Path Quality Research Articles

Urban walkability and tour bus travel

Urban walkability is receiving increasing research attention due to the environmental and health benefits of walking in comparison to driving, yet little is known regarding the walking behaviors of urban visitors who arrive by tour bus. The purpose of this study is to investigate the walking behaviors of diverse tour bus user groups visiting a spatially complex urban destination to better understand urban walkability, barriers to walking, and the derived benefits to walking for tour bus participants. Using an immersive mixed-methods approach, researchers collected eight days of on-bus/off-bus data to document information specific to walking activities, site-to-site transport, tour bus cruising/idling, operational efficiency, mobility, access, and safety. Four target visitor groups included school age, adults, international, and senior citizen veterans. Pedestrian miles traveled ranged from 2.0 to 10.5 miles during tours that lasted between 4.30 and 14.15 h, while primary barriers to walking included itineraries, weather, and safety considerations. Addressing the psychology of walking to evolve from utilitarian to a purposefully integrated leisure activity within tour bus itineraries is the key to decreasing dependency on tour buses for site-to-site transport in urban destinations. Management implications•Spatially complex urban park designs should consider walkability factors such as access, connectivity, path quality, safety, and aesthetics.•Policies that highlight and reward smart mobility should be prioritized to increase utilitarian and leisure walking in urban parks.•Tour mode bundles should purposefully embed walking activities in itineraries to increase physical benefits and participant satisfaction.

Global path planning for autonomous construction vehicles in building construction: A comparative study with a focus on vehicle kinematic characteristics

Technological advancements in robotics and automation have led to a growing emphasis on integration into construction industry from a global research context. In particular, autonomous construction vehicles (ACVs) stand out as a pivotal innovation. One key aspect of successful deployment of ACVs on construction sites is the path planning technology, especially the global path planning (GPP) methods. Thus, this study conducted a comparative study of the six GPP algorithms from various underlying principles, including graph search methods (e.g. Dijkstra and A*), sampling-based methods (e.g. rapidly exploring random tree (RRT) and rapidly exploring random tree star (RRT*)), evolutionary computation (e.g. genetic algorithms (GAs)), and artificial intelligence (e.g. deep Q learning (DQN)), to reveal how GPP algorithms perform on the travel of ACVs. These algorithms were evaluated with consideration of the kinematic characteristics of typical construction vehicles, including truck-trailers and excavators, within a typical unstructured real-world building construction site and based on two tasks and eight criteria encompassing efficiency and safety. The findings were analysed from three perspectives, i.e. the planned global paths and travel paths of ACVs, the performance of algorithms in each metric, and the overall performance of each algorithm, to identify the strengths of each GPP algorithm. GAs excel in generating smoother travel paths than others, whereas the global paths from RRT and DQN exhibit unpredictable changes, leading to a meandering travel paths for ACVs. The quality of global paths induces a greater impact on differential-wheeled excavators compared to front-wheel steering truck-trailers. Practical guidance can therefore be provided to select and improve the GPP algorithms and thereby to enhance the safety and efficiency of ACVs on construction sites.

Enhancement of Harris Hawks optimization applied in path planning for an indoor navigation mobile application

The research focuses on the enhancement of Harris Hawks Optimization (HHO) algorithm in achieving an efficient path planning, specifically in indoor environments. It solves problems encountered in HHO; firstly, the exploration operator of HHO is modified to incorporate the survival of the fittest principle, this ensures a controlled diversity by using an Exponential Ranking selection method. The method aims to guide the algorithm to find a more optimal solution while allowing it to search for alternative paths. Secondly, Linear Path Strategy is used to reduce the number of nodes in the paths, therefore minimizing its length and simplifying trajectories. In addition, Linear Path Strategy aims to create smoother paths and avoid obstacles, improving the overall performance of the algorithm. Lastly, general multi-objective formula is defined to evaluate path length and travel time. Considering these factors established a balanced evaluation metric, which provided a detailed assessment of path quality for scenarios like indoor navigation. Comparative analysis was done, and results highlighted the effectiveness of EHHO in generating better paths, in comparison with Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), and with the current HHO algorithm. It outperformed the algorithms compared in terms of path length, travel time, and efficiency of the execution, especially in a complex environment. In conclusion, EHHO algorithm showed promising results in providing shorter, faster, and more efficient routes, with potential applications across different domains that requires optimal path planning solutions.

