We consider the coordinated escort problem, where a decentralised team of supporting robots implicitly assist the mission of higher-value principal robots. The defining challenge is how to evaluate the effect of supporting robots' actions on the principal robots' mission. To capture this effect, we define two novel auxiliary reward functions for supporting robots called satisfaction improvement and satisfaction entropy, which computes the improvement in probability of mission success, or the uncertainty thereof. Given these reward functions, we coordinate the entire team of principal and supporting robots using decentralised cross entropy method (Dec-CEM), a new extension of CEM to multi-agent systems based on the product distribution approximation. In a simulated object avoidance scenario, our planning framework demonstrates up to two-fold improvement in task satisfaction against conventional decoupled information gathering. The significance of our results is to introduce a new family of algorithmic problems that will enable important new practical applications of heterogeneous multi-robot systems.

Frequency-modulated continuous waveform (FMCW) microwave and millimetre-wave radar is an attractive sensor for intelligent transport systems due to its reliable all-weather performance. This paper discusses issues involved in the design of FMCW radar mapping systems for use in collision avoidance in large vehicles operating in dynamic environments. The performance characteristics of radar are examined before an analysis is made of traditional grid-based and feature-based mapping approaches, both conceptually and in terms of implementation. The probability hypothesis density (PHD) filter is discussed as a potentially superior approach for radar mapping in dynamic environments.

This paper addresses the issue of creating a sensor model for a new short-range 24GHz close proximity detection (CPD) radar. The CPD radar is designed to provide improved situational awareness to the driver of a large vehicle. It is able to detect light vehicles and other targets at ranges from 2.2m to 45m within an arc of 160° azimuth, but radar measurements contain data from noise and clutter which must be filtered out. Dynamic thresholds such as constant false-alarm rate (CFAR) processors [19] do not work well with short measurement vectors or targets that occupy multiple measurement bins. In this paper, a new method is used where measurements of environmental noise, clutter and targets are used to calculate the false alarm and target detection probabilities for each bin and develop a fixed detection threshold for each bin. This filter is used to construct a sensor model which maps measurement power to probability of bin occupancy, which is then used to generate an occupancy grid map of the environment from CPD radar measurements.

This paper addresses the issue of improving situational awareness for large vehicles operating in all weather conditions. A new short-range 24GHz close proximity detection (CPD) microwave radar technology for providing situational awareness to the driver of large vehicles is presented. A series of experiments are performed in which the radar is used to detect a variety of static targets. The CPD radar is able to detect light vehicles at ranges from 2.2m to 17.6m within a horizontal arc of 160° with superior angular resolution to many existing low-cost radars. From this data, key characteristics of the radar's performance and the radar cross-section of light vehicles at 24GHz are calculated. The data gathered allows the design of future tests and the development of a model of the radar that allows a better understanding of its performance characteristics and can be used for occupancy grid mapping.