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Biologically inspired six-legged walking mahcine AMOS-WD06  

The AMOS-WD06 (Advanced Mobility Sensor Driven-Walking Device) is the reactive 6-legged walking machine which mimics the structures of walking animals. This walking
machine driven by modular neural control can autonomously perform reactive behaviors, e.g. wandering around, avoiding obstacles, escaping from deadlock situations or
corners, reflex action (standing in an upside-down position) and escape behavior (run away from a stiking predator). It can serve as a hardware platform for experiments concerning the function of a neural perception-action system.


Biomechatronics:

Legs: Each leg has three joints (three DOF): the thoraco-coxal (TC-) joint enables forward (+) and backward (-) movements, the coxa-trochanteral (CTr-) joint enables elevation (+) and depression (-) of the leg, and the femur-tibia (FTi-) joint enables extension (+) and flexion (-) of the tibia The morphology of this multi-jointed leg is modeled on the basis of a typical insect leg but the tarsus segments are ignored. Each tibia part contains a spring damped compliant element to absorb an impact force as well as to measure the ground contact event during the walk. This high mobility of the legs enables the walking machine to walk in omnidirection and to perform a reflex behavior, e.g., standing in an upside-down position.

 
   

 

Body: Inspired by invertebrate morphology of the American cockroach’s trunk and its motion, a backbone joint which can rotate in a horizontal axis was constructed. It imitates a connection between the first (T1) and second (T2) thoracic of a cockroach. Thus, it will provide enough mobility for the machine to climb over an obstacle to lift the front legs up to reach the top of an obstacle and then bend them downward during step climbing.

 

 
   

 

Tail: A tail with two DOF rotating in the horizontal (y-axis) and vertical (z -axis) axes was implemented on the back of the trunk. In fact, this actively moveable tail, which can be manually controlled, is used only to install a mini wireless camera for monitoring the environment while the machine is walking. However, the tail also gives the walking machine a more animal-like appearance, e.g., in analogy to a scorpion’s tail with its sting

 

 
   

Sensors: The AMOS-WD06 has six foot contact sensors for recording and analyzing the walking patterns and seven infrared (IR) sensors for stimulating reactive behaviors, e.g., obstacle avoidance and escape response. One pair of the IR sensors is located at the front part (IR_FR,FL), other two pairs are fixed at the tibiae of the two front (IR_R1,L1) and two middle legs (IR_R2,L2), and the rest of them is installed at the rear part (IR_RP ) (see Figs. 1b and c). Additionally, one upside-down detector sensor (UD) is implemented beside the machine trunk. It is applied to trigger the reflex behavior when the machine is turned into an upside-down position.

 
   

Sensor-driven neural control for omni directional locomotion and versatile reactive behaviors:

The complete sensor-driven neural controller of the walking machines was modeled with an artificial neural network using discrete-time dynamics. Part of it was developed by realizing dynamical properties of recurrent neural networks. The controller was designed as a modular structure composed of two main modules: the modular neural control and the neural sensory preprocessing networks. The modular neural control, based on a CPG, generates omnidirectional walking and drives the reflex behavior while the neural preprocessing networks filter sensory noise as well as shape the sensory data for activating an appropriate reactive behavior, e.g., a self-protective reflex, escape behavior, and obstacle avoidance behavior. The presented neuromodules are small so that their structure-function relationship can be analysed. The complete controller is general in the sense that it can be easily adapted to different types of even-legged walking machines without changing its internal structure and parameters (read [1,2] for more details). See section below for the example programming code of the controller (As requested!! )

download

 
     

Performance of the autonomous walking machine AMOS-WD06:

 

 

Video clip 1:

 

Simulated AMOS-WD06 via the physical simulation (Yet Another Robot Simulator YARS provided by Frank Pasemann and Keyan Mahmoud Ghazi-Zahedi of Fraunhofer AIS) (the implemented controller is described in [2,3,4,5]).

 

  Video clip 2: Reactive behavior of the physical autonomous walking machine AMOS-WD06 (the implemented controller is described in [2,3,4,5]).

 

 

Video clip 3:

 

The reactive behavior of the four and six-legged walking machines (AMOS-WD02 and -WD06) simulated on the physical simulator YARS. The left panel shows the simulated walking machine with its virtual environment. The right panel shows the reactive neurocontroller and the activation of each neuron. (35.0 MB) avi (Extension 1) (the implemented controller is described in [2,3,4,5]).

 

 

Video clip 4:

 

The physical walking machines (AMOS-WD02 and -WD06) and their reactive behaviors in different environmental situations. (21.0 MB) avi (Extension2) (the implemented controller is described in [2,3,4,5]) .

 

 

Video clip 5:

 

Omnidirectional walking behavior of the simulated 6- and 8-legged walking machines in the physical simulator (YARS provided by Frank Pasemann and Keyan Mahmoud Ghazi-Zahedi of Fraunhofer AIS) (the implemented controller is described in [1]) .