A Sampling-Based Approach to Solve Difficult Path Planning Queries Efficiently in Narrow Environments for Autonomous Ground Vehicles

Path planning is an essential subproblem of autonomous robots’ navigation. Reaching a given goal pose or covering the available space are typical navigation missions, that require different planning approaches. We focus on such problems in this paper, where a goal pose must be reached by a wheeled autonomous ground vehicle in challenging situations, i.e. in complex environments with limited free space. Many path-planning methods are available, from which the sampling-based approaches gained the highest interest due to their computational efficiency. However, the performance of such methods degrades if the free space is limited and narrow passages have to be crossed on the way to the goal. Finding real-time planning methods to deliver high-quality paths in such situations is challenging. This paper aims to take steps toward solving this problem. On the one hand, an approach is presented to characterize free space narrowness and the difficulty of planning tasks. This can be used as a tool to compare planning queries and evaluate the performance of planning methods from the perspective of their sensitivity to environmental narrowness. On the other hand, an improved variant of our previously proposed RTR planner, an incremental sampling-based path-planning method, is introduced that exhibits good performance even in narrow and difficult planning situations. It is shown by simulations that it outperforms the popular RRT and RRT* planners in terms of running time and path quality, and that it is less sensitive to the narrowness of the environment where the planning task has to be solved.

Mobile Robot Path Planning Algorithm Based on NSGA-II

Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm utilizes a search window approach for population initialization, which improves the quality of the initial population. An innovative fitness function is designed as the objective function for optimization iterations. A probability-based selection strategy is employed for population selection and optimization, enhancing the algorithm’s ability to escape local minima and preventing premature convergence to suboptimal solutions. Furthermore, a path smoothing algorithm is developed by incorporating Bézier curves. By connecting multiple segments of Bézier curves, the problem of the high computational complexity associated with high-degree Bézier curves is addressed, while simultaneously achieving smooth paths. Simulation results demonstrated that the proposed path planning algorithm exhibited fewer iterations, superior path quality, and path smoothness. Compared to other methods, the proposed approach demonstrated better overall performance and practical applicability.

PPSwarm: Multi-UAV Path Planning Based on Hybrid PSO in Complex Scenarios

Evolutionary algorithms exhibit flexibility and global search advantages in multi-UAV path planning, effectively addressing complex constraints. However, when there are numerous obstacles in the environment, especially narrow passageways, the algorithm often struggles to quickly find a viable path. Additionally, collaborative constraints among multiple UAVs complicate the search space, making algorithm convergence challenging. To address these issues, we propose a novel hybrid particle swarm optimization algorithm called PPSwarm. This approach initially employs the RRT* algorithm to generate an initial path, rapidly identifying a feasible solution in complex environments. Subsequently, we adopt a priority planning method to assign priorities to UAVs, simplifying collaboration among them. Furthermore, by introducing a path randomization strategy, we enhance the diversity of the particle swarm, thereby avoiding local optimum solutions. The experimental results show that, in comparison to algorithms such as DE, PSO, ABC, GWO, and SPSO, the PPSwarm algorithm demonstrates significant advantages in terms of path quality, convergence speed, and runtime when addressing path planning issues for 40 UAVs across four different scenarios. In larger-scale experiments involving 500 UAVs, the proposed algorithm also exhibits excellent processing capability and scalability.

HB-RRT:A path planning algorithm for mobile robots using Halton sequence-based rapidly-exploring random tree

Path planning remains crucial for efficient robot operation. A Halton Biased Rapidly-exploring Random Tree (HB-RRT) path planning algorithm is introduced in this study. The Halton sequence, known for its uniform distribution and low discrepancy, is employed for sampling. Issues arising from the pseudo-random sequence in the standard RRT algorithm, leading to uneven distribution of sampling points, are addressed. A mouse-inspired goal-oriented strategy and a candidate sampling pool strategy are incorporated to enhance the sampling point quality, thereby addressing the challenge of insufficient memory during node expansion. Path optimization is further achieved through a multi-level planning approach, which aims to minimize redundancy. A subsequent smoothing of the path is conducted using a cubic B-spline method. Comparisons with the RRT, Bionic Target Bias-RRT, and Informed-RRT* algorithms, through both numerical simulations and real-world testing, confirm the superiority of the HB-RRT algorithm in terms of planning time, path length, and overall path quality.

