Robots are now pervasive, leveraging their automation capabilities to assist humans across a diverse range of tasks. Nevertheless, end-users may have a limited understanding of the robot’s operation and typically assume a passive role when interacting with the robot performing a particular task. In this study, we address the critical need for effective explainability in human-robot interaction. By comparing different methods of explaining robotic scenario information to end-users, the proposed methodologies use a labelled property graph-based chatbot that adheres to the IEEE Robotics Ontology Standards. In this study, we designed two virtual robotic scenarios and simulated their information flow using the Robot Operating System. A between-subjects experiment was conducted where participants engaged with the system through various interaction methods to understand the two scenarios. These methods included real-time Linux Command Line Interface outputs, querying a chatbot, exploring knowledge graphs, or a combination of chatbot and knowledge graphs. The study findings suggest that both the knowledge graphs and the chatbot significantly enhance the system’s explainability compared to a simple Linux terminal information output. Moreover, utilizing knowledge graphs alongside the chatbot has received better subjective evaluations concerning metrics such as clarity, usability, and robustness. This research made contributions towards the development of standardised labelled property graphs for representing scenario information in language-based human-robot interaction. The experiment design and evaluations also provided a solution for assessing the explainability of task-oriented dialogue systems both subjectively and objectively.

The problem of diminished unambiguous target velocity interval induced by the time-division-multiplex mode (TDM) of multiple-input-multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) automotive radar has been explored. A novel MIMO antenna array activation mode and a parametric approach for Doppler de-aliasing based on a two-step cross-entropy optimization are proposed. The TDM Doppler signal model has been derived, and a novel two-step cost function is proposed to achieve robust and efficient estimation. In contrast to the state-of-the-art method, the method proposed does not need multiple overlapped antennas and can resolve multiple targets in the same range and Doppler bins. The proposed method has been verified with numerical simulations with different parameter settings, as well as experimental data from a radar target simulator.

The role of radar for building situation awareness in (semi)autonomous vehicles is severely restricted by its low angular resolution. The physical size of the radar, which determines its antenna aperture size and thus the radar angular resolution, is often a subject of stringent limitations to physically fit the system in the vehicles. Multiple input multiple output systems are used to increase the achievable angular resolution, and these are often combined in the literature with algorithms inspired by synthetic aperture radar techniques that exploit the velocity of the vehicle itself for finer resolution. Some of the most common approaches are reviewed, in this context, with a specific focus on challenges for the implementation of data collected in real driving scenarios. Key experimental results using representative algorithms and driving data collected in the city of Delft, The Netherlands, are presented and discussed.

A feasibility study to detect the respiratory rate and heart rate of primates using non-invasive contactless radar sensors and a dedicated processing pipeline is performed. The proposed approach is validated using measurement data from an individual bonobo, simultaneously collected by two different types of radar sensors (pulsed Ultra Wide Band and FMCW, operating at different frequencies). The results show that it is feasible to infer both respiration and heart rate data from the radar sensors.

The problem of 3D ego-motion velocity estimation using multichannel Frequency Modulated Continuous Wave (FM CW) radar sensors has been studied. Special attention is given to presence of moving targets in the scene. These targets are first distinguished by the difference between the measured Doppler, and the Doppler calculated with an initial rough estimation of the vehicle ego-velocity. Then, an iterative algorithm is proposed to reduce the influence of the moving targets in the ego-motion estimation procedure, thus improving the overall accuracy. The performance of the proposed algorithm is compared with state-of-the-art alternatives based on simulated data, and superior performance has been demonstrated.

One of the key problems in automotive radar is its limited cross-range resolution, despite many approaches developed to address this. Conventional high-resolution algorithms in synthetic aperture radar (SAR) can provide good resolution imaging ability but suffer from high computational costs. In this paper, a computationally efficient high-resolution imaging algorithm is proposed, easing its potential implementation for higher throughput. The proposed approach is verified with experimental data of realistic driving scenarios.

This paper presents an approach based on Doppler beam sharpening (DBS) to enhance the resolution of multiple ‘dynamic’ targets in automotive driving scenarios. The ambiguity inherent to the forward-looking DBS and the coupling between azimuth and elevation angles are jointly addressed with the proposed method. Demonstrated experimental results show the feasibility of the proposed technique in automotive radar sensing.

The problem of high-resolution direction-of-arrival (DOA) estimation based on a limited amount of snapshots in automotive multiple-input multiple-output (MIMO) radar has been studied. The number of snapshots is restricted to minimize target spread/migration in range and/or Doppler domains. A computationally efficient approach for side-looking arrays is developed, which combines the generation of motion-enhanced snapshots and MIMO technology, thus exploiting the movement of the vehicle and the spatial diversity of the transmit and receive antennas. Due to motion, a larger virtual aperture is obtained and the angular resolution is boosted, achieving the separation of targets that the traditional MIMO approach cannot discriminate, as well as better results than with other single snapshot DOA estimation techniques. Algorithm performance has been studied in simulations, and possible limitations have been discussed. In addition, the method has been verified experimentally with pointlike and extended targets, and good agreement between simulations and experimental results has been observed.

The MIMO ambiguity functions of the binary phase codes as applied to phase-coded frequency modulated continuous waveform (PC-FMCW) are studied. The range-angle performance of the PC-FMCW with different code families is investigated and compared with the phase modulated continuous waveform (PMCW). An advantage of the PC-FMCW ambiguity function over the PMCW one is demonstrated in terms of the range resolution and sidelobe level for the same types of codes.

A direction-finding approach for arrays with a limited number of antenna elements has been investigated. A method based on the harmonic analysis of the received signal has been proposed to solve it. The angle estimation accuracy has been improved by angle searching and peak detection. The proposed method is theoretically described and numerical simulations are provided to verify its effectiveness. Compared with classical direction-finding methods with limited antenna elements, significant improvements have been demonstrated.

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.