Rational physical agent reasoning beyond logic

The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation

Frequency domain iterative feedforward/feedback tuning for MIMO ANVC

A new gradient estimation method is proposed that relies on efficient computation of the negative gradient of the average linear quadratic cost function completely in the frequency domain. Based on the proposed theory, a new iterative tuning method is developed to solve linear multi-input multi-output Active Noise/Vibration Control problems. Compared with published iterative tuning methods, the new method has the added advantage that the number of experiments per iteration is reduced to one. Combined with the other advantage of relativelysimple controller structures, the method is suitable for real-time implementation as an adaptive controlle

Agent Based Approaches to Engineering Autonomous Space Software

Current approaches to the engineering of space software such as satellite control systems are based around the development of feedback controllers using packages such as MatLab's Simulink toolbox. These provide powerful tools for engineering real time systems that adapt to changes in the environment but are limited when the controller itself needs to be adapted. We are investigating ways in which ideas from temporal logics and agent programming can be integrated with the use of such control systems to provide a more powerful layer of autonomous decision making. This paper will discuss our initial approaches to the engineering of such systems.Comment: 3 pages, 1 Figure, Formal Methods in Aerospac

Verification of logical consistency in robotic reasoning

Most autonomous robotic agents use logic inference to keep themselves to safe and permitted behaviour. Given a set of rules, it is important that the robot is able to establish the consistency between its rules, its perception-based beliefs, its planned actions and their consequences. This paper investigates how a robotic agent can use model checking to examine the consistency of its rules, beliefs and actions. A rule set is modelled by a Boolean evolution system with synchronous semantics, which can be translated into a labelled transition system (LTS). It is proven that stability and consistency can be formulated as computation tree logic (CTL) and linear temporal logic (LTL) properties. Two new algorithms are presented to perform realtime consistency and stability checks respectively. Their implementation provides us a computational tool, which can form the basis of efficient consistency checks on-board robots

Formal Verification of Autonomous Vehicle Platooning

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 new autonomous behaviors of the vehicles in these platoons must be certified. An appropriate 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 agent code does not scale to the full system and as the global verification technique does not capture the essential verification of autonomous behavior, 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

Reliable Non-Linear State Estimation Involving Time Uncertainties

This paper presents a new approach to bounded-error state estimation involving time uncertainties. For a given bounded observation of a continuous-time non-linear system, it is assumed that neither the values of the observed data nor their acquisition instants are known exactly. For systems described by state-space equations, we prove theoretically and demonstrate by simulations that the proposed constraint propagation approach enables the computation of bounding sets for the systems’ state vectors that are consistent with the uncertain measurements. The bounding property of the method is guaranteed even if the system is strongly non-linear. Compared with other existing constraint propagation approaches, the originality of the method stems from our definition and use of bounding tubes which enable to enclose the set of all feasible trajectories inside sets. This method makes it possible to build specific operators for the propagation of time uncertainties through the whole trajectory. The efficiency of the approach is illustrated on two examples: the dynamic localization of a mobile robot and the correction of a drifting clock