Nonholonomic Research Articles

Adapting to discontinuous technological change from the perspective of knowledge management: a case study from the lighting industry in Lin’an, China

Purpose This study aims to answer the question of how incumbent firms cultivate dynamic capabilities through knowledge management so that they can efficiently adapt to the changing external environment. Design/methodology/approach This study adopts a case study approach and collects data through interviews and secondary public information on the lighting industry and two lighting firms in Lin'an, China. It qualitatively examines the challenges and strategic recommendations for incumbent firms in the context of discontinuous technological change from a knowledge management perspective. Findings Incumbent firms often face a variety of challenges when responding to discontinuous technological change. These challenges include identifying opportunities, overcoming path dependence and dealing with employee resistance to change. To overcome these difficulties, three strategies have been proposed to enhance the dynamic capabilities of incumbent firms through knowledge management: cross-border search helps firms improve their knowledge acquisition capabilities and better understand their environment to identify opportunities; building strategic leadership overcomes path dependence and improves knowledge integration capabilities; organizational learning deepens employees’ understanding of change and enhances organizational knowledge application capabilities. Research limitations/implications Previous research attributes a firm's ability to cope with discontinuous technological change solely to its general resources, which weakens the importance of knowledge management in this context. This study emphasizes the importance of knowledge as a crucial strategic resource in developing the essential dynamic capabilities for incumbent firms to cope with discontinuous technological change. Practical implications This study provides an in-depth analysis of incumbent firms' coping strategies in the new context of discontinuous technological change and further promotes cross-disciplinary research. Originality/value This study provides an in-depth analysis of coping strategies in the new context of discontinuous technological change, furthermore theoretically advancing the interdisciplinary research of firm transformation and knowledge management. Meanwhile, it is crucial to identify the preconditions for cultivating dynamic capabilities, especially from a knowledge-based view, which enhances the depth of knowledge management research.

Pricing Chinese Convertible Bonds with Learning-Based Monte Carlo Simulation Model

In this paper, we explore a novel model for pricing Chinese convertible bonds that seamlessly integrates machine learning techniques with traditional models. The least squares Monte Carlo (LSM) method is effective in handling multiple state variables and complex path dependencies through simple regression analysis. In our approach, we incorporate machine learning techniques, specifically support vector regression (SVR) and random forest (RF). By employing Bayesian optimization to fine-tune the random forest, we achieve improved predictive performance. This integration is designed to enhance the precision and predictive capabilities of convertible bond pricing. Through the use of simulated data and real data from the Chinese convertible bond market, the results demonstrate the superiority of our proposed model over the classic LSM, confirming its effectiveness. The development of a pricing model incorporating machine learning techniques proves particularly effective in addressing the complex pricing system of Chinese convertible bonds. Our study contributes to the body of knowledge on convertible bond pricing and further deepens the application of machine learning in the field in an integrated and supportive manner.

The End of Globalized Production? Supply-Chain Resilience, Technological Sovereignty, and Enduring Global Interdependencies in the Post-Pandemic Era

This article assesses the impact of the COVID-19 pandemic, the war in Ukraine and geopolitics on geographies of production. Criticizing simplified perspectives on globalized versus intraregional production, we stress the multi-scalarity, the role of industrial policies and sector-specific path dependencies in shaping global production. Based on expert interviews and policy and industry documents, our analysis focuses on the automotive, clothing, and electronics industries. Although concerns for resilience increasingly shape lead firms’ strategies, increased regionalization of production through re- or near-shoring is only one of several strategies. Where it does occur, it has been driven by state policies that tackle certain strategically important products, not production networks as a whole. Hence, while recent events exposed the vulnerabilities of global production, we do not observe deglobalization in the sense of a comprehensive retreat from globalized in favor of intraregional production. Nonetheless, state interventions that are geopolitically motivated and affect firms’ investment decisions have intensified particularly in the United States and the European Union.

