Abstract The autonomous coordinated flying for groups of unmanned aerial vehicles that maximise network coverage to mobile ground-based units by efficiently utilising the available on-board power is a complex problem. Their coordination involves the fulfilment of multiple objectives that are directly dependent on dynamic, unpredictable and uncontrollable phenomena. In this paper, two systems are presented and compared based on their ability to reposition fixed-wing unmanned aerial vehicles to maintain a useful airborne wireless network topology. Genetic algorithms and non-cooperative games are employed for the generation of optimal flying solutions. The two methods consider realistic kinematics for hydrocarbon-powered medium-altitude, long-endurance aircrafts. Coupled with a communication model that addresses environmental conditions, they optimise flying to maximising the number of supported ground-based units. Results of large-scale scenarios highlight the ability of genetic algorithms to evolve flexible sets of manoeuvres that keep the flying vehicles separated and provide optimal solutions over shorter settling times. In comparison, game theory is found to identify strategies of predefined manoeuvres that maximise coverage but require more time to converge.

Agent-based routing in wireless ad hoc networks defines a set of rules that all the participating nodes follow. Routing becomes a collaboration between nodes, reducing computational and resource costs. Swarm Intelligence uses agent-like entities from insect societies as a metaphor to solve the routing problem. Certain insects exchange information about their activities and the environment in which they operate in order to complete their tasks in an adaptive, efficient and scalable manner. This paper examines Swarm Intelligence based routing protocols, along with a newly proposed bee-inspired routing protocol for providing multi-path routing in wireless ad hoc networks of mobile nodes. Simulation results indicate that applying Swarm Intelligence offers a significant level of adaptability and efficiency that, under several network conditions, allow the protocol to outperform traditional approaches.

An autonomous mobile robot requires an onboard controller that allows it to perform its tasks for long periods in isolation. One possibility is for the robot to adapt to its environment using some form of artificial intelligence. Evolutionary techniques such as genetic programming (GP) offer the possibility of automatically programming the controller based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been successfully applied to various problems, particularly those for which GP has been successful. We present a method for applying GE to autonomous robot control and evaluate it in simulation for the Khepera robot.

A hormone-inspired task scheduling method is described which assigns tasks to a group of robots, taking into account the robotspsila performances. This method draws on previous work using endocrine-based methodologies, and incorporates existing evolutionary robotics methods. In the resulting system, robot performance data, generated as part of evolution, is used to autonomously reassign tasks amongst the group, without the use of a central controller.

A central aim of robotics research is to design robots that can perform in the real world; a real world that is often highly changeable in nature. An important challenge for researchers is therefore to produce robots that can improve their performance when the environment is stable, and adapt when the environment changes. This paper reports on experiments which show how evolutionary methods can provide lifelong adaptation for robots, and how this evolutionary process was embodied on the robot itself. A unique combination of training and lifelong adaptation are used, and this paper highlights the importance of training to this approach.

Researchers in mobile robotics have for many years been investigating the use of evolutionary techniques for the development of control methods. Most evolutionary robotics work has concentrated on the use of evolution exclusively during the training phase of the robot controller. Few investigators continue evolution throughout the lifetime of a robot. This paper investigates a system that uses a genetic algorithm to train a robot for a generalised test environment, then an evolutionary strategy to investigate the effect of continuing the evolution as the robot progresses with its task. It is concluded that lifelong evolution has an important role to play in producing truly adaptive robots.

Robotic systems range from teleoperated to fully autonomous (where no human intervention takes place). The word "telerobotic" describes robotic systems which, although guided by a human, have a degree of autonomous behavior. This paper examines the tradeoff between the increasing design and implementation effort necessary as the system moves through the continuum from teleoperated to autonomous and the amount of human interaction required. A case study of a human "shepherd" interacting with a robotic "sheepdog" which directs a robotic "sheep" is used.

One problem often encountered when performing assembly using robots is how to deal with variation and uncertainty in the workcell. Either the workcell becomes tightly constrained, thus losing the flexibility of the system, or errors occur, which reduces the reliability of the system. This paper attempts to address this problem using a hybrid architecture. A planning system produces a high-level ordering of the assembly in terms of part motions, which are translated into robot motions by an adaptive run-time execution system. The execution system contains a flexible hierarchy of competent modular units which combine to perform the assembly reliably. The work in this paper attempts to reduce the complexity found in assembly planning and to provide a reactive run-time system which can deal with uncertainty and variation without reference to a computationally expensive global-world model. The background to this work is presented, along with an experimental system designed to test out the ideas. Conclusions about the usefulness of the system are drawn at the end of the paper.