Optimal maintenance of sensor nodes in a Wireless Rechargeable Sensor Network (WRSN) requires effective scheduling of power delivery vehicles by solving the Charging Scheduling Problem (CSP). Deploying Unmanned Aerial Vehicles (UAVs) as mobile chargers has emerged as a promising solution due to their mobility and flexibility. The CSP can be formulated as a Mixed-Integer Non-Linear Programming problem whose optimization objective is maximizing the recharged energy of sensor nodes within the UAV battery constraint. While many studies have demonstrated satisfactory performance of heuristic algorithms in addressing specific routing problems, few studies explore online updating (i.e., mission re-planning `on the fly') in the CSP context. Here we present a new offline and online mission planner leveraging a first-principles power consumption model that uses real-time state information and environmental information. The planner, namely Rapid Online Metaheuristic-based Planner (ROMP), supplements solutions from a Guided Local Search (GLS) with our Context-aware Black Hole Algorithm. Our results demonstrate that ROMP outperforms GLS in most cases tested. We developed and proposed FastROMP to speed up the online mission (re-)planning algorithm by introducing a new online adjustment operator that uses the latest state information as input, eliminating the need for re-initialization. FastROMP not only provides a better quality route, but it also significantly reduces computational time. The reduction ranges from 39.57% in sparse deployment to 93.3% in denser deployments.

Rescue missions in mountain environments are hardly achievable by standard legged robots-because of the high slopes-or by flying robots-because of limited payload capacity. We present a concept for a rope-aided climbing robot which can negotiate up-to-vertical slopes and carry heavy payloads. The robot is attached to the mountain through a rope, and it is equipped with a leg to push against the mountain and initiate jumping maneuvers. Between jumps, a hoist is used to wind/unwind the rope to move vertically and affect the lateral motion. This simple (yet effective) two-fold actuation allows the system to achieve high safety and energy efficiency. Indeed, the rope prevents the robot from falling while compensating for most of its weight, drastically reducing the effort required by the leg actuator. We also present an optimal control strategy to generate point-to-point trajectories overcoming an obstacle. We achieve fast computation time (<1 s) thanks to the use of a custom simplified robot model. We validated the generated optimal movements in Gazebo simulations with a complete robot model with a < 5% error on a 16 m long jump, showing the effectiveness of the proposed approach, and confirming the interest of our concept. Finally, we performed a reachability analysis showing that the region of achievable targets is strongly affected by the friction properties of the foot-wall contact.

In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters may be a priori unknown but can be estimated from historical data. A typical predict-then-optimize approach separates predictions and optimization into two stages. Recently, end-to-end predict-then-optimize has become an attractive alternative. In this work, we present the PyEPO package, a PyTorchbased end-to-end predict-then-optimize library in Python. To the best of our knowledge, PyEPO (pronounced like pineapple with a silent "n") is the first such generic tool for linear and integer programming with predicted objective function coefficients. It provides four base algorithms: a convex surrogate loss function from the seminal work of Elmachtoub and Grigas [16], a differentiable black-box solver approach of Pogancic et al. [35], and two differentiable perturbation-based methods from Berthet et al. [6]. PyEPO provides a simple interface for the definition of new optimization problems, the implementation of state-of-the-art predict-then-optimize training algorithms, the use of custom neural network architectures, and the comparison of end-to-end approaches with the two-stage approach. PyEPO enables us to conduct a comprehensive set of experiments comparing a number of end-to-end and two-stage approaches along axes such as prediction accuracy, decision quality, and running time on problems such as Shortest Path, Multiple Knapsack, and the Traveling Salesperson Problem. We discuss some empirical insights from these experiments, which could guide future research. PyEPO and its documentation are available at https://github.com/khalil-research/PyEPO.

If you're familiar with the helpful ChatGPT chatbot, which is based on the powerful natural language processing system GPT LLM developed by OpenAI, you might be surprised to hear that there's another chatbot with opposite intentions. ChaosGPT is an AI chatbot that's malicious, hostile, and wants to conquer the world. In this blog post, we'll explore what sets ChaosGPT apart from other chatbots and why it's considered a threat to humanity and the world. Let's dive in and see whether this AI chatbot has what it takes to cause real trouble in any capacity. Human beings are among the most destructive and selfish creatures in existence.