Mathematical Modeling of Robotic Locomotion Systems

This article deals with the presentation of an alternative approach that uses methods of geometric mechanics, which allow one to see into the geometrical structure of the equations and can be useful not only for modeling but also during the design of symmetrical locomotion systems and their control and motion planning. These methods are based on extracting the symmetries of Lie groups from the locomotion system in order to simplify the resulting equations. In the second section, the special two-dimensional Euclidean group SE2 and its splitting into right and left actions are derived. The physical interpretation of the local group and spatial velocities is investigated, and by virtue of the fact that both of these velocities represent the same velocity from a physical point of view, the dependence between them can be found by means of the adjoint action. The third section is devoted to the modeling and analysis of the planar locomotion of the symmetrical serpentine robot; the positions and local group velocities of its links are derived, the vector fields for the local connections are given, and the trajectories of the individual variables in the lateral movement of the kinematic snake are shown. At the end of the article, the overall benefits of the scientific study are summarized, as is the comparison of the results from the simulation phase, while the most significant novelty compared to alternative publications in the field can be considered the realization of this study with a description of the relevant methodology at a detailed level; that is, the locomotion results confirm the suitability of the use of geometric mechanics for these symmetrical locomotion systems with nonholonomic constraints.

The Ukraine War as a Driver of Intraparty Conflict: Germany's Left Party and the AfD

ABSTRACT The two-year anniversary of Russia’s invasion of Ukraine affords an occasion to examine why the war has produced such deep rifts within the Left Party and the AfD. Drawing upon party documents, parliamentary proceedings, electoral and polling data and secondary literature, this article examines why Russia’s war against Ukraine proved so disruptive to both parties and explores the implications of these intraparty conflicts. Germany’s domestic politics of the Ukraine War matter given Berlin’s central role in backing the Kyiv government. This article assesses three explanations for the lack of internal unity in the Left Party and AfD regarding a new Russia policy: (a) path dependency; (b) eastern Germany, and (c) the reinforcing rather than crosscutting nature of the Ukraine War vis-à-vis the primary intraparty cleavage. It finds that the third explanation fits both Left Party and AfD cases.

Trust-induced cooperation under the complex interaction of networks and emotions

Numerous studies have explored the relationship between cooperation and inter-individual trust. However, the understanding of the dynamic interaction process of trust among networked agents with bounded rationality remains limited. To bridge this gap, this paper develops an evolutionary game model that incorporates a trust perspective considering emotional scaling. Trust is quantified based on individuals’ limited memory of interaction experiences, acting as a perturbation factor that influences cooperative strategies. The role of emotional factors, particularly guilt and anger, in trust formation is emphasized. Extensive numerical simulations reveal that introducing trust greatly enhances cooperation in the system, especially in networks with lower randomness. Trust-based cooperative decision-making demonstrates evident historical path dependence characteristics. Notably, the effects of emotions differ. Guilt effectively motivates defectors to switch strategies, thereby enhancing cooperation. In contrast, the influence of anger on cooperation diminishes as it becomes intertwined with other factors during the trust-building process. The empirical results of higher returns for low-trust groups have also been validated in this paper. Overall, this work contributes to a deeper understanding of the mechanisms underlying cooperation formation.

Spatio-Temporal Dynamics of Carbon Emissions and Their Influencing Factors at the County Scale: A Case Study of Zhejiang Province, China

Significant carbon emissions, a key contributor to global climate warming, pose risks to ecosystems and human living conditions. It is crucial to monitor the spatial and temporal patterns of carbon emissions at the county level to reach the goals of carbon peak and neutrality. This study examines carbon emissions and economic and social problems data from 89 counties in Zhejiang Province. It employs analytical techniques such as LISA time path, spatio-temporal transition, and standard deviational ellipse to investigate the trends of carbon emissions from 2002 to 2022. Furthermore, it utilizes the GTWR model to evaluate the factors that influence these emissions on a county scale. The findings reveal the following: (1) The LISA time path analysis indicates a pronounced local spatial structure in the distribution of carbon emissions in Zhejiang Province from 2002 to 2022, characterized by increasing stability, notable path dependency, and some degree of spatial integration, albeit with a diminishing trend in overall integration. (2) The LISA spatio-temporal transition analysis indicates significant path dependency or lock-in effects in the county-level spatial clustering of carbon emissions. (3) Over the period 2002–2022, the centroid of carbon emissions in Zhejiang’s counties mainly oscillated between 120°55′15″ E and 120°57′01″ E and between 29°55′52″ N and 29°59′11″ N, with a general northeastward shift forming a “V” pattern. This shift resulted in a stable “northeast–southwest” spatial distribution. (4) Factors such as population size, urbanization rate, and economic development level predominantly accelerate carbon emissions, whereas industrial structure tends to curb them. It is crucial to customize carbon mitigation plans to suit the circumstances of each county. This study provides insight into the spatial and temporal patterns of carbon emissions at the county level in Zhejiang Province. It offers crucial guidance for developing targeted and practical strategies to reduce carbon emissions.

