Verification of an Autonomous Reliable Wingman using CCL

We present a system of two aircraft, one human-piloted and one autonomous, that must coordinate to achieve tasks. The vehicles communicate over two data channels, one high rate link for state data transfer and one low rate link for command messages. We analyze the operation of the system when the high rate link fails and the aircraft must use the low rate link to execute a safe "lost wingman" procedure to increase separation and re-acquire contact. In particular, the protocol is encoded in CCL, the Computation and Control Language, and analyzed using temporal logic. A portion of the verified code is then used to command the unmanned aircraft, while on the human-piloted craft the protocol takes the form of detailed flight procedures. An overview of the implementation for a June, 2004 flight test is also presented

A Decomposition Approach to Multi-Vehicle Cooperative Control

We present methods that generate cooperative strategies for multi-vehicle control problems using a decomposition approach. By introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decomposed the problem into a combinatorial component and a continuous component. The continuous component of the problem is captured by task execution, and the combinatorial component is captured by task assignment. In this paper, we present a solver for task assignment that generates near-optimal assignments quickly and can be used in real-time applications. To motivate our methods, we apply them to an adversarial game between two teams of vehicles. One team is governed by simple rules and the other by our algorithms. In our study of this game we found phase transitions, showing that the task assignment problem is most difficult to solve when the capabilities of the adversaries are comparable. Finally, we implement our algorithms in a multi-level architecture with a variable replanning rate at each level to provide feedback on a dynamically changing and uncertain environment.Comment: 36 pages, 19 figures, for associated web page see http://control.mae.cornell.edu/earl/decom

MASL: a Language for Multi-Agent System

The classical approach for Multi-Agent System (MAS) Control, especially autonomous and robotic ones, deals first from a microscopic point of view: each agent embed a control program with communication/synchronization primitives that enable cooperation between agents. The emergence of a global behaviour from a macroscopic point of view can only be observed afterwards. In this context, MASL offers a macroscopic and unified approach with heterogeneous and distributed calculations over deliberative, reactive or hybrid agents. In this high level language, regardless of the runtime, each concurrent agent locally decides its participation in a collective execution block named an e-block. Each e-block is an anonymous collective program that runs over an agent network following local conditions. The orchestral mode (scalar, asynchronous, synchronous) is statically fixed by a shared block attribute. The communication use shared memory, events, synchronous messages passing, and asynchronous messages passing. Heterogeneous agents are managed with heritage and polymorphism. Permeability mechanism, dealing with agent autonomy, allows an agent to dynamically filter calls to its interface in respects to the sender position in the e-block hierarchy. In dynamic task allocation of agents, auto failover and recovery, agent replacement in a robot fleet (case of agent failure, loss of a mandatory functionality for the mission) an e-block is an entry point of a collaborative work. In the case of synchronous e-block, the programming paradigm is the data parallel model with iterative task for waves of agents. Finally, MASL offers advances in the field of MAS (dynamic belonging to groups, accuracy of the pace of actions to undertake to enable a desired cooperation) and for the management of errors

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible