An enhanced decentralized artificial immune-based strategy formulation algorithm for swarms of autonomous vehicles

This work presents an algorithmic approach to the problem of strategy assignment to the members of a swarm of autonomous vehicles. The proposed methodology draws inspiration from the artificial immune system (AIS), where a large number of antibodies cooperate in order to protect an organism from foreign threats by local exchange of information. The decentralized nature of the methodology does not suffer from problems like the need of a central control unit, the high maintenance costs and the risks associated with having a single point of system failure, which are common to centralized control techniques. Decentralized and distributed optimization schemes employ simple algorithms, which are fast, robust and can run locally on an autonomous unit due to their low processing power requirements. In contrast to standard AIS-based decentralized schemes, the proposed methodology makes use of a dynamic formulation of the available strategies and avoids the possibility of choosing an invalid strategy, which may lead to inferior swarm performance. The methodology is further enhanced by a dual strategy activation decay technique and a blind threat-follow rule. Statistical testing on different case studies based on “enemy search and engage” type scenarios in a simulated environment demonstrates the superior performance of the proposed algorithm against the standard AIS, an enhanced AIS version and a centralized particle swarm optimization (PSO) based methodology.