Pedestrian-path pavement materials to improve microclimate and air quality on current pedestrian paths

In this study, I investigated pavement materials to improve the microclimate and air quality of pedestrian paths on streets in Seoul, South Korea. Existing pedestrian-path pavement materials were assessed, and several pavement materials were simulated. Asphalt had greater air (Ta) and ground surface (Tg) temperatures than the granite pavement. The granite pavement had a greater mean radiant temperature (Tmrt) and physiologically equivalent temperature than asphalt. Asphalt had a 3.2-times higher sensible heat flux than the granite pavement. The granite pavement reflected approximately 3.9-times more radiation than asphalt and emitted lower NO2 and NOx concentrations than interlock blocks. The high-albedo granite pavement showed a low Ta, and the high-albedo granite pavement + grass showed a low Tg; these Ta and Tg values were lower than those of asphalt. The grass pavement had lower Tmrt and PET values, than the granite pavement. The high-albedo granite pavement exhibited an endothermic phenomenon during the day, whereas grass showed a high sensible heat flux due to an exothermic phenomenon. The granite pavement, with higher albedo, reflected approximately 2.7-times more radiation than the unmodified granite pavement. High-albedo granite pavement material had the lowest NO2 and NOx concentrations, which were lower than those of interlock blocks.

Multi-objective optimal trajectory planning for robot manipulator attention to end-effector path limitation

Abstract In the process of trajectory optimization for robot manipulator, the path that is generated may deviate from the intended path because of the adjustment of trajectory parameters, if there is limitation of end-effector path in Cartesian space for specific tasks, this phenomenon is dangerous. This paper proposes a methodology that is based on the Pareto front to address this issue, and the methodology takes into account both the multi-objective optimization of robotic arm and the quality of end-effector path. Based on dung beetle optimizer, this research proposes improved non-dominated sorting dung beetle optimizer. This paper interpolates manipulator trajectory with quintic B-spline curves, achieves multi-objective trajectory optimization that simultaneously optimizes traveling time, energy consumption, and mean jerk, proposes a trajectory selection strategy that is based on Pareto solution set by introducing the concept of Fréchet distance, and the strategy enables the end-effector to approach the desired path in Cartesian space. Simulation and experimental results validate the effectiveness and practicability of the proposed methodology on the Sawyer robot manipulator.

Optimizing radiation exposure in dynamic radioactive environments: Dynamic ACO-RRT* path planning algorithm

Abstract Radioactive environments are often dynamic for various reasons, such as the dismantling of nuclear facilities, leakage of radiation sources, and nuclear accidents. Path planning in a dynamic radioactive environment is one of the important issues in radiation protection for nuclear facility workers. In traditional dynamic optimal path planning, there are phenomena, in which the optimal path is not found within the allowable time or the quality of the path searched within the allowable time is low. To solve this problem, we propose a hybrid algorithm that combines meta-heuristic and sample-based algorithms, called dynamic ant colony optimization-rapidly exploring random tree star (ACO-RRT*). A low-level grid optimal path consisting of grid vertices is obtained by the ACO algorithm, and then it is further refined by the RRT* algorithm. At every instant of environmental change, the vertices of the grid optimal path are divided into vertices with and without samples, and new samples are generated only around vertices without samples. Sampling optimization is performed only around the grid optimal path using new samples and already existing samples. Simulations in a virtual radioactive environment showed that the proposed algorithm reduces computational costs and improves the quality of the optimal path compared to traditional algorithms.

A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning.

With the rise and development of autonomy and intelligence technologies, UAVs will have increasingly significant applications in the future. It is very important to solve the problem of low-altitude penetration of UAVs to protect national territorial security. Based on an S-57 electronic chart file, the land, island, and threat information for an actual combat environment is parsed, extracted, and rasterized to construct a marine combat environment for UAV flight simulation. To address the problem of path planning for low-altitude penetration in complex environments, a photosensitivity-enhanced plant growth algorithm (PEPG) is proposed. Based on the plant growth path planning algorithm (PGPP), the proposed algorithm improves upon the light intensity preprocessing and light intensity calculation methods. Moreover, the kinematic constraints of the UAV, such as the turning angle, are also considered. The planned path that meets the safety flight requirements of the UAV is smoother than that of the original algorithm, and the length is reduced by at least 8.2%. Finally, simulation tests are carried out with three common path planning algorithms, namely, A*, RRT, and GA. The results show that the PEPG algorithm is superior to the other three algorithms in terms of the path length and path quality, and the feasibility and safety of the path are verified via the autonomous tracking flight of a UAV.

