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 a modular neural controller (Only one artificial neural netwrok) 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 auditory- and wind evoked escape behavior (run away from a wind puff and/or an auditory signal). Furthermore, it can also walk in omnidirection  generated by the neural controller.
It can serve as a hardware platform for experiments concerning the function of a neural perception-action system [1,3].
This robot has now 28 sensors, 21 motors.
It consists of six identical 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 (see Figure 1). Each tibia segment has a spring-like compliant element to absorb impact force as well as to measure ground contact during walking. All leg joints are driven by analog servo motors. Inspired by invertebrate morphology of the American cockroach’s trunk and its motion, the trunk of the machine is formed with two body parts (see Figure 2) : a front part (T1) where two forelegs are installed and a central body part (T2) where two middle legs and two hind legs are attached. They are connected by one active backbone joint driven by a digital servo motor. This machine has six foot contact (FC1,...,6) sensors, seven infrared (IR1,...,7) sensors, two light dependent resistor (LDR1,2) sensors, one gyro (GR) sensor, one inclinometer (IM) sensor, one upside-down detector (UD) sensor, and one auditory-wind detector (AW) sensor . The foot contact sensors are for recording and analyzing the walking patterns . The IR1,...,7 sensors are used to elicit negative tropism, e.g., obstacle avoidance and escape response , while the LDR1,2 sensors serve to activate positive tropism like phototaxis . The GR and IM sensors apply to upward/downward slope detection. The UD sensor is employed to trigger a self-protective reflex behavior when the machine is turned into an upside-down position . The AW sensor is applied for the auditory-wind application. It will activate the auditory- and wind-evoked escape behavior of the walking machine.
The control (artificial neural network [1,3]) of this walking machine is programmed into a mobile processor (a PDA). The PDA is interfaced with the MBoard which digitizes all sensory signals and generates a pulse width modulation (PWM) signal to command all servomotors. The communication between the PDA and the MBoard* is accomplished via an RS232 interface at 57.6 kbits/s.
Electrical power supply of the whole systems is via batteries of one 7.4 V Lithium polymer 2200 mAh for all servomotors, two 9 V NiMH 180 mAh for the electronic board (MBoard) and a wireless camera, and four 1.2 V NiMH 2200 mAh for all sensors.
*MBoard is the Multi-Servo IO-Board (MBoard). It can control up to 32 servo motors and can digitized analog sensory signals up to 36 signals. Its size is 125 mm x 42 mm x 15 mm.
• C programming on the MBoard for controlling servomotors and for reading digitized sensor data
• C/ C++ programming of the neural controller [1,2,3] created in Embedded Visual C++ for implementing on a PDA.
 P. Manoonpong, F. Pasemann and F. Woergoetter, “Sensor-driven neural control for omnidirectional locomotion and versatile reactive behaviors of walking machines,” Robotics and Autonomous Systems, vol. 56, no. 3, pp. 265–288, 2008.
 P. Manoonpong, F. Pasemann and F. Woergoetter, “Reactive neural control for phototaxis and obstacle avoidance behavior of walking machines,” International Journal of Mechanical Systems Science and Engineering, vol. 1, no. 3, pp. 172–177, 2007.
 P. Manoonpong, “Neural preprocessing and control of reactive walking machines: Towards versatile artificial perceptionaction systems,” Cognitive Technologies, (BOOK), Springer, 2007.
Check the following links to see its performance:
Obstacle avoidance behavior:
The same controller is also applied to 4 legs and 8 legs:
[ame="http://www.youtube.com/watch?v=xfyYfhJoTxk"]YouTube - Autonomous walking robot (AMOS-WD06) under neural control[/ame]
[ame="http://www.youtube.com/watch?v=g2bduFRgieo"]YouTube - Phototaxis: Autonomous walking under neural control[/ame]