Forklift operator Safety & Productivity: A Review of Current Research and Future Directions

This paper explores the challenges warehouse operations face in meeting customer demand and managing rapid growth, focusing on the role of forklift operators. Despite their versatility and importance in warehouse operations, forklifts are also a significant safety concern, with thousands of work-related injuries and fatalities recorded annually. The paper presents findings from a literature review on the factors related to forklift operator safety and efficiency, including energy consumption, training, the Internet of Things, ergonomics, and human factors such as worker fatigue. The study aims to develop a methodological framework to enhance the efficiency and safety of forklift operators by integrating these factors. The research questions addressed in the study are related to the techniques used to measure forklift operator safety and productivity. The paper highlights the need for prioritizing preventative measures, such as training, facility layout, and technological advancements, to reduce the likelihood of injuries from this type of equipment and provides a framework to build the human digital twin of the forklift operator

Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving

Autonomy, robotics, and Artificial Intelligence (AI) are among the main defining themes of next-generation societies. Of the most important applications of said technologies is driving automation which spans from different Advanced Driver Assistance Systems (ADAS) to full self-driving vehicles. Driving automation is promising to reduce accidents, increase safety, and increase access to mobility for more people such as the elderly and the handicapped. However, one of the main challenges facing autonomous vehicles is robust perception which can enable safe interaction and decision making. With so many sensors to perceive the environment, each with its own capabilities and limitations, vision is by far one of the main sensing modalities. Cameras are cheap and can provide rich information of the observed scene. Therefore, this dissertation develops a set of visual perception algorithms with a focus on autonomous driving as the target application area. This dissertation starts by addressing the problem of real-time motion estimation of an agent using only the visual input from a camera attached to it, a problem known as visual odometry. The visual odometry algorithm can achieve low drift rates over long-traveled distances. This is made possible through the innovative local mapping approach used. This visual odometry algorithm was then combined with my multi-object detection and tracking system. The tracking system operates in a tracking-by-detection paradigm where an object detector based on convolution neural networks (CNNs) is used. Therefore, the combined system can detect and track other traffic participants both in image domain and in 3D world frame while simultaneously estimating vehicle motion. This is a necessary requirement for obstacle avoidance and safe navigation. Finally, the operational range of traditional monocular cameras was expanded with the capability to infer depth and thus replace stereo and RGB-D cameras. This is accomplished through a single-stream convolution neural network which can output both depth prediction and semantic segmentation. Semantic segmentation is the process of classifying each pixel in an image and is an important step toward scene understanding. Literature survey, algorithms descriptions, and comprehensive evaluations on real-world datasets are presented.Ph.D.College of Engineering & Computer ScienceUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/153989/1/Mohamed Aladem Final Dissertation.pdfDescription of Mohamed Aladem Final Dissertation.pdf : Dissertatio

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

Implementation of an Autonomous Impulse Response Measurement System

Data collection is crucial for researchers, as it can provide important insights for describing phenomena. In acoustics, acoustic phenomena are characterized by Room Impulse Responses (RIRs) occurring when sound propagates in a room. Room impulse responses are needed in vast quantities for various reasons, including the prediction of acoustical parameters and the rendering of virtual acoustical spaces. Recently, mobile robots navigating within indoor spaces have become increasingly used to acquire information about its environment. However, little research has attempted to utilize robots for the collection of room acoustic data. This thesis presents an adaptable automated system to measure room impulse responses in multi-room environments, using mobile and stationary measurement platforms. The system, known as Autonomous Impulse Response Measurement System (AIRMS), is divided into two stages: data collection and post-processing. To automate data collection, a mobile robotic platform was developed to perform acoustic measurements within a room. The robot was equipped with spatial microphones, multiple loudspeakers and an indoor localization system, which reported real time location of the robot. Additionally, stationary platforms were installed in specific locations inside and outside the room. The mobile and stationary platforms wirelessly communicated with one another to perform the acoustical tests systematically. Since a major requirement of the system is adaptability, researchers can define the elements of the system according to their needs, including the mounted equipment and the number of platforms. Post-processing included extraction of sine sweeps and the calculation of impulse responses. Extraction of the sine sweeps refers to the process of framing every acoustical test signal from the raw recordings. These signals are then processed to calculate the room impulse responses. The automatically collected information was complemented with manually produced data, which included rendering of a 3D model of the room, a panoramic picture. The performance of the system was tested under two conditions: a single-room and a multiroom setting. Room impulse responses were calculated for each of the test conditions, representing typical characteristics of the signals and showing the effects of proximity from sources and receivers, as well as the presence of boundaries. This prototype produces RIR measurements in a fast and reliable manner. Although some shortcomings were noted in the compact loudspeakers used to produce the sine sweeps and the accuracy of the indoor localization system, the proposed autonomous measurement system yielded reasonable results. Future work could expand the amount of impulse response measurements in order to further refine the artificial intelligence algorithms