Terahertz reflective imaging systems with improved angle compatibility realized by the designed quasi-scattering screen

Active imaging can provide significantly larger signal margins in the terahertz (THz) spectral region than passive imaging. However, the imaging effect of THz active imaging is severely constrained by the orientation requirement of target reflection, which limits the application scenarios of this technique. In view of the shortcomings of existing THz reflective imaging systems, this paper proposes a new system optimization scheme from the aspect of hardware - a compact quasi-scattering screen (QSS) is proposed and designed as a beam-shaping element to eliminate the need for the best direction of specular reflection to the greatest extent. The corresponding relationship between the divergent Gaussian beam and target light field is directly established by using the equal-energy mapping method in non-imaging optics, which rejects the collimation process of the THz source. By customizing the optimization function in ZEMAX® to automatically optimize the surface of the QSS, the direction of incident light propagation can be adjusted. A non-coaxial reflective THz imaging system using a 0.1 THz continuous wave source demonstrates the ability of the QSS to improve the angle compatibility of the traditional THz reflective imaging system. The results show that the imaging angle compatibility range of the system can be increased to more than 5 times without changing the original imaging optical path and its resolution. In addition, it can also improve the imaging quality of the original imaging optical path for the object with a smooth surface or certain structure.

A novel redundant cooperative control strategy for robotic pollination

This paper proposes a novel Redundant Cooperative Control (RCC) strategy to address the pollination sequence planning problem for a mobile pollination robot in the greenhouse. Firstly, the RCC strategy divides the robot’s workspace into subspaces based on the distribution of flowers through clustering. Subsequently, the RCC strategy plans the traveling salesman problem within the visual field of these subspaces, seeking a compromise solution for an optimal path. Finally, the RCC strategy utilizes the redundant motion capabilities of the mobile chassis and robotic arm to accomplish the pollination task during the movement. The RCC strategy exploits the available redundancy in both the agricultural robot’s mobile platform and end-effector, carefully balancing computational complexity and path quality. To validate the effectiveness of the RCC strategy, we designed a series of experiments conducted on our pollination robot platform, measuring different strategies. The results demonstrate that the RCC strategy achieves an average pollination rate of 7.5 s per flower, representing a 36.4% improvement compared to the most common intermittence strategy. Furthermore, the RCC strategy can be extended to tasks such as harvesting and pruning, significantly enhancing the efficiency of robot task execution even in industrial and service sectors.

Heuristic Expanding Disconnected Graph: A Rapid Path Planning Method for Mobile Robots

Existing mobile robots mostly use graph search algorithms for path planning, which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity. Due to the limitations of the neighborhood search strategy, the robots could hardly obtain the most optimal global path. A global path planning algorithm, denoted as EDG*, is proposed by expanding nodes using a well-designed expanding disconnected graph operator (EDG) in this paper. Firstly, all obstacles are marked and their corners are located through the map pre-processing. Then, the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes. Finally, the EDG* heuristic iterative algorithm is proposed. It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset. Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM. The result shows that the proposed EDG* reduced the planning time by more than 90% and total length of paths reduced by more than 4.6%. Compared to A*, Dijkstra and JPS, EDG* does not show an exponential explosion effect in map size. The EDG* showed better performance in terms of path smoothness, and collision avoidance. This shows that the EDG* algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.

Improved genetic algorithm for mobile robot path planning in static environments

The genetic algorithm (GA) is a well-known meta-heuristic technique for addressing the static mobile robot global path planning (MRGPP) issue. Current GA, however, has certain shortcomings, such as inefficient population initialization and low-quality solutions. As an enhanced GA, a Linear Rank-based, or Clearance-based Probabilistic Road Map (CBPRM), technique is proffered to overcome these difficulties. The new model guides the population initialization process by using the fitness score of each cell in the environment, lowering the number of infeasible pathways created. Furthermore, a genetic operator combination is proposed to balance the global and local search and increase the quality of the optimum path created in terms of path length and safety. Two experiments were carried out to assess the suggested GA. The novel population initialization strategy was compared to two current models in the first experiment, and the findings revealed that the suggested approach greatly decreases the number of infeasible pathways created and the time required for the process. The ideal genetic operator combination was determined in the second experiment, and the results revealed that the suggested combination improves the quality of the optimal path created in fewer iterations. In summary, the proposed GA improves on previous models by proposing a novel population initialization method and combining numerous genetic operators. These alterations improve the quality of the optimum path and indicate the suggested model’s potential for solving the MRGPP challenge.