Time-switching patrolling controller for holonomic and nonholonomic multi-robot systems

In this paper, we propose a distributed controller for holonomic and nonholonomic multi-robot systems to patrol multiple targets. Distributed control is a control method that uses only information observed by the robot itself and gathered from its surrounding without relying on centralized control. In order for the robots to accomplish their patrols, we propose an objective function that changes switching weights depending on the time when the targets are not observed. As a result, each robot determines which targets to observe based on the time weights and the information from surrounding robots to achieve efficient patrols. We design a distributed controller based on the gradient-flow method for both holonomic and nonholonomic systems. Finally, simulation and experimental results show that the proposed controller is useful for the robots to realize patrolling.

An electric kickscooter multibody model: equations of motion and linear stability analysis

AbstractIn this work, a detailed multibody model of an electric kickscooter is presented. The model includes toroidal wheels as well as rear and front suspensions. The equations of motion are derived and linearized along the steady forward motion of the vehicle. Using an efficient linearization approach, suitable for complex multibody systems with holonomic and nonholonomic constraints, allows for obtaining the reduced linearized equations of motion as a function of the geometric, dynamic, wheels’, and suspensions’ parameters. The proposed electric kickscooter multibody model is validated with the stability results of a previously presented electric kickscooter benchmark. Since the resulting eigenvalues are parameterized regarding the design parameters, a detailed linear stability analysis of the system is performed. In particular, the influence on the stability of the toroidal geometry of the wheels, the elliptic cross-section of the toroidal wheels, the rider model, the steering axis inclination angle, the inertia tensor of the front frame, and the rear and front suspensions is analyzed. The model presented, together with the linearized equations of motion obtained in this work, enables a systematic analysis of the stability of these vehicles, which helps design new electric kickscooters with improved vehicle safety conditions and oriented to a wider range of potential users.

A Hierarchical Trajectory Planning Algorithm for Automated Guided Vehicles in Construction Sites

Herein, to address the challenges faced by Automatic Guided Vehicles (AGVs) in construction site environments, including heavy vehicle loads, extensive road search areas, and randomly distributed obstacles, this paper presents a hierarchical trajectory planning algorithm that combines coarse planning and precise planning. In the first-level coarse planning, lateral and longitudinal sampling is performed based on road environment constraints. A multi-criteria cost function is designed, taking into account factors such as deviation from the road centerline, shortest path cost, and obstacle collision safety cost. An efficient dynamic programming algorithm is used to obtain the optimal path. Considering nonholonomic constraints of vehicles, eliminating inflection points using improved B-Spline path fitting, and a quadratic programming algorithm is proposed to enhance path smoothness, completing the coarse planning algorithm. In the second-level precise planning, the coarse planning path is used as a reference line, and small-range sampling is conducted based on AGV motion constraints, including lateral displacement and longitudinal velocity. Lateral and longitudinal polynomials are constructed. To address the impact of randomly appearing obstacles on vehicle stability and safety, an evaluation function is designed, considering factors such as jerk and acceleration. The optimal trajectory is determined through collision detection, ensuring both safe obstacle avoidance and AGV smoothness. Experimental results demonstrate the effectiveness of this method in solving the path planning challenges faced by AGVs in construction site environments characterized by heavy vehicle loads, extensive road search areas, and randomly distributed obstacles.

A finite-time path-tracking control algorithm for nonholonomic mobile robots with unknown dynamics and subject to wheel slippage/skid disturbances

Path planning and tracking control are two performance-critical tasks for wheeled mobile robots, particularly when nonholonomic constraints are imposed on robots in dynamically uncertain conditions. Accomplishing certain performance and safety considerations related to path-tracking, such as global stability, transient performance, and smooth finite-time convergence, becomes more difficult for nonholonomic robots. This paper is concerned with proposing a new adaptive robust finite-time tracking control approach for a large class of differential drive autonomous nonholonomic wheeled mobile robots (NWMRs) that are subject to structured uncertainties and extraneous disturbances with fully unknown dynamics. For this purpose, nonlinear kinodynamics of a type of rear-wheel drive NWMRs are developed by incorporating the skid/slippage constituents of the wheel motion. Then, a path-tracking controller is proposed using a continuous finite-time adaptive integral sliding mode control coupled with an integral backstepping approach (FTAISM-IBC). For the adaptive controller design, the entire nonlinear dynamics of the robot, including nonlinear vector functions and control gain functions, together with extraneous disturbances, are estimated by leveraging the universal approximation capabilities of radial basis neural networks (RBFNNs). The finite-time stability proof is presented by utilizing the Lyapunov stability theorem. Furthermore, the adaptive gains are derived to ensure the finite-time stability of the system subject to unknown functions, parametric variations, and unknown but bounded disturbances. Finally, the effectiveness of the proposed controller is evaluated through simulations in terms of several key performance indicators against several reported studies.

