This paper addresses the problem of satellite inspection, where one or more satellites (inspectors) are tasked with imaging or inspecting a resident space object (RSO) due to potential malfunctions or anomalies. Inspection strategies are often reduced to a discretized action space with predefined waypoints, facilitating tractability in both classical optimization and machine learning based approaches. However, this discretization can lead to suboptimal guidance in certain scenarios. This study presents a comparative simulation to explore the tradeoffs of passive versus active strategies in multi-agent missions. Key factors considered include RSO dynamic mode, state uncertainty, unmodeled entrance criteria, and inspector motion types. The evaluation is conducted with a focus on fuel utilization and surface coverage. Building on a Monte-Carlo based evaluator of passive strategies and a reinforcement learning framework for training active inspection policies, this study investigates conditions under which passive strategies, such as Natural Motion Circumnavigation (NMC), may perform comparably to active strategies like Reinforcement Learning based waypoint transfers.

Real-world problems often involve complex objective structures that resist distillation into reinforcement learning environments with a single objective. Operation costs must be balanced with multi-dimensional task performance and end-states' effects on future availability, all while ensuring safety for other agents in the environment and the reinforcement learning agent itself. System redundancy through secondary backup controllers has proven to be an effective method to ensure safety in real-world applications where the risk of violating constraints is extremely high. In this work, we investigate the utility of a stacked, continuous-control variation of universal successor feature approximation (USFA) adapted for soft actor-critic (SAC) and coupled with a suite of secondary safety controllers, which we call stacked USFA for safety (SUSFAS). Our method improves performance on secondary objectives compared to SAC baselines using an intervening secondary controller such as a runtime assurance (RTA) controller.

Fuel optimization of diesel and petrol vehicles within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the driving behaviour style. In this study, we developed an Explainable Boosting Machine (EBM) model to predict fuel consumption of different types of industrial vehicles, using real-world data collected from 2020 to 2021. This Machine Learning model also explains the relationship between the input factors and fuel consumption, quantifying the individual contribution of each one of them. The explanations provided by the model are compared with domain knowledge in order to see if they are aligned. The results show that the 70% of the categories associated to the fuel-factors are similar to the previous literature. With the EBM algorithm, we estimate that optimizing driving behaviour decreases fuel consumption between 12% and 15% in a large fleet (more than 1000 vehicles).

Improving the fuel efficiency of vehicles is imperative to reduce costs and protect the environment. While the efficient engine and vehicle designs, as well as intelligent route planning, are well-known solutions to enhance the fuel efficiency, research has also demonstrated that the adoption of fuel-efficient driving behaviors could lead to further savings. In this work, we propose a novel framework to promote fuel-efficient driving behaviors through real-time automatic monitoring and driver feedback. In this framework, a random-forest based classification model developed using historical data to identifies fuel-inefficient driving behaviors. The classifier considers driver-dependent parameters such as speed and acceleration/deceleration pattern, as well as environmental parameters such as traffic, road topography, and weather to evaluate the fuel efficiency of one-minute driving events. When an inefficient driving action is detected, a fuzzy logic inference system is used to determine what the driver should do to maintain fuel-efficient driving behavior. The decided action is then conveyed to the driver via a smartphone in a non-intrusive manner. Using a dataset from a long-distance bus, we demonstrate that the proposed classification model yields an accuracy of 85.2% while increasing the fuel efficiency up to 16.4%.

One of the new impressive features that will come along with Ford's 2019 Edge and Edge ST is the use of artificial intelligence aimed at improving traction and fuel usage in the SUV. The Dearborn, Michigan-based automaker says the Edge SUV will come with its new "all-wheel-drive disconnect" which will switch automatically between two-wheel and all-wheel-drive "in a fraction of a second." Ford says the SUV set to hit dealers later in September, will use a "form of artificial intelligence to sift through information like wheel slip, road conditions, vehicle speed, windshield wiper usage and outside temperature." As for how the Edge will be able to improve fuel usage, the automaker says it will use features such as active transmission warm-up, deceleration fuel shutoff and exhaust gas recirculation. Scott Beiring, Ford driveline applications supervisor, said in a news release that the shifting between two-wheel and all-wheel-drive "needs to be fast and seamless enough that the customer doesn't know it is happening. The 2019 Edge and Edge ST SUV will use what the automaker calls "fuzzy logic," and that it can detect the need to engage or disengage within 10 milliseconds. "Fuzzy logic refers to the algorithm," Beiring said in the release. "It's like you or I determining what to wear based on reading a weather forecast, where we're going, the time of year and looking outside.

Technology is a wonderful thing; with those small glimpses of the future from sci-fi films are now realities. We have Artificial Intelligence (AI) managing our homes and businesses and automation software streamlining every process. In fact, there is a gadget out there that can help with almost every bit of our daily lives. However, a lot of technology runs on electricity, a need which presents endless issues for those concerned with global warming, climate change and all things green. This is where the use of smart tech or green tech comes in.