We propose a decentralised multi-robot coordination algorithm that features a rich representation for encoding and communicating each robot’s intent. This representation for “intent messages” enables improved coordination behaviour and communication efficiency in difficult scenarios, such as those where there are unknown points of contention that require negotiation between robots. Each intent message is an adaptive policy that conditions on identified points of contention that conflict with the intentions of other robots. These policies are concisely expressed as behaviour trees via algebraic logic simplification, and are interpretable by robot teammates and human operators. We propose this intent representation in the context of the Dec-MCTS online planning algorithm for decentralised coordination. We present results for a generalised multi-robot orienteering domain that show improved plan convergence and coordination performance over standard Dec-MCTS enabled by the intent representation’s ability to encode and facilitate negotiation over points of contention.

Meeting or conference assistance is a popular application that typically requires compact configurations of co-located audio and visual sensors. This paper proposes a novel solution for online separation of an unknown and time-varying number of moving sources using only a single microphone array co-located with a single visual device. The approach exploits the complementary nature of simultaneous audio and visual measurements, accomplished by a model-centric 3-stage process of detection, tracking, and (spatial) filtering, which performs separation in a block-wise or recursive fashion. Fusing the measurements requires solving the multi-modal space-time permutation problem, since the audio and visual measurements reside in different observation spaces, but also are unidentified or unlabeled (with respect to the unknown and time-varying number of sources), and are subject to noise, extraneous measurements and missing measurements. A labeled random finite set tracking filter is applied to resolve the permutation problem and recursively estimate the source identities and trajectories. A time-varying set of generalized side-lobe cancellers is constructed based on the tracking estimates to perform online separation. Evaluations are undertaken with live human speakers.

One of the major challenges in multi-robot exploration is to fuse the partial maps generated by individual robots into a consistent global map. We address 3D volumetric map fusion by extending the well known iterative closest point(ICP) algorithm to include probabilistic distance and surface information. In addition, the relative transformation is evaluated based on Mahalanobis distance and map dissimilarities are integrated using relative entropy filter. The efficiency of the proposed algorithm is evaluated using maps generated from both simulated and real environments and is shown to generate more consistent global maps.

Although simultaneous localization and mapping (SLAM) algorithms are widely appreciated in mobile robot navigation, they can be further improved to suit practical applications in dynamic environmental conditions. One such important improvement is the detection and tracking of moving objects present in the sensor field of view (FOV). In this paper we propose to extend our recently introduced Collaborative Multi-vehicle SLAM (CMSLAM) solution based on the random finite set (RFS) representation of the feature map and measurements, by tracking both static and dynamic features. We represent static features observed during the SLAMprocess, along with dynamic features present in the current sensor FOV, as an augmented RFS. The corresponding probability density is propagated using a Bayes recursion, from which the static feature map and the estimates of dynamic feature locations can be obtained. Measurement update in the CMSLAM process is carried out only using the static feature map to take advantage of obvious accuracy improvements.

In this paper we present a novel solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the random finite set (RFS) based SLAM filter framework using two recently developed multi-sensor information fusion approaches. Our solution is based on the modelling of the measurements and the landmark map as RFSs and factorizing the MVSLAM posterior into a product of the joint vehicle trajectories posterior and the landmark map posterior conditioned the vehicle trajectories. The joint vehicle trajectories posterior is propagated using a particle filter while the landmark map posterior conditioned on the vehicle trajectories is propagated using a Gaussian Mixture (GM) implementation of the probability hypothesis density (PHD) filter.

In this paper we describe a fully integrated system for detecting and tracking pedestrians in a dynamic urban environment. The system can reliably detect and track pedestrians to a range of 100 m in highly cluttered environments. The system uses a highly accurate 3D LIDAR from Velodyne to segment the scene into regions of interest or blobs, from which the pedestrians are determined. The pedestrians are then tracked using probability hypothesis density (PHD) filter which is based on random finite set theoretic framework. In contrast to classical approaches, this random finite set framework does not require any explicit data associations. The PHD filter is implemented using a Gaussian Mixture technique. Experimental results obtained in dynamic urban settings demonstrate the efficacy and tracking performance of the proposed approach.