Carrot or stick, or both? Examining U.S. presidents' use of counterterrorism tools in Pakistan, 2001–2020

AbstractWithin the context of eliciting partner country cooperation and interests alignment, U.S. presidents and their administrations have a wide array of policy tools at their disposal to mitigate terrorist threats emanating from other countries. Yet, the factors that account for variation in American presidents' and their administrations' specific counterterrorism policy choices to influence target states' behavior remain underexplored. To understand the underlying factors that influence the adoption of different U.S. counterterrorism tools within a given country, and their variation across administrations in the post‐9/11 era, we examine the case of Pakistan—a challenging yet enduring partner in the U.S.‐led Global War on Terror. Categorizing the counterterrorism policies and tools adopted by Presidents Bush, Obama, and Trump as punitive or nonpunitive policy decisions, we explore the extent to which each president's policy choices during the Global War on Terror were largely a continuation of predecessors or responded to specific shifting patterns of terrorism within Pakistan. Our study yields important insights regarding the extent to which path dependency or evolving security environments shape choices of counterterrorism tools.

Securing securities: political risk, sovereign debt, and the Anglo-American financial power transition

Abstract Under what conditions do countries lose their status as the leading global financial center? Some scholars argue that such shifts follow shortly after transitions in the distribution of other key capabilities (e.g. GDP), while others argue that path dependence or other more bespoke capabilities might be able to sustain financial leadership long after decline in other capabilities. This paper aims to understand the causes of the Anglo-American financial transition. I argue that the ability to manage political risk for investors is critical to the position of countries as financial entrepôts. In the case of British financial leadership, I argue that Britain’s position as an entrepôt hinged on its power projection capability, which enabled Britain to limit political risk for investors in ways that other states could not replicate. The gradual loss of those capabilities, in turn, saw Britain eventually become overshadowed by the United States. I support my claims with a TERGM analysis of the interwar sovereign debt network.

Dependence of the parallel mean free path of high-energy galactic cosmic rays in the heliosphere on their rigidity, and solar activity as measured by the GRAPES-3 experiment

Dependence of the parallel mean free path of high-energy galactic cosmic rays in the heliosphere on their rigidity, and solar activity as measured by the GRAPES-3 experiment

Adaptive region-reaching control for a non-holonomic mobile robot via system decomposition approach

This article focuses on the region-reaching control for a non-holonomic mobile robot system with two actuated wheels. The region-reaching control task consists of three basic control objectives: reaching an objective region, maintaining a rest state in the objective region, and stabilising at a desired posture. The constraints imposed on the motion of the mobile robot system are not integrable and have no inverse resolution, and thus increase the complexity of the controller design for non-holonomic systems. A system decomposition approach is proposed to convert the non-holonomic constraint control system to the cascade holonomic constraint control subsystems, and an adaptive control gain technique is presented to guarantee the stability of the non-holonomic system. Then, a novel torque controller is presented to implement the region-reaching control task. Specially, the objective region can be considered as a parking spot for a mobile robot.

Investigating Suitable Combinations of Dynamic Models and Control Techniques for Offline Reinforcement Learning Based Navigation: Application of Universal Omni-Wheeled Robots

Abstract Omnidirectional locomotion provides wheeled mobile robots (WMR) with better maneuverability and flexibility, which enhances their energy efficiency and dexterity. Universal omni-wheels are one of the best categories of wheels that can be used to develop a WMR (Amarasiri et al., 2022, “Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot,” ASME Letters Dyn. Sys. Control., 2(4), p. 040902. 10.1115/1.4055690). We study dynamic modeling and controllers for mobile robots to train in a reinforcement learning (RL)-based navigation algorithm. RL tasks require copious amounts of learning iteration episodes, which makes training very time consuming. The choice of dynamic model and controller has a significant impact on training time. In this paper, we compare a traditional Kane’s equations model to a non-holonomic canonical momenta model (Barhorst, 2019, “Generalized Momenta in Constrained Non-Holonomic Systems—Another Perspective on the Canonical Equations of Motion,” Int. J. Non-Linear Mech., 113, pp. 128–145.). We implemented four controllers: proportional integral derivative, linear quadratic regulator with integral action, pole placement, and a full nonlinear sliding mode controller. We summarize the pros and cons of each of the modeling techniques, and control laws implemented. The outcomes of our analysis will improve RL training time for path generation in unstructured environments.

