Motion planning for autonomous vehicles often requires satisfying multiple conditionally conflicting specifications. In situations where not all specifications can be met simultaneously, minimum-violation motion planning maintains system operation by minimizing violations of specifications in accordance with their priorities. Signal temporal logic (STL) provides a formal language for rigorously defining these specifications and enables the quantitative evaluation of their violations. However, a total ordering of specifications yields a lexicographic optimization problem, which is typically computationally expensive to solve using standard methods. We address this problem by discretizing the multi-objective lexicographic optimization problem via non-uniform quantization and transforming it into a single-objective optimization problem using bit-shifting. Specifically, we extend a deterministic model predictive path integral (MPPI) solver to efficiently solve optimization problems without quadratic input cost. Additionally, a novel predicate robustness measure that combines spatial and temporal violations is introduced. Our results show that the proposed method offers an interpretable and scalable solution for lexicographic STL minimum-violation motion planning using a single-objective solver.
The maritime sector is undergoing a disruptive technological change driven by three main factors: autonomy, decarbonization, and digital transformation. Addressing these factors necessitates a reassessment of inland vessel operations. This paper presents the design and development of a decision support system for ferry operations based on a shrinking-horizon optimal control framework. The problem formulation incorporates a mathematical model of the ferry's dynamics and environmental disturbances, specifically water currents and wind, which can significantly influence the dynamics. Real-world data and illustrative scenarios demonstrate the potential of the proposed system to effectively support ferry crews by providing real-time guidance. This enables enhanced operational efficiency while maintaining predefined maneuver durations. The findings suggest that optimal control applications hold substantial promise for advancing future ferry operations on inland waters. A video of the real-world ferry MS Insel Mainau operating on Lake Constance is available at: https://youtu.be/i1MjCdbEQyE
This paper examines how subsidiaries in multinational corporations (MNCs) reposition themselves within global R D networks by leveraging managerial influence and locational advantages. Through a case study of a German automotive OEM’s R D operations in Japan and India, we analyze how subsidiaries with initially limited mandates develop strategies to expand their influence and secure a stronger role within the R D network. Building on Birkinshaw’s framework on charter evolution, this study extends the understanding of subsidiary mandate development by integrating issue-selling strategies and the role of managerial agency in driving mandate changes. We highlight how subsidiary managers actively shape mandate evolution through negotiation, strategic alignment, and internal lobbying. The findings indicate that while access to resources is a fundamental condition for mandate expansion, the ability to advocate for and legitimize a subsidiary’s strategic importance within the corporate structure plays an equally critical role. By comparing the approaches taken in Japan and India, this study provides insights into how subsidiaries can alter power dynamics within MNC networks and influence the distribution of innovation mandates.
The rising share of flexibilities combined with the recent development of machine learning techniques has the potential to create a sustainable and efficient low voltage grid infrastructure. The new operational challenges, as the increasing uncertainty of the production of renewable energy sources, have to be met by effective measures. To prevent or mitigate disturbances such as the overloading of the operating equipment or excessive voltage deviations, a predictive operation based on an autoregressive model using graph neural networks is presented in this paper. The autoregressive approach enables predictive grid operation over multiple time steps that influence each other sequentially. This approach was demonstrated on a real existing grid with high shares of renewable generation and flexible loads. The results presented in this study show a good performance on a large simulated data set applied on a real-world grid topology. The proposed method can prevent violations of the operational constraints of low voltage grids in real time by regulating controllable generation of PV systems and adjusting transformer tap changers, thereby protecting lines and substations in the grid from overload at an early stage.
Formulating the intended behavior of a dynamic system can be challenging. Signal temporal logic (STL) is frequently used for this purpose due to its suitability in formalizing comprehensible, modular, and versatile spatiotemporal specifications. Due to scaling issues with respect to the complexity of the specifications and the potential occurrence of non-differentiable terms, classical optimization methods often solve STL-based problems inefficiently. Smoothing and approximation techniques can alleviate these issues but require changing the optimization problem. This paper proposes a novel sampling-based method based on model predictive path integral control to solve optimal control problems with STL cost functions. We demonstrate the effectiveness of our method on benchmark motion planning problems and compare its performance with state-of-the-art methods. The results show that our method efficiently solves optimal control problems with STL costs.