 

 

Video clip 6:

 

Omnidirectional walking behavior of the physical 6- and 8-legged walking machines in a real environment (the implemented controller is described in [1]) .

 

 

Video clip 7:

 

Self-protective reflex: This action will be triggered as soon as the machine is turned into an upside-down position. As a consequence, it stands still in thisposition as long as the stimulus (upside-down detector signal) is presented (the implemented controller is described in [1]).

 

 

Video clip 8:

 

 

Escape behavior: This action will be activated as soon as the rear IR sensor detects an object. As a consequence, the AMOS-WD06 increases its walking speed, as if it escapes from an attack. This action will be preserved for a few steps even if the activating stimulus has already been removed (the implemented controller is described in [1]).

 

 

Video clip 9:

 

 

 

 

 

 

Obstacle avoidance behavior: A behavior has been implemented on the AMOS-WD06 in order to enable it to avoid obstacles as well as to protect it from getting stuck in corners or deadlock situations during walking in an unknown environment (the implemented controller is described in [1]).

Three different situations showing different obstacle avoidance behaviors:

-situation1: the obstacles are placed beside the machine

- situation2: the obstacles are placed placed on the left front of the machine

- situation3: the obstacles are placed placed in front of the machine

- situation4: corner and deadlock situations

 

Video clip 10:

The situation where all reactive behaviors are sequentially activated (the implemented controller is described in [1]).

 

Video clip 11:

AMOS-WD06 performing an outdoor test run (the implemented controller is described in [1]).

 

Video clip 12:

AMOS-WD06 performing Phototaxis (the implemented controller is described in [7]).

 

Video clip 13:

AMOS-WD06 performing Phototaxis (the implemented controller is described in [7]).

 

Video clip 14:

AMOS-WD06 performing Phototaxis (the implemented controller is described in [7]).

 

Video clip 15:

AMOS-WD06 performing Auditory- and Wind-Evoked Escape Responses(the implemented controller is described in [11]).

 

 

(For more details of the AMOS-WD06 and its controller, read the papers )


Chaos control:

 

Video 1:

 

 

Examples of five different gaits. Slow wave gait (p = 9), fast
wave gait (p = 8), mixed tetrapod-wave or transition gait (p = 6), tetrapod gait (p = 5),
and tripod gait (p = 4).

 

Video 2:

Autonomous walking behaviors in different environmental conditions.
A complex sequence of eight different behaviors that include standard walking
in a tetrapod gait, up-slope walking in a wave gait, rough-terrain walking in a wave gait,
self-untrapping through chaotic motion, down-slope walking in a mixture or transition
gait (from wave to tetrapod), active phototaxis by fast walking in a tripod gait, and resting.
During walking under different conditions, if the obstacles are detected, the machine then
performs obstacle avoidance behavior by turning left/right.

 

Video 3:

Sensor-driven behavioral patterns. First scenario, the walking
machine tries to escape from the attack of a manually controlled robot by increasing its
walking speed by mean of changing its gait from a wave gait to a tripod gait. Second
scenario, the walking machine shows orientational responses by avoiding obstacle and
performing phototaxis. Note that in this scenario it is set to walk with only one gait type
(tripod gait) in order to see orientational motion clearly. Furthermore, here we show that
stopping the machine in front of a light source during phototaxis can be done by inhibiting
only all TC-joints. As a result, it makes marching in front of the light source. Last scenario,
the walking machine performs self-protective reflex by standing upside-down when
it is turned into abnormal walking position and it immediately returns to walk again if it is
turned back to its normal walking position.

 

Video 4:

Foothold searching experiment with and without chaos motion.
When applying the chaos motion, the walking machine successfully performs selfuntrapping
if its foot gets stuck in a hole but without chaos motion it failed.

 

 


Description of the walking machine


Mechanics


Dimension without the tail (L x B x H): 40 x 30 x 12 cm
Weight: 4.2 Kg
Structure of Polyvinyl Chloride (PVC) and Aluminum alloys AL5083184

6 Legs with 3 degrees of freedom of each leg
Backbone joint rotating in a horizontal axis
Active tail rotating in horizontal and vertical axis
Driven by eighteen analog (100 Ncm), one digital (220 Ncm) and two
micro (20 Ncm) servomotors.

