Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe deterministically. In this paper, we propose DetMCVI, an adaptation of the Monte Carlo Value Iteration (MCVI) algorithm for DetPOMDPs, which builds policies in the form of finite-state controllers (FSCs). DetMCVI solves large problems with a high success rate, outperforming existing baselines for DetPOMDPs. We also verify the performance of the algorithm in a real-world mobile robot forest mapping scenario.

This paper presents a detailed user study aimed at experimentally comparing the experience levels within bi-lateral teleoperation. The primary objective is to elucidate the key performance metrics that can effectively evaluate the competency level of human operators. Existing methodologies typically focus on the quantitative psychological evaluation of human-in-the-loop systems rather than operator performance. In our experimental study, six novice and four professional operators participated in various telerobotic activities. Various parameters, including task completion duration, errors, remote manipulators' motion, and subjects' gaze information, were captured. Subsequently, the measured performance parameters across all subjects were compared with respect to their level of proficiency through statistical analyses. The results indicate that tasks were performed more quickly by experienced operators, fewer mistakes were made, and remote manipulators were operated more smoothly (e.g., fewer jerks and better maintenance within the centre of the workspace). Additionally, better compensation for the lack of depth perception was demonstrated by experienced operators through effective scanning of multiple viewpoints.

Wireless Sensor and Robot Networks (WSRNs) are heterogeneous collections of sensor nodes and robotic vehicles that communicate wirelessly. In the last decade many research studies have attempted to address the challenging trade-offs of Wireless Sensor Networks (WSNs) that arise due to their resource limitations, however, much work is still to be done. A recent trend is to merge different subclasses of cyber-physical systems together to achieve the desired performance goals by benefiting from their heterogeneity. This paper presents a bio-inspired robot guidance technique that is used to improve network coverage, increase service availability and minimise energy consumption of WSNs using robotic agents on vehicles. We explore the performance consequences of the Pheromone Signalling-based Load Balancing (PS) Robot Guidance on an abstract level simulator which provides a system perspective. The effectiveness of the algorithm is evaluated with different network topologies and investigated on various scenarios. Simulated experimental results on mesh and sparse topologies validate that robot guidance based on PS increases network lifetime.