Human Machine Interaction

Transcription

1 Human Machine Interaction XVIII BrainStorming Cordinator: L. Fortuna Tutor: P. Arena Ignazio Aleo June 09

2 introduction Network of systems Outlines our goals Network of systems development Robot-embodiment through a distributed sensor network Environment cognition and interaction Project analysis roadmap time Electronics (sensors, communication devices, controllers, firmware, ) (Bioloid, miniarm, d00b, 3Fingers, ) Structures Algorithms (MMC, NGPCA, SARSA, ) (simulation environment, control user interfaces, ) Interfaces

3 next Systems integration Meshed communication among different systems wireless environment Cooperation and swarming Distributed systems network (WSN)

4 electronics Platform Architecture elements roadmap MicroController Unit Communication interfaces Storage capability Internal sensors External sensors HW layer ARM Cortex M3 stm32f103 FW layer Ember ZigBee sn260 MEMS accelerometer lis3lv02dl Free RTOS scheduler Fat FS file system - FAT MEMS gyroscope ly530al infrared network wired BUS PC ext ADCs - ZigBee - RS USB infrared MCU µsd - Temperature - Pressure - Gyroscope - 3-axis accelerometer

5 structures Bioloid 18 DoF Humanoid distributed proprioceptive sensory systems Electronic features Smart servos 3-wire bus interface Battery Pack 9.6V NiHM 2700mAh Decentralized sensor system Digital 2 axis magnetometer (torso) Accelerometer 3-axis (torso) Microphone (head) 3 x Infrared (head) CCD Colour camera (head) each servo Voltage Temperature Torque Position Speed Mechanical features Weight Stand up height Motors Maximum joint angular speed 1.9 Kg 35 cm 18 x Dynamixel AX-12+ (16Kg/cm@10V) [s/60 deg]

6 structures miniarm 7DoF serial manipulator (6DoF + gripper) decentralized low level control Electronic features 32-bit embedded control board USB communication interface Smart servos RS 485 bus interface Decentralized sensor system each servo Voltage Temperature Torque Position Speed Mechanical features Weight Stretched length Servos Maximum joint angular speed Maximum payload 900 g 40 cm 6 x Dynamixel RX-28 (37 Kg/cm@16V) 1 x Dynamixel RX-64 (60 Kg/cm@16V) [s/60 deg] Dynamixel RX [s/60 deg] Dynamixel RX g

7 structures d00b Differential Drive wheeled robot wireless system architecture Electronic features Centralized internal sensors (temperature, gyro, etc.) Decentralized sensors and control on wheels 2.4 GHz ZigBee interface External distance sensor each servo Battery LiPo 11.1 V 1600mAh Voltage Temperature Torque Position Speed Mechanical features Weight 690 g Dimensions 18.4 x 11 x 11.5 cm Motors 2 x Dynamixel RX-28 (37 Kg/cm@16V) Maximum joint angular speed [s/60 deg] Dynamixel RX [s/60 deg] Dynamixel RX-64 Maximum payload 1.5 Kg (esimated)

8 structures 3Fingers Robotic Hand tendons and springs KTH institute project Electronic features Optical encoder (no reduced axis) Magnetic absolute encoder (gear reduced axis) each motor 32-bit embedded control board USB communication interface Tendons and springs Additional servo for thumb orientation Mechanical features Weight 400 g Thumb length 13 cm Fingers length 9 cm Motors 3 x Micro-gearhead 1 x Analog Servo Shortest fingers time 3 s (closing) 1.9 s (opening) Maximum payload 1 Kg

9 algorithms MMC Mean of Multiple Computation IK model I PC: absolute reference MMC: linear model orientation constrain in the entire trajectory NLB: joint angles estimation angular limits

10 algorithms MMC Mean of Multiple Computation IK model II PC: absolute reference Algorithms convergence performance PM: relative error feedback Algorithms timing performance MMC: variable damping The weight matrix should be computed after each iteration

11 algorithms MMC Real-time MMC for redundant serial manipulator Control and simulation Real encoder readings feedback Forward kinematic for error estimation

12 interfaces Matlab MiniArm simulation virtual reality tollbox Real-time kinematic simulation Importable models Low level sensors readings High level robot communication Robot pose recording/playing

13 interfaces Control Simulation environment interfaced with the robot Control and simulation Features Real time camera streaming Serial communication Real time simple kinematic model Sensor measurements Target recognition

14 algorithms more gsarsa reinforcement learning NGPCA pattern reconstruction

15 interfaces DyNAmics Kinematic and dynamic simulation environment PhysX engine Three-dimensional Open GL rendering Importable vector format maps Multiple robot interaction Low level sensors simulations (e.g. infrared) High level sensors (e.g. target recognition) Robot pose recording/playing

16 interfaces DyNAmics Simulation interface OpenGL rendering

17 appendix Courses Sistemi Real-Time: L. Lo Bello Metodologie di progettazione hardware-software: G. Ascia Submissions IROS 2009: Redundant manipulator control using Recurrent Neural Networks Ignazio Aleo, Paolo Arena and Luca Patanè PhysCon: Learning sequences with Neural Gas for robot motion planning Ignazio Aleo, Paolo Arena and Luca Patanè time