Ending 1990s Law and Development Ideas, Paradox of Path Dependence In Economic Planning Institutions Under Covid-19: SA’s Response

Abstract This paper argues that the COVID-19 pandemic can and should be understood as a form of creative destruction (Schumpeter’s gale), at a hyper level owing to its biological/medical dimension. Therefore, the critical response to such a hyper force is to rethink how institutions administer Public Policy in South Africa (Path Dependency), most importantly economic development planning institutions and Covid-19 responses, in the form of ‘The Economic Reconstruction and Recovery Plan’. It’s the contention of this paper that the reason why Covid-19 continues to impact the South African government’s economic planning ethos is anchored in its developmental orientation, particularly how constitutional legalism has impaired economic development planning. This could impart be due to the unaddressed influences of the initial waves of Law and Development post-1994. The South African experience with the initial waves of Law and Development were muted owing to the problematic nature of the 1994 transition which sought peace at all costs without necessary addressing substantive economic development reform considerations. Therefore, using the policy experiences of Covid-19 and Lee’s, General Theory of Law and Development, particularly the aspects of Development and State Capacity and Political Will, a revision of the South African Constitution will be called on, principally chapter’s 2 and 6 (Bill of Rights) and (Province).

How ad hoc coalitions deinstitutionalize international institutions

Abstract As ad hoc coalitions (AHCs) proliferate, particularly on the African continent, two questions crystallize. First, what consequences do they bring about for the existing institutional security landscape? And second, how can the trend of AHCs operating alongside instead of inside international organizations be captured and explored conceptually? To answer these questions, we closely examine the Multinational Joint Task Force (MNJTF) fighting Boko Haram and its changing relationship to the African Union. Through the case-study and a review of policy and academic literatures, the article launches the concept of deinstitutionalization and how it can be characterized. We identify three features of deinstitutionalization: AHCs can bypass standard procedures for decision-making processes, whittle down established institutional scripts and shift resource allocations. We detail how the AHCs contribute to changing practices of financing international peace and security operations, with an examination of European Union and United Nations policies and practices. In sum, the article unwraps processes of deinstitutionalization and identifies three forms of rationales for this process: lack of problem-solving capacity, limited adaptability and path dependency.

How does digital finance affect rural capital misallocation? Evidence from China

ABSTRACT Digital finance can take advantage of new financial services to support economic growth. Its effect on rural capital misallocation is valuable to be discussed as rural capital misallocation has severely restricted rural development worldwide. This paper, with data from Chinese counties, studies the effect of digital finance on rural capital misallocation and further analyzes how it acts by exploring the moderating role of industrial structure upgrading. The system GMM method is applied to resolve the path dependence and identify the dynamic effects of capital misallocation. The findings include that (1)rural digital finance has reduced the capital misallocation for the rural area in China; (2)industrial structure upgrading will strengthen the negative effect of digital finance on capital misallocation which is more significant in the eastern and western regions, as well as in the capital over-allocation regions in rural China; (3)the impact of rural digital finance on rural capital misallocation has a spatial spillover effect, and digital finance has reduced capital misallocation not only in the local but also in neighbouring regions. This study contributes to the existing rural digital finance literature and will help regions reduce capital misallocation by improving rural digital finance.

Educating AI developers to prevent harmful path dependency in AI resort-to-force decision making

ABSTRACT The integration of AI technology into resort-to-force decision making would give machines influence over decisions determining the life or death of countless people. Despite this prospective impact on entire societies, many AI developers have only a rudimentary ethical understanding of their AI. Moreover, if AI development is ethically unreflective, minor flaws can spiral into harmful machine-made decisions. So how can we ensure responsible military AI development, especially in the context of AI-supported decision making on the resort to force? We argue that harmful ‘path dependencies’ of AI systems can only be avoided by providing developers with clear training on ethical and developmental issues arising during the AI lifecycle. Based on lessons from applied human systems exploration, we introduce an educational regime: ‘10 pillars of responsible AI development.’ Each pillar is broken down into detailed questions, helping developers reflect on their decisions throughout the AI lifecycle. This article is thereby situated between political science, normative theory, and computer science and contributes to this collection’s treatment of the risks of integrating AI technology into resort-to-force decision making by focusing on the AI development process itself, particularly on the role of developers.