As the number of the degrees of motion freedom increase in a robotic system, so grows the difficulty of control. We describe a model of a novel highly flexible robotic architecture composed of hundreds of motor elements, each associated with a unique degree of motion freedom. This new robotic architecture possesses a variably compliant structure that allows for the controlled distribution of loads and forces, and for the maintenance of different conformations. We then suggest two methods of intelligent control to manage the many motor elements. One method derives from neural networks, the other involves algorithms inspired by the biological immune system. Both methods are based on the system's perception of its own kinematics, and later self-prediction of the forces generated by coordinated subsets of motor elements that accomplish robot mobility and other work upon the environment.
Mandelbrot, through his analysis of Fractals (Mandelbrot, 1977), has shown that the complexity of the physical geometry of nature is similar at all scales. This implies that a robot of fixed dimensions will always be too big to get through some passageways, and too small to get over some other obstacles. However, as others have demonstrated, increasing the number of the vehicle’s motion degrees of freedom (dof) may permit it to change its conformation and dimensions, affording to it a greater range of environmental dimensionality through which it may move. This paper contains a description of our multi dof unmanned ground vehicle (UGV), including the variety of basic behaviors of which it is capable. Our UGV is a six-dof, sensor-rich small mobile robot composed of three segments -- a central core and two tracked pods. The rotations of the pod tracks are the primary mobility mode (2-dof) of the vehicle. The pods are attached to the core at opposite ends, each by a single "L" axle that rotates through 180 degrees (2-dof), serving to improve balance and leverage. The pods can rotate 360 degrees about their end of the axle (2-dof) providing increased mobility over obstacles. The UGV in compact form is 17.6" long, 16.2" wide, and 4.6" tall, but can extend to 49" long to climb over obstacles or cross chasms, or rise to 16" high to straddle low obstacles. In its extended mode its maximum width is 9.5" permitting it to squeeze through an opening of that size. The UGV can independently draw in its two outer pods to grasp and longitudinally traverse horizontal pipes or logs or travel within a narrow culvert.
The real-time coordination and control of a many motion degrees of freedom (dof) unmanned ground vehicle under dynamic conditions in a complex environment is nearly impossible for a human operator to accomplish. Needed are adaptive on-board mechanisms to quickly complete sensor-effector loops to maintain balance and leverage. This paper contains a description of our approach to the control problem for a small unmanned ground vehicle with six dof in the three spatial dimensions. Vehicle control is based upon seven fixed action patterns that exercise all of the motion dof of which the vehicle is capable, and five basic reactive behaviors that protect the vehicle during operation. The reactive behaviors demonstrate short-term adaptations. The learning processes for long-term adaptations of the vehicle control functions that we are implementing are composed of classical and operant conditionings of novel responses to information available from distance sensors (vision and audition) built upon the pre-defined fixed action patterns. The fixed action patterns are in turn modulated by the pre-defined low-level reactive behaviors that, as unconditioned responses, continuously serve to maintain the viability of the robot during the activations of the fixed action patterns, and of the higher-order (conditioned) behaviors. The sensors of the internal environment that govern the low-level reactive behaviors also serve as the criteria for operant conditioning, and satisfy the requirement for basic behavioral motivation.