motor retrabalhado4.7 min

Resultados

Todas las metas alcanzadas. Latencia extremo a extremo a 1,1× la meta, con cuello de botella en el Wi-Fi compartido — no en el vehículo. Migrar a enlace dedicado reduciría el p50 en un orden de magnitud sin cambios en el código.

Comunicación UDP
Latência fim a fim (p50 / p99)55,3 / 107,7 ms
Latência interna RPi (p50 / p99)0,22 / 0,40 ms
Perda de pacotes (1080p)0,46 / 0,13 %
Video y sensores
Câmera OV5647 (480p / 1080p)60 / 54 fps
BMI160 — taxa / erro |g|100 Hz / < 1 %
Calibração geométrica BMI160< 0,5°
Violações de budget (10 ms)0 / 983 mil
Force feedback
Latência sensor → atuação (p50 / p99)5,0 / 10,5 ms
Força lateral em pista (pico)39 %
Vibração de impacto (pista)92–96 %
Energía, térmica y mecánica
Consumo médio (indoor / pista)8,8 / 27,0 W
Autonomia projetada90–120 min
Temperatura CPU (média / pico)< 50 °C
Temperatura motor (S1 / S2 / S3)33 / 40 / 56 °C
Distribución acumulada de la latencia extremo a extremo — p50 en 55,3 ms.
Distribución acumulada de la latencia extremo a extremo — p50 en 55,3 ms.

Conclusión

UDP simple + modelo de primer orden + force feedback directo entregan teleoperación responsiva en hardware de bajo costo. La limitación principal fue estructural, no electrónica: chasis en PLA y soportes del motor cedieron en pista. El trabajo valida la hipótesis y abre camino para evoluciones con chasis metálico y enlace de red dedicado.

Referencias bibliográficas

53 referencias citadas en la tesis, agrupadas por tipo. Haz clic en cada categoría para expandir.

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