In this paper, we review the five rules published in EPSRC Principles of Robotics with a specific focus on future robotics research topics. It is demonstrated through a pictorial representation of the five rules that these rules are questionably not sufficient, overlapping and not explicitly reflecting the true challenges of robotics ethics in relation to the future of robotics research.

Large-scale traffic systems require techniques that are able to 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, and 4) cope with multimodal conditional probability density functions (pdfs) for the states. Often, centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques that are able to cope with these problems of large traffic network systems. These are parallelized particle filters (PPFs) and a parallelized Gaussian sum particle filter (PGSPF) that are suitable for online traffic management. We show how complex pdfs of the high-dimensional traffic state can be decomposed into functions with simpler forms and how the whole estimation problem solved in an efficient way. The proposed approach is general, with limited interactions, which reduce the computational time and provide high estimation accuracy. The efficiency of the PPFs and PGSPFs is evaluated in terms of accuracy, complexity, and communication demands and compared with the case where all processing is centralized.

The development of real-time traffic models is of paramount importance for the purposes of optimizing traffic flow. Inspired by the compositional model (CM) and the METANET model, this paper proposes an interval approach for macroscopic traffic modeling. We develop an interval CM (ICM) and an interval implementation of the METANET model (IMETANET) that provide a natural way of predicting traffic flows without the assumption of uniform distribution of vehicles in a cell. The interval macroscopic models are suitable for real-time applications in road networks and can be part of road traffic surveillance and control systems. The performances of the interval approaches are investigated for both the ICM and the IMETANET models. The efficiency of the interval models is demonstrated over simulated data, and as well as over real traffic data from Motorway Incident Detection and Automatic Signalling (MIDAS) data sets from the United Kingdom.

Over the last years, particle filters (PF) have attracted considerable attention in the field of nonlinear state estimation due to their relaxation of the linear and Gaussian restrictions in the state space model. However, for some applications, PF are not adapted for a real-time implementation. In this paper we propose a new method, called box particle filter (BPF), for dynamic nonlinear state estimation, which is based on particle filters and interval frameworks and which is well adapted for real time applications. Interval framework will allow to explain regions with high likelihood by a small number of box particles instead of a large number of particles in the case of PF. Experiments on real data for global localization of a vehicle show the usefulness and the efficiency of the proposed approach.

This paper presents a new fusion strategy that mixes interval analysis techniques and particle filters for data fusion and state estimation purposes occurring in many robotic perception problems. The method requires a small number of "box particles". This, in fact, answers one of major problems when using particle filters techniques. We report the case study of a land vehicle localization problem and we make a comparison based on real data between the performance of a particle filter and the new developed strategy.

In order to implement a continuous and robust dynamic localization of a mobile robot, the fusion of dead reckoning and absolute sensors is often used. Depending on the objectives of precision or integrity, the choice of an algorithm could be crucial. For example, if the models used for the fusion are non linear, classical tools (such as a Kalman filter) cannot guarantee maximum error estimation. There are bounded error approaches that are insensitive to non linearity. In this context, the random errors are only modeled by their maximum bound. This paper compares a technique based on constraints propagation on intervals, with the usual Extended Kalman Filter for the data fusion of redundant sensors. We have thus developed both techniques and we consider the fusion of wheel encoders, a gyro and a differential GPS receiver. Experimental results show that the precision of a constraints propagation technique can be very good with guaranteed estimations. Moreover, such an approach is well adapted to a real time implementation.

In order to get continuous dynamic localization of a mobile robot, dead reckoning and absolute sensors are usually merged. The models used for this fusion are non linear and, therefore, classical tools (such as Kalman filter) cannot guarantee a maximum error estimation. In some applications, integrity is essential and the ability to guaranty the result is a crucial point. There are ensemblist approaches that are insensitive to non linearity. In this context, the random errors are only modeled by their maximum bound. This paper presents a new technique to merge the data of redundant sensors with a guaranteed result based on constraints propagation techniques on intervals. We have thus developed an approach for the fusion of the 4 ABS wheel encoders, a measure of the angle of the driving wheel and a differential GPS receiver. Experimental results show that the precision that one can obtain is very good with a guaranteed result. Moreover, constraints propagation techniques are well adapted to a real time implementation.