Sixth International Joint Conference on Electronic Voting E-Vote-ID 2021. 5-8 October 2021

This volume contains papers presented at E-Vote-ID 2021, the Sixth International Joint Conference on Electronic Voting, held during October 5-8, 2021. Due to the extraordinary situation provoked by Covid-19 Pandemic, the conference is held online for second consecutive edition, instead of in the traditional venue in Bregenz, Austria. E-Vote-ID Conference resulted from the merging of EVOTE and Vote-ID and counting up to 17 years since the _rst E-Vote conference in Austria. Since that conference in 2004, over 1000 experts have attended the venue, including scholars, practitioners, authorities, electoral managers, vendors, and PhD Students. The conference collected the most relevant debates on the development of Electronic Voting, from aspects relating to security and usability through to practical experiences and applications of voting systems, also including legal, social or political aspects, amongst others; turning out to be an important global referent in relation to this issue. Also, this year, the conference consisted of: · Security, Usability and Technical Issues Track · Administrative, Legal, Political and Social Issues Track · Election and Practical Experiences Track · PhD Colloquium, Poster and Demo Session on the day before the conference E-VOTE-ID 2021 received 49 submissions, being, each of them, reviewed by 3 to 5 program committee members, using a double blind review process. As a result, 27 papers were accepted for its presentation in the conference. The selected papers cover a wide range of topics connected with electronic voting, including experiences and revisions of the real uses of E-voting systems and corresponding processes in elections. We would also like to thank the German Informatics Society (Gesellschaft für Informatik) with its ECOM working group and KASTEL for their partnership over many years. Further we would like to thank the Swiss Federal Chancellery and the Regional Government of Vorarlberg for their kind support. EVote- ID 2021 conference is kindly supported through European Union's Horizon 2020 projects ECEPS (grant agreement 857622) and mGov4EU (grant agreement 959072). Special thanks go to the members of the international program committee for their hard work in reviewing, discussing, and shepherding papers. They ensured the high quality of these proceedings with their knowledge and experience

Planning, Estimation and Control for Mobile Robot Localization with Application to Long-Term Autonomy

There may arise two kinds of challenges in the problem of mobile robot localization; (i) a robot may have an a priori map of its environment, in which case the localization problem boils down to estimating the robot pose relative to a global frame or (ii) no a priori map information is given, in which case a robot may have to estimate a model of its environment and localize within it. In the case of a known map, simultaneous planning while localizing is a crucial ability for operating under uncertainty. We first address this problem by designing a method to dynamically replan while the localization uncertainty or environment map is updated. Extensive simulations are conducted to compare the proposed method with the performance of FIRM (Feedback-based Information RoadMap). However, a shortcoming of this method is its reliance on a Gaussian assumption for the Probability Density Function (pdf) on the robot state. This assumption may be violated during autonomous operation when a robot visits parts of the environment which appear similar to others. Such situations lead to ambiguity in data association between what is seen and the robot’s map leading to a non-Gaussian pdf on the robot state. We address this challenge by developing a motion planning method to resolve situations where ambiguous data associations result in a multimodal hypothesis on the robot state. A Receding Horizon approach is developed, to plan actions that sequentially disambiguate a multimodal belief to achieve tight localization on the correct pose in finite time. In our method, disambiguation is achieved through active data associations by picking target states in the map which allow distinctive information to be observed for each belief mode and creating local feedback controllers to visit the targets. Experiments are conducted for a kidnapped physical ground robot operating in an artificial maze-like environment. The hardest challenge arises when no a priori information is present. In longterm tasks where a robot must drive for long durations before closing loops, our goal is to minimize the localization error growth rate such that; (i) accurate data associations can be made for loop closure, or (ii) in cases where loop closure is not possible, the localization error stays limited within some desired bounds. We analyze this problem and show that accurate heading estimation is key to limiting localization error drift. We make three contributions in this domain. First we present a method for accurate long-term localization using absolute orientation measurements and analyze the underlying structure of the SLAM problem and how it is affected by unbiased heading measurements. We show that consistent estimates over a 100km trajectory are possible and that the error growth rate can be controlled with active data acquisition. Then we study the more general problem when orientation measurements may not be present and develop a SLAM technique to separate orientation and position estimation. We show that our method’s accuracy degrades gracefully compared to the standard non-linear optimization based SLAM approach and avoids catastrophic failures which may occur due a bad initial guess in non-linear optimization. Finally we take our understanding of orientation sensing into the physical world and demonstrate a 2D SLAM technique that leverages absolute orientation sensing based on naturally occurring structural cues. We demonstrate our method using both high-fidelity simulations and a real-world experiment in a 66, 000 square foot warehouse. Empirical studies show that maps generated by our approach never suffer catastrophic failure, whereas existing scan matching based SLAM methods fail ≈ 50% of the time

Electronic Voting: 6th International Joint Conference, E-Vote-ID 2021, Virtual Event, October 5–8, 2021: proceedings

This volume contains the papers presented at E-Vote-ID 2021, the Sixth International Joint Conference on Electronic Voting, held during October 5–8, 2021. Due to the extraordinary situation brought about by the COVID-19, the conference was held online for the second consecutive edition, instead of in the traditional venue in Bregenz, Austria. The E-Vote-ID conference is the result of the merger of the EVOTE and Vote-ID conferences, with first EVOTE conference taking place 17 years ago in Austria. Since that conference in 2004, over 1000 experts have attended the venue, including scholars, practitioners, authorities, electoral managers, vendors, and PhD students. The conference focuses on the most relevant debates on the development of electronic voting, from aspects relating to security and usability through to practical experiences and applications of voting systems, also including legal, social, or political aspects, amongst others, and has turned out to be an important global referent in relation to this issue

The datafied welfare state: a perspective from the UK

The crisis emerging from the COVID-19 pandemic has elevated the relevance of the welfare state as well as the role of platforms and data infrastructures across key areas of public and social life. Whilst the crisis shed light on the ways in which these might intersect, the turn to data-driven systems in public administration has been a prominent development in several countries for quite some time. In this chapter I focus on the UK as a pertinent example of key trends at the intersection of technological infrastructures and the welfare state. In particular, using developments in UK welfare sectors as a lens, I advance a two-part argument about the ways in which data infrastructures are transforming state-citizen relations through on the one hand advancing an actuarial logic based on personalised risk and the individualisation of social problems (what I refer to as responsibilisation) and, on the other, entrenching a dependency on an economic model that perpetuates the circulation of data accumulation (what I refer to as rentierism). These mechanisms, I argue, fundamentally shift the 'matrix of social power' that made the modern welfare state possible and position questions of data infrastructures as a core component of how we need to understand social change. "We are witnessing the gradual disappearance of the postwar British welfare stat