This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. A novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. In contrast, to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters of the algorithm for a specific problem a priori. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.

Autonomous small unmanned helicopter systems have been widely studied in the last decades. These systems are extremely agile due to their energy efficiency, overall costs and high levels of maneuverability compared to manned helicopters. This allows them to be used in urban environments for different applications such as search and rescue, aerial stunts for movie industry, fire fighting, surveillance, etc. Such applications require the control system to be robust and safe since a fault may lead to environmental damage and endangering human life. For reasons of the very high safety requirements, in this paper we propose a robust control design and also introduce formal verification of control for small-scale unmanned helicopters. The controller proposed is based on dynamic inversion control for a 3-DOF (degree-of-freedom) attitude dynamics while taking into account the system modelling uncertainty with variable payloads and external disturbances of wind. An invariant set called control-enabled-set is defined for flight envelope, which represents the dynamical state vectors comprised of the attitude and rotation rates, for which the stable control of the craft is feasible with our control scheme. Then the controller is verified using formal methods represented by MetiTarski automated theorem prover to ensure controller stability and robustness. Our approach also paves the way to the possibility that the autopilot system monitors whether it is getting near the boundary of its flight envelope, in which case it can propose or plan and execute an emergency landing to a safe location.

Cooperative games-based robot cooperation is analysed for reoccurring scenarios. It is shown that potential games can be used for robot coordination when the robots have a shared objective. By observing each others' behaviour in similar scenarios, they estimate each other's expected actions, which they use for their own choice of action. The resulting learning scheme can enable “tuning” of smooth cooperation by task allocation in teams of robots for various goals and in reoccurring scenarios of their environment. The theoretical results and methods are illustrated in simulation.

The coordination of multiple autonomous vehicles into convoys or platoons is expected on our highways in the near future. However, before such platoons can be deployed, the behaviours of the vehicles in these platoons must be certified. This is non-trivial and goes beyond current certification requirements, for human-controlled vehicles, in that these vehicles can act autonomously . In this paper, we show how formal verification can contribute to the analysis of these new, and increasingly autonomous, systems. An appropriate overall representation for vehicle platooning is as a multi-agent system in which each agent captures the “autonomous decisions” carried out by each vehicle. In order to ensure that these autonomous decision-making agents in vehicle platoons never violate safety requirements, we use formal verification. However, as the formal verification technique used to verify the individual agent's code does not scale to the full system, and as the global system verification technique does not capture the essential verification of autonomous behaviour, we use a combination of the two approaches. This mixed strategy allows us to verify safety requirements not only of a model of the system, but of the actual agent code used to program the autonomous vehicles.

Abstract Formal verification of agents representing robot behaviour is a growing area due to the demand that autonomous systems have to be proven safe. In this paper we present an abstract definition of autonomy which can be used to model autonomous scenarios and propose the use of small-scale simulation models representing abstract actions to infer quantitative data. To demonstrate the applicability of the approach we build and verify a model of an unmanned aerial vehicle (UAV) in an exemplary autonomous scenario, utilising this approach.

We present a verification methodology for analysing the decision-making component in agent-based hybrid systems. Traditionally hybrid automata have been used to both implement and verify such systems, but hybrid automata based modelling, programming and verification techniques scale poorly as the complexity of discrete decision-making increases making them unattractive in situations where complex logical reasoning is required. In the programming of complex systems it has, therefore, become common to separate out logical decision-making into a separate, discrete, component. However, verification techniques have failed to keep pace with this development. We are exploring agent-based logical components and have developed a model checking technique for such components which can then be composed with a separate analysis of the continuous part of the hybrid system. Among other things this allows program model checkers to be used to verify the actual implementation of the decision-making in hybrid autonomous systems.

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident.

A new iterative robust adaptive identification and control scheme by unfalsification is applied to a complex microwave super conductivity filter tuning problem (SCFTP), and for the first time, it gave an im-plementable solution. The new scheme is based on eliminating any redundancy in assigning equal importance to every model candidate in the model space. This is achieved by using a set of strategies and a set of cost functions to carry out a multi-stage unfalsification (MSU) instead of a single stage one in order to get an efficient scheme.