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The basis behavior set of a system provides elements that are not further reducible to each other and that, when composed by sequential or concurrent execution, produce the complete behavior repertoire for the system.

Such basis behaviors are constructed, learned, or evolved from stable, robust interaction dynamics between the agent/robot and its environment, and serve as a substrate for the system's more complex behaviors.

My work generalized the notion of basis behaviors to multi-robot interactions, and demonstrated how a small set of basis behaviors per robot can be used to demonstrate a rich repertoire of individual and group-level behaviors, including following, flocking, homing, herding, aggregation, dispersion, and formations.

Note that behaviors themselves can have state, and can form representations when networked together. Thus, unlike reactive systems, behavior-based systems are not limited in their expressive and learning capabilities.

Behavior-based robotics is quite simply the design of robots where there are no internal "models" of the environment. Instead, the robot's action is state-machine driven via inputs gleaned from the robot's sensors.

I wanted to share an adaptation of the Schead v4, that I have been experimenting with. It is (for lack of a better term) a Master/Slave Schmitt Comparitor Head (M/S SC-H). With the addition of a 74 AC 240 or two (as motor drivers) and a pair of motors, you can put together an interesting little light seeking, wheeled robot with peripheral vision.

As long as the light reaching the photo-bridge of the Master SC-H is balanced, then the Slave SC-H acts as a regular, lone SC-H would. So, if one of the slave photo-diodes detects more light then the other, the inverter that controls the motor on that side changes states and is now the same as the inverter of the Master SC-H tied to the same motor. This turns that motor off and the robot will pivot around the stopped wheel toward the greater light source until the light on each sensors is balanced and the motor again begins to turn.

I am also using SCar to continue experimenting with Stacking separate Sensor/Behavior circuits onto a robot. I will post more as progress is made.

The robots do not build a model of their world they simply act in response to the things they encounter whilst existing there.

This form of robotics has proved to be successful in environments that are unknown to the robot, environments that are busy or noisy such as a place with moving objects or people

An important part of the behaviour based theory is "embodiment" This means that a robot must be embodied, have a presence (it is an entity in itself).  In order to react the robot must be surrounded by the real world.

stimulus-response

a plan of action

combine the best of both Reactive and Deliberative control

Droidmakr (Cliff Boerema) came up with an interesting idea for a light-tracking head with a form of peripheral vision. As often happens, the circuit turned into something different -- a photopopper:

All done with a single 74HC14 (the '240 being a motor driver).

I tried the same setup with the 74*240 (with an extra inverter per motor) and 7404, but the 74HC14 seems to work best.

camera-based vision system (for light detection and navigation) two microphones, binaural hearing beat detection (allows pleo to dance and listen to music) - this feature was removed but may be added on again. eight touch sensors (head, chin, shoulders, back, feet) four foot switches (surface detection) fourteen force-feedback sensors, one per joint orientation tilt sensor for body position infrared mouth sensor for object detection into mouth infrared transmit and receive for communication with other Pleos Mini-USB port for online downloads SD card slot for Pleo add-ons infrared detection for external objects 32-bit Atmel ARM 7 microprocessor (main processor for Pleo) 32-bit NXP ARM 7 sub processor (camera system, audio input dedicated processor) four 8-bit processors (low-level motor control)

CALEB CHUNG: Of course we could have used micro-servo motors to accomplish the motion of Pleo, but we aren’t able to use expensive motors. So we had to engineer it with a high-speed motor with high gearing and no backlash for control purposes and have it all fit within the muscle envelope of Pleo.

So what we did was go after a lot of ethology research. How do animals really handle the complexity of their environment? We built a virtual brain—a whole system that decides how Pleo will react in various situations.

CALEB CHUNG: Pleo will reset thresholds and adjust his idea of what he thinks is normal. Let’s say you get Pleo and you take him home to your shag carpet. When Pleo walks, the carpet will drag on his feet. So his force feedback sensors will realize that he is spending too much energy to walk around. Pleo will try different things to reduce the energy spent. Eventually, he will have the idea to step higher. Your Pleo compared to my Pleo will walk with a higher step.