Electronics


MBoard which is able to control up to 32 servomotors synchronously.
At the same time 32 (+4 optional) analog input channels can be sampled
and read with an update rate of up to 50 cycles per second. The
board has an RS232 interface, which serves as the standard communication
interface. The size of the board is 125 mm. x 42 mm.
PDA having an Intel (R) PXA255 processor for programming neural
preprocessing and control. It communicates with the MBoard via an
RS232 interface.
Battery of 6v NiMH 3600mAh for the servomotors
Battery of 4.8v NiMH 800mAh for six distance measurement infrared
sensors

Battery of 9v NiMH for the MBoard
Battery of 9v NiMH for a wireless camera
6 distance measurement infrared sensors (antenna-like sensors) located
at the front head and two front and two middle legs
Mini wireless camera built in a microphone installed on the top of the tail
One upside-down detector located beside of the body


Programming


C programming on the MBoard for controlling servomotors and for
reading digitized sensor data
C/ C++ programming of the neural controller [1] created in Embedded Visual C++ for implementing on a PDA.

- The C/C++ programming code of the Sensor-Driven Neural Controller (As requested!! )


Download source

 

Publications:

[1] Manoonpong, P.; Pasemann, F.; Woergoetter, F. (2008) Sensor-Driven Neural Control for Omnidirectional Locomotion and Versatile Reactive Behaviors of Walking Machines. Robotics and Autonomous Systems, doi:10.1016/j.robot.2007.07.004, Elsevier Science, Vol 56(3), pp 265-288 . (pdf)

[2] Manoonpong, P. Neural Preprocessing and Control of Reactive Walking Machines: Towards Versatile Artificial Perception-Action Systems (Cognitive Technologies) (Hardcover), Springer-Verlag, (published 2007, in English)

[3] Manoonpong, P.; Pasemann, F.; Roth, H. (2007). Modular reactive neurocontrol for biologically-inspired walking machines. The International Journal of Robotics Research, vol. 26, no. 3, pp. 301-331, dio: 10.1177/0278364906076263 (pdf) ("The final, definitive version of this paper has been published in IJRR, 3, March/2007 by Sage Publications Ltd, All rights reserved. © SAGE Publications Ltd, year of publication. It is available at: http://online.sagepub.com/ ") , http://ijr.sagepub.com/ (Multimedia files: Extension1, Extension2)

[4] Manoonpong, P.; Pasemann, F.; Roth, H. (2006). A modular neurocontroller for a sensor-driven reactive behavior of biologically inspired walking machines. International Scientific Journal of Computing, "Special Issue on ICNNAI-2006", vol. 5, no. 3., pp. 75-86 (pdf)

[5] Manoonpong, P.; Pasemann, F.; Roth, H. (2006). A modular neurocontroller for a sensor-driven reactive behavior of biologically inspired walking machines. In: Proceedings of the Fourth International Conference on Neural Networks and Artificial Intelligence (ICNNAI ’2006), the Brest State Technical University Press (ISBN 985-493-036-X), 31 May - 2 June, Brest, Belarus, pp. 70–77. (pdf)

[6] Manoonpong, P.; Pasemann, F. (2005). Advanced mobility sensor driven-walking device 06 (AMOS-WD06). In: Proceedings of the Third International Symposium on Adaptive Motion in Animals and Machines, Robot data sheet, Ilmenau: ISLE, ISBN: 3-938843-03-9, p. R23. (pdf)

[7] Manoonpong P., Pasemann F., and Woergoetter F. (2007) Reactive Neural Control for Phototaxis and Obstacle Avoidance Behavior of Walking Machines. Proceedings of World Academy of Science, Engineering and Technology (PWASET), International conference on Intelligent systems (ICIS 07), Volume 26 with ISSN: 1307-6884, Bangkok, Thailand, December 14-16 (pdf).

[8] Manoonpong P., Roth H. (2008) Reactive neural control for autonomous robots: From simple wheeled robots to complex walking machines, In: Proceedings of the Fifth International Conference on Neural Networks and Artificial Intelligence (ICNNAI ’2008), May 27-30, 2008, Minsk, Belarus. (pdf)

[9] Manoonpong P., Woergoetter F. (2008) Neural Control for Locomotion ofWalking Machines, Proceedings of 4th International Symposium on Adaptive Motion of Animals and Machines (AMAM2008), Case Western Reserve University, Cleveland OH-USA, June 1-6 2008, pp. 54-55. (pdf)

[10] Manoonpong P., Woergoetter F. (2008) Biologically-Inspired ReactiveWalking Machine AMOS-WD06, Proceedings of 4th International Symposium on Adaptive Motion of Animals and Machines (AMAM2008), Case Western Reserve University, Cleveland OH-USA, June 1-6 2008, pp. 240-241.(pdf)

[11] Manoonpong P., Woergoetter F., Pasemann, F. (2008) Neural Preprocessing of Auditory-Wind Sensory Signals and Modular Neural Control for Auditory- and Wind-Evoked Escape Responses of Walking Machines, Proceedings of the 2008 IEEE International Conference
on Robotics and Biomimetics, Bangkok, Thailand, 21- 26 February 2009, pp. 786-793. (Finalist of the Tzyh-Jong Tarn best paper award) (pdf)


News of AMOS-WD06 :

2008:

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(link) (link) (link)  

2007:

 

2006: