Search Results (57)

Recent advances in mobile robotics have emphasized the need for systems capable of operating in unstructured environments, combining obstacle negotiation, stability, and adaptability. This study presents the preliminary design and testing of Brush.Q, an articulated ground robot featuring a novel structure distinct from existing wheel-legged robots, equipped with compliant brush-like wheels composed of multiple spokes. The main contribution is the experimental analysis of suspension capability across different wheel geometric profiles, combined with the assessment of obstacle-climbing performance. A simplified prototype was constructed to evaluate the effects of wheel rotation direction, spoke number, and spoke tapering. Results show that reducing the number of spokes improves obstacle-climbing at the expense of suspension, while higher spoke count and compliant geometry enhance suspension and stability. Spoke tapering improves obstacle climbing in the backward-facing configuration but consistently reduces suspension. Overall, these findings highlight the critical role of wheel geometry and the potential for reconfigurable spoked wheels to enhance adaptability and versatility in unstructured terrains. Full article

This article focuses on one critical factor among the many influencing identity formation in the second generation of Albanian origin in Greece: the acquisition of citizenship. Citizenship is more than a legal status; it serves as a fundamental marker of belonging, shaping access to rights, social mobility, and political participation. Despite the 2015 Greek citizenship law aiming to facilitate naturalization, many second-generation Albanians still face bureaucratic obstacles, and prolonged legal uncertainty. These barriers create a sense of social exclusion by limiting opportunities in education and employment and depriving them the right of political participation. Navigating these challenges forces individuals to negotiate their identity in complex ways. Some emphasize Greek identity, others adopt a hybrid identity, yet others reinforce Albanian self-identification. Broader societal attitudes, including stereotypes and discrimination, further shape these identity strategies. These strategies are furthermore influenced by the individuals’ life trajectories, which can either reinforce a sense of otherness or counteract it. Through in-depth interviews, this qualitative study argues that citizenship constitutes a crucial determinant of cultural and/or national belonging for some people, as it produces practical and symbolic conditions of inclusion or exclusion. Ultimately, Greek citizenship functions not just as an institutional gatekeeper but as a broader social force that shapes an individuals’ identity and sense of belonging within Greek society. Full article

The inability to regain balance following a trip-induced event is one of the leading causes of falls and fall-related injuries in older adults. Virtual reality (VR) has the potential to expose individuals to realistic environments with minimal injury risk compared to real-world exposures. The purpose of this study was to compare lower extremity joint kinematics during overground walking when exposed to real and virtually generated trip obstacles. A total of 14 healthy participants [7 male, 7 female; age: 23.46 ± 3.31 years; height: 173.85 ± 8.46 cm; mass: 82.19 ± 11.41 kg; shoe size (men’s): 9.03 ± 2.71 s] were recruited for this study. Knee and ankle joint kinematics were recorded during obstacle negotiation when exposed to REAL and VR environments. Environmental exposure was assigned in a counterbalanced order to prevent an order effect. Knee and ankle joint kinematics were independently analyzed using a 2 × 3 repeated measures ANOVA to compare environmental conditions and gait type of the lead leg and trail leg at an alpha level of 0.05. No significant differences were observed between environmental conditions. However, significant differences were observed between gait types of the lead leg and trail leg. Current findings reveal similar gait kinematics during obstacle negotiation when exposed to real and virtually generated trip obstacles, suggesting the potential transfer of skill in fall prevention strategies to real-world conditions. Full article

In response to the challenges of obstacle avoidance and terrain negotiation encountered by wheel-legged robots in static environments with complex obstacles, this study introduces an enhanced A* path planning algorithm that incorporates a jump-point search strategy, a dynamically weighted heuristic strategy, and a continuous jumping constraint mechanism to facilitate efficient obstacle traversal. The algorithm extends the traditional 8-neighborhood rule to support jumping in the horizontal, vertical, and diagonal directions. A dynamic, weighted heuristic is introduced to adaptively adjust heuristic weights, guide the search direction, improve efficiency, and reduce detours. Redundant point removal and Bézier curve smoothing were employed to enhance path smoothness, whereas the continuous jumping constraint limited the jump frequency and improved motion stability. The results validate that—relative to the standard A* algorithm, which achieves a 73.7% reduction in path nodes (from 54 to 16)—85% fewer search nodes (from 542 to 78) and a planning time of 0.0032 s were achieved while also enhancing performance in crossing complex structures. This enhances the capability of wheel-legged robots to perform real-time path planning in structurally complex yet static environments, thereby improving their autonomous navigation efficiency. Full article

Contemporary multi-UAV formations face dual challenges of obstacle avoidance and rapid formation recovery. To enable UAV swarms to efficiently restore their predefined configurations post-obstacle navigation, a formation recovery strategy grounded in Nash equilibrium game theory is proposed in this paper. By integrating the IVY optimization algorithm, a collaborative control model that systematically balances individual UAV interests with swarm-level objectives through carefully designed optimization criteria is established. Comparative experimental results demonstrate that, compared to traditional formation obstacle-avoidance algorithms, Improved Particle Swarm Optimization (IPSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA), our method exhibits superior performance across multiple key metrics, including average path length, formation accuracy rate, recovery time, and total time consumption. Real-flight tests on a multi-UAV platform confirm IVYA-Nash surpasses improved APF in formation accuracy and aerodynamic disturbance resistance, proving robustness in dynamic multi-agent scenarios. The work provides an efficient and reliable solution for coordinated control of UAV formations in complex environments. Full article

The dynamic model of a wheeled wall-climbing robot exhibits stage-specific changes when traversing different types of obstacles and during various stages of obstacle negotiation. Previous studies often employed remote control methods for obstacle-crossing control, which fail to dynamically adjust the torque distribution of magnetic wheels in response to real-time changes in the dynamic model. This limitation makes it challenging to precisely control the robot’s speed and attitude angles during the obstacle-crossing process. To address this issue, this paper first establishes a staged dynamic model for the wall-climbing robot under typical obstacle-crossing scenarios, including steps, 90° concave corners, 90° convex corners, and thin plates. Secondly, an adaptive controller based on a radial basis function neural network (RBFNN) is designed to effectively compensate for variations and uncertainties during the obstacle-crossing process. Finally, comparative simulations and physical experiments demonstrate the effectiveness of the proposed method. The experimental results show that this method can quickly respond to the dynamic changes in the model and accurately track the trajectory, thereby improving the control precision and stability during the obstacle-crossing process. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article

Urban regeneration projects have been used in urban planning in different cities to recover strategic areas and combat new land consumption. Often, Public–Private Partnership (PPP) agreements are signed to meet the demands of these projects. In the Italian case, PPP arrangements can be one of three types, namely, contractual, institutionalized, and negotiable. Urban regeneration, in the Italian context, aims to increase the value of the existing infrastructure. Despite this, there are several obstacles to how these practices are developed, which demand more attention to be paid to the enabling environment and the Critical Success Factors (CSFs) that can improve the performance of these practices. Based on this, this study aims to analyze the Italian context of PPP arrangements in urban regeneration projects and the specific case of the Livorno “Porta a Mare”. To this end, the study adopts a previously established CSFs framework. The data analyzed are derived from secondary sources obtained through documentary collection and primary sources obtained through interviews. Regarding the PPP process, the results indicate the commitment of the public and private sectors, despite limitations in the selection process and the lack of public participation. Concerning urban regeneration, although not yet completed, the project allocates a significant area of waterfront to urban use. Full article

This paper concerns lightweight—up to 800 kg—UGVs (unmanned ground vehicles), where multi-tracked running gears are used to improve the obstacle negotiation performance. A comparison of four different multi-tracked systems and, for reference, classic tracked running gear is presented. Simulations in a multi-body dynamics program were performed, where running gear solutions overcame three typical obstacles and were assessed using a total of five effectiveness and functionality criteria. The simulation models took into account the variable track–ground contact surface. The ground parameters were validated by means of experimental tests for grassy terrain. In the results, the most advantageous solutions in each category are indicated, and design guidelines for increasing the obstacle-overcoming capabilities of multi-tracked UGVs are presented. Full article

Body awareness allows the individual to negotiate spatial tasks by referencing their own body. Here, we tested whether biologically meaningful factors, such as an alternative solution (detour around an obstacle) and learning from a human demonstrator, would affect dogs’ reliance on their body size in an aperture test. We hypothesized that the dogs would choose the socially reinforced solution over a shortcut, and they would choose the shortcut when the opening was comfortably large. We tested N = 45 adult, mixed-breed dogs, by using a 3 m long, transparent fence. The dogs had three trials with closed doors and then three trials with either a small (but passable) or a large open door. In the demonstration group the experimenter performed a detour before the first three trials, then we opened the large door. The dogs preferred the shortcut, where they relied on body awareness, as they either hesitated or opted for a detour when the small door was open. The dogs who watched the demonstration for longer in trial 3 chose the door less often in trial 4, which indicates social learning. By testing mixed-breed dogs, our results are minimally influenced by functional breed selection and serve as valuable baseline for testing complex socio-cognitive traits in companion dogs. Full article