Everstream Analytics, the global supply chain insights and risk analytics company, announced that Vestas, a world leader in sustainable energy solutions, has selected Everstream to reduce risk exposure and increase sustainability performance within its global value chain. "We are thrilled to support Vestas' continued commitment to developing and deploying sustainable energy solutions around the world." Vestas sought a comprehensive supply chain solution capable of increasing transparency and providing real-time monitoring spanning weather, geo- and socio-political, labor, workforce, production, transportation, and sustainability risks across planning, procurement, and logistics. Everstream's multi-tier supply chain discovery and end-to-end risk management solution was chosen for its unsurpassed ability to quickly expose vulnerabilities and reduce disruption across Vestas' complex global supplier base. "Even a small disruption at a sub-tier level can have an impact on our ability to serve our clients," said Dieter Dehoorne, Chief Procurement Officer, at Vestas.

Artificial intelligence (AI) has rapidly emerged as a disruptive technology with the potential to transform industries and societies. It is a branch of computer science that involves creating algorithms and models that can perform tasks that typically require human intelligence, such as natural language processing, image recognition, and decision-making. Leveraging AI can provide numerous benefits, including increased efficiency, accuracy, and cost savings. In this article, we will discuss how to leverage artificial intelligence in various domains and industries. Before diving into the specifics of leveraging AI, it's essential to understand the different types of AI.

Group of Seven (G7) finance leaders have pledged to take action to maintain the stability of the global financial system after recent banking turmoil and to give low- and middle-income countries a bigger role in diversifying supply chains to make them more resilient. Their communique did not mention China by name but the supply-chain language fits in with "friend-shoring" efforts by industrial democracies to work with each other to become less reliant on the Asian manufacturing powerhouse for battery minerals, semiconductors and other strategic goods. "We commit to jointly empowering low- and middle-income countries to play bigger roles in supply chains through mutually beneficial cooperation by combining finance, knowledge, and partnership, which will help contribute to sustainable development and enhance supply chain resilience globally," the G7 finance ministers and central bank governors said in the statement on Wednesday. The finance chiefs of G7 nations – Canada, France, Germany, Italy, Japan, the United Kingdom and the United States – met on the sidelines of International Monetary Fund (IMF) and World Bank meetings in Washington, DC. They said they discussed recent financial sector developments after the failure of two United States banks and the forced sale of troubled global lender Credit Suisse. "We will continue to closely monitor financial sector developments and stand ready to take appropriate actions to maintain the stability and resilience of the global financial system," the G7 finance leaders said.

Soft-sensors are gaining popularity due to their ability to provide estimates of key process variables with little intervention required on the asset and at a low cost. In oil and gas production, virtual flow metering (VFM) is a popular soft-sensor that attempts to estimate multiphase flow rates in real time. VFMs are based on models, and these models require calibration. The calibration is highly dependent on the application, both due to the great diversity of the models, and in the available measurements. The most accurate calibration is achieved by careful tuning of the VFM parameters to well tests, but this can be work intensive, and not all wells have frequent well test data available. This paper presents a calibration method based on the measurement provided by the production separator, and the assumption that the observed flow should be equal to the sum of flow rates from each individual well. This allows us to jointly calibrate the VFMs continuously. The method applies Sequential Monte Carlo (SMC) to infer a tuning factor and the flow composition for each well. The method is tested on a case with ten wells, using both synthetic and real data. The results are promising and the method is able to provide reasonable estimates of the parameters without relying on well tests. However, some challenges are identified and discussed, particularly related to the process noise and how to manage varying data quality.

Physics simulation is ubiquitous in robotics. Whether in model-based approaches (e.g., trajectory optimization), or model-free algorithms (e.g., reinforcement learning), physics simulators are a central component of modern control pipelines in robotics. Over the past decades, several robotic simulators have been developed, each with dedicated contact modeling assumptions and algorithmic solutions. In this article, we survey the main contact models and the associated numerical methods commonly used in robotics for simulating advanced robot motions involving contact interactions. In particular, we recall the physical laws underlying contacts and friction (i.e., Signorini condition, Coulomb's law, and the maximum dissipation principle), and how they are transcribed in current simulators. For each physics engine, we expose their inherent physical relaxations along with their limitations due to the numerical techniques employed. Based on our study, we propose theoretically grounded quantitative criteria on which we build benchmarks assessing both the physical and computational aspects of simulation. We support our work with an open-source and efficient C++ implementation of the existing algorithmic variations. Our results demonstrate that some approximations or algorithms commonly used in robotics can severely widen the reality gap and impact target applications. We hope this work will help motivate the development of new contact models, contact solvers, and robotic simulators in general, at the root of recent progress in motion generation in robotics.

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.