In this paper we tackle the problem of persistently covering a complex non-convex environment with a team of robots. We consider scenarios where the coverage quality of the environment deteriorates with time, requiring to constantly revisit every point. As a first step, our solution finds a partition of the environment where the amount of work for each robot, weighted by the importance of each point, is equal. This is achieved using a power diagram and finding an equitable partition through a provably correct distributed control law on the power weights. Compared to other existing partitioning methods, our solution considers a continuous environment formulation with non-convex obstacles. In the second step, each robot computes a graph that gathers sweep-like paths and covers its entire partition. At each planning time, the coverage error at the graph vertices is assigned as weights of the corresponding edges. Then, our solution is capable of efficiently finding the optimal open coverage path through the graph with respect to the coverage error per distance traversed. Simulation and experimental results are presented to support our proposal.

Control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based methods to systematically construct CBFs for static obstacle avoidance tasks between geometric shapes. In this work, we extend the application of diffOpt CBFs to perform dynamic obstacle avoidance tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can perform obstacle avoidance for dynamic geometric obstacles. Additionally, we show how to extend the TVCBF constraint to consider measurement noise and actuation limits. To demonstrate the efficacy of our proposed approach, we first compare its performance with a model predictive control based method and a circular CBF based method on a simulated dynamic obstacle avoidance task. Then, we demonstrate the performance of our proposed approach in experimental studies using a 7-degree-of-freedom Franka Research 3 robotic manipulator.

Quality scheduling in industrial job shops is crucial. Although neural networks excel in solving these problems, their limited explainability hinders their widespread industrial adoption. In this research, we introduce an innovative framework for solving job shop scheduling problems (JSSP). Our methodology leverages Petri nets to model the job shop, not only improving explainability but also enabling direct incorporation of raw data without the need to preprocess JSSP instances into disjunctive graphs. The Petri net, with its controlling capacities, also governs the automated components of the process, allowing the agent to focus on critical decision-making, particularly resource allocation. The integration of event-based control and action masking in our approach yields competitive performance on public test benchmarks. Comparative analyses across a wide spectrum of optimization solutions, including heuristics, metaheuristics, and learning-based algorithms, highlight the competitiveness of our approach in large instances and its superiority over all competitors in small to medium-sized scenarios. Ultimately, our approach not only demonstrates a robust ability to generalize across various instance sizes but also leverages the Petri net's graph nature to dynamically add job operations during the inference phase without the need for agent retraining, thereby enhancing flexibility.

This paper introduces the Smac Planner, an openly available search-based planning framework with multiple algorithm implementations including 2D-A*, Hybrid-A*, and State Lattice planners. This work is motivated by the lack of performant and available feasible planners for mobile and surface robotics research. This paper contains three main contributions. First, it briefly describes a minimal open-source software framework where search-based planners may be easily added. Further, this paper characterizes new variations on the feasible planners - dubbed Cost-Aware - specific to mobile roboticist's needs. This fills the gap of missing kinematically feasible implementations suitable for academic, extension, and deployed use. Finally, we provide baseline benchmarking against other standard planning frameworks. Smac Planner has further significance by becoming the standard open-source planning system within ROS 2's Nav2 framework which powers thousands of robots in research and industry.

Abstract--This paper proposes ISDC, a novel feedback-guided iterative system of difference constraints (SDC) scheduling algorithm for high-level synthesis (HLS). ISDC leverages subgraph extraction-based low-level feedback from downstream tools like logic synthesizers to iteratively refine HLS scheduling. Technical innovations include: (1) An enhanced SDC formulation that effectively integrates low-level feedback into the linear-programming (LP) problem; (2) A fanout and window-based subgraph extraction mechanism driving the feedback cycle; (3) A no-human-inloop ISDC flow compatible with a wide range of downstream tools and process design kits (PDKs). Evaluation shows that ISDC reduces register usage by 28.5% against an industrial-strength open-source HLS tool. Scheduling is one of the most important problems in highlevel synthesis (HLS) that partitions a computation graph into multiple clock cycles under the given timing and resource Figure 1: Post-synthesis STA vs. XLS-estimated critical path constraints. In 2006, Cong and Zhang [1] proposed a scheduling delay of 6912 different HLS design points.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The journey toward improved mental health by setting thoughtful and achievable goals can be a powerful strategy. Whether grappling with anxiety, depression, ADHD or other conditions, establishing personalized goals fosters a sense of direction, accomplishment and empowerment. Explore specific goals tailored to each condition, promoting overall mental well-being.

With the recent influx in demand for multi-robot systems throughout industry and academia, there is an increasing need for faster, robust, and generalizable path planning algorithms. Similarly, given the inherent connection between control algorithms and multi-robot path planners, there is in turn an increased demand for fast, efficient, and robust controllers. We propose a scalable joint path planning and control algorithm for multi-robot systems with constrained behaviours based on factor graph optimization. We demonstrate our algorithm on a series of hardware and simulated experiments. Our algorithm is consistently able to recover from disturbances and avoid obstacles while outperforming state-of-the-art methods in optimization time, path deviation, and inter-robot errors. See the code and supplementary video for experiments.

Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-generation (6G) wireless networks. Besides bringing significant benefits to various applications and services, SAGINs are envisioned to extend high-speed broadband coverage to remote areas, such as small towns or mining sites, or areas where terrestrial infrastructure cannot reach, such as airplanes or maritime use cases. However, due to the limited power and storage resources, as well as other constraints introduced by the design of terrestrial networks, SAGINs must be intelligently configured and controlled to satisfy the envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another critical enabler of 6G. Due to massive amounts of available data, AI has been leveraged to address pressing challenges of current and future wireless networks. By adding AI and facilitating the decision-making and prediction procedures, SAGINs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics. In this work, we aim to investigate the interplay of AI and SAGINs by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs. Specifically, we present a comprehensive overview of some potential applications of AI in SAGINs. We also cover open issues in employing AI and detail the contributions of SAGINs in the development of AI. Finally, we highlight some limitations of the existing research works and outline potential future research directions.

Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting from our review is that the true potential of LLMs unfolds when they are integrated with traditional symbolic planners, pointing towards a promising neuro-symbolic approach. This approach effectively combines the generative aspects of LLMs with the precision of classical planning methods. By synthesizing insights from existing literature, we underline the potential of this integration to address complex planning challenges. Our goal is to encourage the ICAPS community to recognize the complementary strengths of LLMs and symbolic planners, advocating for a direction in automated planning that leverages these synergistic capabilities to develop more advanced and intelligent planning systems.

In response to the gap in considering wind conditions in the bridge inspection using unmanned aerial vehicle (UAV) , this paper proposes a path planning method for UAVs that takes into account the influence of wind, based on the simulated annealing algorithm. The algorithm considers the wind factors, including the influence of different wind speeds and directions at the same time on the path planning of the UAV. Firstly, An environment model is constructed specifically for UAV bridge inspection, taking into account the various objective functions and constraint conditions of UAVs. A more sophisticated and precise mathematical model is then developed based on this environmental model to enable efficient and effective UAV path planning. Secondly, the bridge separation planning model is applied in a novel way, and a series of parameters are simulated, including the adjustment of the initial temperature value. The experimental results demonstrate that, compared with traditional local search algorithms, the proposed method achieves a cost reduction of 30.05\% and significantly improves effectiveness. Compared to path planning methods that do not consider wind factors, the proposed approach yields more realistic and practical results for UAV applications, as demonstrated by its improved effectiveness in simulations. These findings highlight the value of our method in facilitating more accurate and efficient UAV path planning in wind-prone environments.