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 cockroach 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. Note that the figure of a cockroach leg is from Zill S, Schmitz J, Büschges A (2004) Load sensing and control of posture and locomotion, Arthropod Struct. Dev. 33, 273–286.

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. Note that the figure of a cockroach is from http://www.angelfire.com/in/ghcfaq/

Tail

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. Note that the figure of a scorpion’s tail with a sting is modified from S.R. Petersen 2005 (http://www.bugweb.dk/).

Sensors

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

The 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).

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

 

Multi-Servo IO-Board (MBoard)1 developed at Fraunhofer Institute in Sankt Augustin. It 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

Personal Digital Assistant (PDA) having an Intel (R) PXA255 processor for programming neural preprocessing and control. It communicates with the MBoard via an RS232 interface

Battery of 4.8v NiMH 2100mAh for the servomotors

Battery of 4.8v NiMH 2100mAh for the support circuitry of the auditory sensors

Battery of 9v NiMH for the MBoard

Battery of 9v NiMH for the wireless camera

Mini wireless camera built in a microphone installed on the top of the tail

6 distance measurement infrared sensors (antenna-like sensors) located at the legs and the front part

Upside-down detector sensor, current sensor, auditory-wind detector sensor, gyro sensor, etc

C programming on the MBoard for controlling servomotors and for reading digitized sensor data

Embedded Visual C++ on the PDA for programming neural preprocessing and control

Reactive behaviors under sensor-driven neural control [1]

Video1		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]).
Video2		Reactive behavior of the physical autonomous walking machine AMOS-WD06 (the implemented controller is described in [2,3,4,5]).
Video3		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]).
Video4		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]) .
Video5		Omnidirectional walking behavior of the physical 6- and 8-legged walking machines in a real environment (the implemented controller is described in [1]) .
Video6		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]).

Adaptive and reactive behaviors under neural chaos control and learning [12]

Video15		AMOS-WD06 may use multiple different gaits (controlled by the same central pattern generator)   Description: 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).
Video16		AMOS runs an obstacle course: Different gaits, obstacle avoidance, phototaxis, and resting Description: Autonomous walking behaviors in different environmental conditions. A complex sequence of eight different behaviors is shown that include standard walking in a tetrapod gait, upslope 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. As soon as obstacles are detected the machine performs obstacle avoidance behavior by turning left/right.
Video17		Escaping from danger from behind, orienting and self-protection Description: Sensor-driven behavioral patterns. In the first scenario, the walking machine tries to escape from the attack of a manually controlled robot by increasing its walking speed by means of changing its gait from a wave gait to a tripod gait. In the second scenario, the walking machine shows orienting responses by avoiding obstacles and performing phototaxis. Note that in this scenario it is set to walk with only one gait type (tripod gait) in order to see the orienting behavior more clearly. Furthermore, here we show that stopping the machine in front of a light source during phototaxis can be achieved by inhibiting only all TC-joints. As a result, it performs “marching” in front of the light source. In the last scenario, the walking machine performs a self-protective reflex by standing upside-down when it is turned into an abnormal walking position and it immediately returns to walk again as soon as it is turned back to its normal walking position.
Video18		Exploiting chaos for self-untrapping Description: Foothold searching experiment with and without chaos. When applying chaos, the walking machine successfully performs self-untrapping if its foot gets stuck in a hole but without chaos it fails
Video19		Learning to use optimal gaits to save energy Description: Learning to optimize energy consumption during walking on a steep slope. The walking machine learns to search for an energy saving gait (i.e., slow wave gait) while walking on a steep slope.
Video20		Learning to escape the noisy enemy Description: Learning to escape danger from behind. The walking machine learns to search for a fast gait (i.e., tripod gait) in order to escape danger from behind. Here the low frequency sound and the reflex infra-red signal are thought to represent danger signals. They are detected by the auditory-wind detector sensor (AW) and the rear infra-red sensor (IR).