This study examines the role of humanitarian diplomacy during the Tigray humanitarian crisis in Ethiopia, a humanitarian disaster marked by severe shortages in food, healthcare, and essential services that deeply affect civilians. A qualitative approach using both primary and secondary data grounds the study by exploring key actors in humanitarian diplomacy, their successes, and barriers to aid delivery. Humanitarian actors, such as UN agencies, international NGOs, donor countries, the EU, the US, and the African Union, have engaged with the Ethiopian government, the TPLF, and the Tigray Transitional Government to alleviate the crisis. Notable achievements in humanitarian diplomacy include negotiations, information gathering, communication, civilian needs assessment, resource mobilization, advocacy for international law, and distressed civilians. Humanitarian diplomacy has facilitated international aid operations, saving lives during critical periods, despite practical difficulties. Diplomatic efforts have faced significant interruptions due to access restrictions imposed by the Ethiopian government, security threats from ongoing fighting leading to attacks on aid convoys and casualties among aid workers, and bureaucratic obstacles imposed by the Ethiopian government. This study highlights the necessity for effective humanitarian diplomacy in accounting for complex political landscapes in conflict-affected regions, developing flexible strategies that enhance access to aid, and improving humanitarian interventions. Full article

In today’s competitive and environmentally conscious industries, the ability of organizations to adapt and respond is more important than ever. This study focuses on overcoming the obstacles faced by the Iranian automobile sector by highlighting the significance of incorporating green supply chain techniques. The research intends to integrate organizational operations with environmental sustainability goals by utilizing a MULTIMOORA strategy for supplier selection. The Iranian automobile sector, facing substantial environmental challenges, requires a strategy framework for selecting environmentally friendly suppliers in order to sustain competitiveness and fulfill ecological obligations. The study develops a supplier selection model based on extensive research and expert knowledge. The Delphi and MULTIMOORA techniques are employed to assess and prioritize suppliers according to green criteria, assuring conformity with environmental goals. Data are collected by conducting a comprehensive analysis of the existing literature and engaging in conversations with industry experts in order to acquire information for the construction of the model. The results emphasize the crucial significance of trust-based relationships with suppliers, rigorous compliance with quality standards in new product development, and substantial investment in employee training and development. Sector analysts view these characteristics as crucial for promoting sustainability and gaining a competitive advantage in the Iranian vehicle sector. This study provides firms with strategic instruments to effectively negotiate the intricacies of green supply chain management, with a particular focus on the need for adopting sustainable practices while selecting suppliers in the dynamic and competitive context of the Iranian automobile industry. Full article

In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom dynamic model and a guidance control model for unmanned aerial vehicles (UAVs) are established, and the guidance commands are decomposed into control signals for the pitch and yaw planes. Secondly, based on the theory of dynamic inversion control, a temporal consistency guidance law for a single UAV is constructed. On the other hand, an improved artificial potential field theory is used and integrated with a predictive correction network to generate guidance commands for threat avoidance. A threshold smoothing method is employed to integrate the two guidance systems, and a cluster consultation mechanism is introduced to design a two-layer temporal synchronization architecture, which negotiates to change the synchronization time of the swarm to achieve the convergence of consistency once again. Finally, in typical application scenarios, simulation verification demonstrates the effectiveness of the control method proposed in this paper. The proposed control method achieves the guidance of UAV formations to synchronize their arrival at the target location under complex threat conditions. Full article

This qualitative research project seeks to examine the obstacles faced by educators who come to the U.S. as refugees. The three participants in this study are from Iraq, Sudan, and Turkey. While there are similarities between them, there are also differences in terms of race, sex, and religion. While this work examines practical barriers, such as those related to credentialing, it also considers how these educators negotiate their identity in the workplace. Using labeling theory, the impact of the category of refugee and how that label impacts work life is interrogated. Partially as a result of the labels associated with being a refugee, findings indicate that, for educators from refugee backgrounds, there are significant barriers to credentialing, their knowledge and experience from outside of the U.S. is rarely taken seriously in many workplace environments, and, consequently, a lack of confidence can easily develop. Despite all of this, educators from refugee backgrounds often have extensive experience in education and can more easily connect to diverse student populations and their families. Suggestions for how best to expand the hiring of educators from refugee backgrounds are provided, as well as implications for future research. Full article

This study explores the opportunities and challenges of integrating artificial intelligence (AI) into engineering education. Through a review of the literature and a qualitative focus group study, an assessment was made for the role of AI in personalizing learning, enhancing simulation engagement, providing real-time feedback, and preparing students for AI-integrated workplaces. The study emphasizes how AI may significantly improve educational experiences by making them more dynamic, interactive, and successful. It also draws attention to important issues, such as moral questions, algorithmic biases in AI, infrastructure constraints, the need for AI literacy training for educators, and a range of student perspectives on AI engineering education. The results support a systematic approach to AI integration, highlighting the necessity of cooperative efforts by educators, legislators, curriculum designers, and technologists in order to overcome these obstacles. The study makes the case that AI can transform engineering education by negotiating these challenges and providing students with the information and skills needed for the digital future, all the while assuring fair and moral access to technology-enhanced learning. Full article