Robotic systems aim at achieving intelligence in behavior and dexterity in motion through a
real-time connection between sensing and action. Achieving such intelligence and dexterity
often requires an integration of distributed sensors and actuators to provide a rich source of
sensory and movement patterns that can be clustered into higher levels of concepts and
actions. The key to successful integration may be a system architecture that supports com-
putational requirements unique to robotics, including uncertainty management and adaptive
error recovery through the interaction among such processes as feature transformation and
abstraction, data and concept fusion [1-14], consistency maintenance among data and
knowledge, and monitoring and replanning.
In spite of the fact that a decade of research and development in robotics has produced
numerous theoretical and experimental results, robots are yet to acquire the level of
intelligence and dexterity required for autonomous task execution in unstructured environ-
ments. Conventional approaches to building robotic systems without underlying computa-
tional principles of integrating sensing, knowledge, and action in real time seem to suffer from
limitations in the task complexity they can handle. If robot intelligtence is measured in terms
of a power-to-weight ratio, where the power is defined by the product of the complexity and
execution speed of tasks and the weight is defined by the product of volume and cost
associated with the required hardware and software, an order of magnitude improvement in
the power-to-weight ratio seems necessary for the new generation of robotics. A robot's
intelligence may be manifested by its extended autonomy. However, the extension should not
simply be the result of aggregating additional functional units, which may cause a reduction
of the power or power-to-weight ratio by increasing space and time complexity. It is
necessary to develop a system architecture that supports extended autonomy without a
decrease in the power or power-to-weight ratio. An architecture that embeds system
knowledge as well as a general problem-solving paradigm in itself may be desirable.
Planetary science sampling robots should possess extended autonomy with the capabilities
of uncertainty management, adaptation to new situations, and fault tolerance. To provide the
robot with extended autonomy requires the integration of a high level of discrete event
planning and low level of continuous time control in a hierarchy of multiresolution time
scales. However, such integration should be done under the limitation of computational
power and the requirement of real-time operation. Conventional architectures for intelligent
robotic systems, such as the subsumption architecture [15] and Nasrem architecture [16],
do not directly address the problem of reducing uncertainties as well as dealing with
unexpected events and system faults. Furthermore, the efficacy and efficiency of integrating
planning and control in multiresolution time scales are yet to be consolidated.
An architecture of intelligent robotic systems, referred to here as a perception-action net
(PAN), is presented for planetary robotic sampling. While connecting sensing and action in
real time. PAN automatically synthesizes goal-oriented behaviors or sequences of actions
toward the set goals under uncertainties, errors, and faults, through task monitoring and
In this chapter, we present a method of system uncertainty management based on
representing the overall system sensing capabilities by a perception net and propagating
uncertainties and errors forward and backward through the net for consistency by a
geometric algorithm. A geometric fusion method with a statistical basis can be found in the
literature [17]. However, there are number of problems that statistical methods cannot
handle, such as uncertainty propagation in feature transformation when nonlinearity is
involved and treating system constraints for consistency. The perception net is capable of

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