This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

$-calculus is a higher-order polyadic process algebra for resource bounded computation. It has been designed to handle autonomous agents, evolutionary computing, neural nets, expert systems, machine learning, and distributed interactive AI systems, in general. $-calculus has built-in cost-optimization mechanism allowing to deal with nondeterminism, incomplete and uncertain information. In this paper, we investigate expressiveness of $-calculus. We show that due to innitary means, it allows to express models having richer behavior than Turing machine, including cellular automata, interaction machines, neural networks, and random automata networks. We also investigate the importance of synchronization, representation of continuity, and higher-order.

We propose a novel design environment for developing multi-agent systems (MASs) for applications in mobile robotics. Because emergent behavior phenomena make it next to impossible to directly synthesize viable MAS designs from specications, extensive simulation studies are needed to evaluate these designs. Furthermore, due to the fact that the design space for MASs systems is combinatorially large, the evaluation of candidate designs must be done in a hierachical, multi-resolution, parallel and distributed manner. Our proposed design environment is based on US Department of Defense's high-level architecture (HLA), an established software infrastructure for heterogeneous, distributed simulations. The proposed environment automatically generates and manages HLA federations (i.e., collections of distributed and/or parallel simulation and service federates) that communicate over runtime infrastructure (RTI) software buses. Each federation simulates a different candidate design for the MAS under development. Federations execute independently and in parallel. Our proposed design environment's refinement component uses a genetic algorithm (GA) to select the best candidate designs from the current generation and generates a set of refined, next-generation, candidate designs. A federation is created and managed for each of the next-generation designs and the automatic design process is repeated.

GEOINFORMATIC SURVEILLANCE FOR HOTSPOT DETECTION AND PRIORITIZATION

Cooperative behavior, visible in many animal species, is achieved through varying degrees of communication (e.g. chemical clues from ants, a bees "dance"). When looking at military operations, groups communicate. In the networking of computers and their coordination, communication is necessary to achieve goals. In the same way, cooperative behavior for autonomous vehicles will require some level of communication between group members. Both military and scientific concepts for autonomous operations are projecting the use of a variety of heterogeneous platforms in groupoperations in order to address various operating regimes and specialized tasks thus introducing unique issues for coordinated operations.

Abstract—This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.