Onboard Mission Management for a VTOL UAV Using Sequence and Supervisory Control [chapter]

Florian Adolf, Franz Andert
2010 Cutting Edge Robotics 2010  
Cutting Edge Robotics 2010 302 the loop at all levels of autonomy whenever the data link is available. Also, the operational environment is characterized by events that can occur in an unknown order and at sporadic time instances. It must implement input checks for syntactical plausibility and even semantic correctness, wherever possible. Beside this autonomy management problem, the organization and abstraction of the system into a suitable architecture is a challenge. Thus, in the next section
more » ... existing architectural concepts are discussed. Related High-Level Control Architectures More self-reliance and decision-making autonomy poses questions regarding a suitable architecture according to which the management system is designed. Knowledge-based systems establishing the concept of a cognitive process as decisionmaking entity were presented in the UAV domain (Hill 1997 , Putzer 2003 . Concepts exist that are based on the behavior-based paradigm (Weiss 2005) , where a set of elementary behaviors (so-called skills, such as movement primitives) is combined in such a way that a new emergent behavior is created. Furthermore, layered architectures (Freed 2005) have been proposed that comprise distinct system modes also known as hybrid control (Egerstedt 1999). Using knowledge-based systems, classical artificial intelligence spent over five decades trying to model human-like intelligence. Inspired by these systems, several research projects seek to produce a human-like thinking process (also known as cognition) in order to achieve high-level control in decision-making systems (Hill 1997 , Putzer 2003 . A commonly used cognitive architecture is implemented in SOAR (Laird 1987). Since real-time properties are one crucial design aspect for a UAV decision-making system, a real-time derivate of SOAR, Hero-SOAR exists. However, there are major implementation issues related to cognitive production systems (Musliner 1995). First, "chunking", a pattern matching technique, might be hard to confine with respect to execution time and memory usage. Second, real-time reflexive actions (a direct connection of a sensor to an actuator) invoke a high-variance of unpredictable system events. Furthermore, problems were experienced when trying to effectively coordinate and mediate reflexive behaviors with the overall deliberative behavior of the system. If the reflexive actions can bypass the normal deliberation mechanisms, it may be difficult or impossible for the deliberation processing to reason about and affect the real-time reaction. Hence, the architecture for any UAV decision making system should particularly focus on "embedding real-time in artificial intelligence" rather than "embedding artificial intelligence in real-time" (Musliner 1995). Moreover, a principle shortcoming of the cognitive approach is the emphasis on representation at a high, symbolic level. This yields to control strategies that may make conceptual sense to a human designer but the intelligence in such systems belongs to the designer. Additionally, it is questionable whether humans deploy a complex thinking process for every intended behavior rather than think in a more reactive way (Agre 1995). These disadvantages are addressed by the behavior-based control with the Subsumption Architecture (Toal 1996 , Brooks 1990 ), which does not necessarily seek to produce cognition. It rather uses a hierarchy of fast reactive loops where each loop is capable of executing a distinct behavior. Moreover, higher reactive loops modify the behavior of lower ones. The
doi:10.5772/10323 fatcat:wghw3fksafa6dgsvsxckof52hu