Xiaotong Zhang

Smart pipes can be adopted as a solution to problems in water distribution systems. However, the real application of such a system is usually constrained by power delivery. In this article, a magnetohydrodynamic (MHD) energy harvester for low-power pipe instrumentation is developed. A theoretical model of the maximum power output containing parameters of water conductivity, flow velocity, magnetic flux density, and water channel volume is derived. To enhance the magnetic flux density, a magnetic concentrator is designed and carefully tuned to arrange the magnetic flux as we desire and magnify the magnetic flux density within the channels. A spiral flow diverter is proposed to reconfigure the original pipe flow pattern and divert the flow into the surrounding channels to enhance the flow velocity. After integrating the proposed improvements from different fields and globally optimizing the power output, a final design is prototyped and tested in the lab, which achieves a maximum power output of 87.47 nW with a 2-m/s pipe inlet velocity. To the best of our knowledge, this is the first MHD energy harvester for low-power sensor networks. Its great potential is demonstrated, and several potential enhancements to the power output are proposed and analyzed.

This paper focuses on problems associated with the deployment of automatic agents for last-mile delivery. We propose a framework and methodology to systematically evaluate and compare different hybrid strategies. Performance metrics in agent noise, delivery time, energy consumption, coverage rate, package throughput, and system costs are defined rigorously and modeled mathematically. Using the methodology, we conduct a case study in the city of Boston for four agent delivery strategies, including a hybrid strategy proposed in this paper. The proposed strategy utilizes available space in public transits’ cabins during off-peak hours to relocate the agent traveling start locations. Simulations and analyses show that hybrid strategies outperform the Agent-Only delivery strategy in terms of noise exposure, energy consumption, and coverage rate. The performance of hybrid strategies highly depends on the characteristics of the ground transportation methods accompanying agents. Thus, the methods of ground transportation should carefully be examined and selected for each case and strategy in real-world applications.

Perception plays a pivotal role in enhancing the functionality of autonomous agents. However, the intricate relationship between robotic perception metrics and actuation metrics remains unclear, leading to ambiguity in the development and fine-tuning of perception algorithms. In this paper, we introduce a methodology for quantifying this relationship, taking into account factors such as detection rate, detection quality, and latency. Furthermore, we introduce two novel perception metrics for Human-Robot Collaboration safety predicated upon basic perception metrics: Critical Collision Probability (CCP) and Average Collision Probability (ACP). To validate the utility of these metrics in facilitating algorithm development and tuning, we develop an attentive processing strategy that focuses exclusively on key input features. This approach significantly reduces computational time while preserving a similar level of accuracy. Experimental findings demonstrate that integrating this strategy into an object detector results in a notable maximum reduction of 30.09% in inference time and 26.53% in total time per frame. Additionally, the strategy lowers the CCP and ACP in a baseline model by 11.25% and 13.50%, respectively.

Human motion prediction is an essential step for efficient and safe human-robot collaboration. Current methods either purely rely on representing the human joints in some form of neural network-based architecture or use regression models offline to fit hyper-parameters in the hope of capturing a model encompassing human motion. While these methods provide good initial results, they are missing out on leveraging well-studied human body kinematic models as well as body and scene constraints which can help boost the efficacy of these prediction frameworks while also explicitly avoiding implausible human joint configurations. We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints in a Gaussian Process Regression (GPR) model to predict human motion over a set time horizon. This formulation is combined with an online context-aware constraints model to leverage task-dependent motions. It is tested on a human arm kinematic model and implemented on a human-robot collaborative setup with a UR5 robot arm to demonstrate the real-time capability of our approach. Simulations were also performed on datasets like HA4M and ANDY. The simulation and experimental results demonstrate considerable improvements in a Gaussian Process framework when these constraints are explicitly considered.