Key sustainable mobility indicators for university campuses

The increasing focus on promoting sustainable transportation in higher education institutions has resulted in studies that examine different aspects of the planning process. However, little research has focused on an essential element of the process: indicators of sustainable mobility in the context of university campuses. This gap is particularly noticeable in countries like Brazil, where this investigation was carried out. This study aimed to identify a set of key indicators to evaluate sustainable mobility on university campuses by taking into account their relevance and suitability to different contexts. The study combined knowledge gained from real-life planning initiatives with experts' judgment to identify the key indicators. After selecting potentially transferable indicators, we formed an expert panel with professionals from various university campuses to determine the degree of relevance and suitability of these indicators in different contexts. The results were analyzed using the Friedman non-parametric test, which indicated experts' agreement, in terms of relevance and suitability, in the following 13 indicators: Public transport infrastructure, Quality of pedestrian paths (inside and to access the campus), Parking spaces for users with mobility constraints, Urban public transport, Security within and around the campus, Universal access to buildings, Parking infrastructure, Bike racks and bike shelters, Infrastructure to access the campus, Adequacy of transport modes, Quality of campus roads, Cycling infrastructure, and Road safety campaigns. The final list may be particularly convenient for preliminary assessments or for universities that do not have resources available for wide-ranging evaluations of sustainable mobility.

A new figure in oncology pathways: Oncology Nurse Navigator and his preliminary results in the San Benedetto del Tronto Surgery

Background. The role of the Nurse Navigator was developed in order to address the health disparities created by the social determinants of health, , being an expert, specialized and trained nurse who takes charge of the patient diagnosed with cancer and accompanies him throughout the treatment process, helping him to extricate himself from the labyrinth of the healthcare system. This figure was introduced and studied in the general surgery of the Madonna del Soccorso hospital in San Benedetto del Tronto. Objective. Primary outcomes are the reduction of time interval between the first patient contact and each phase of the diagnostic and therapeutic pathway. Secondary outcomes are the assessment of the number and frequency of contacts between the patient and the ONN, as well as of the number and type of organized procedures, and patient satisfaction assessment. Methods. A prospective cohort study was conducted with a historical control group represented by the same number of cancer patients treated in the same surgical unit in the previous year and not followed by an ONN. The patients’ contacts were registered within a dedicated mobile number. A questionnaire on patient satisfaction with cancer care was completed and administered to patients in a blinded manner by a third party to avoid bias. Results. The study group included 30 cancer patients. The average time from first contact to primary diagnostic test was 8 days versus 20 days in 2022 (control group). All patients underwent discussion by a multidisciplinary team (MDT), and the time to reach the MDT discussion was not different between the 2 groups. Five patients were deemed ineligible for surgery, and the average time to referral to a medical oncologist was 2 days in the study compared to 10 days in the control group. Twenty-five patients were eligible for initial surgery, and the mean time from first contact to surgery was 23 days after ONN establishment compared with 45 days in the control group. Each patient had an average of 10 phone calls with the ONN. For patients with a first diagnosis of cancer, the ONN organized an average of 4 tests. A patient satisfaction questionnaire achieved a response rate of 100%, with an average score of 85.2/90. Conclusion. The data demonstrate that ONN service is effective and may improve the quality and outcomes of the management path of surgical oncology patients. The professional role of the ONN, with predefined technical and non-technical skills, should also be officially recognized by the healthcare system and hospital administration.

A CASE STUDY ON CYCLIST ROUTE CHOICE MODEL

This research focuses on improving cycle path design by analyzing the determinants of route choice for bicycle users. The study uses a large GPS dataset to develop a route choice model for the inner-city of Amsterdam. The main research question is to identify the factors that influence route choice decisions, which is broken down into sub-questions related to the contribution of the GPS dataset to route choice models, the relevant variables for bicycle users' route choices, and the effect of these variables on route choice. The study finds that route attributes, such as distance and cycle path quality, and contextual variables, such as trip type and time of day, have a significant effect on bicycle users' route choices. The results provide insights for designing more desirable bicycle infrastructure in urban areas.

DBVS-APF-RRT*: A global path planning algorithm with ultra-high speed generation of initial paths and high optimal path quality

During the last decade, RRT* algorithm has been widely concerned by researchers because of its asymptotic optimality. However, the slow convergence rate of the RRT* algorithm leads to poor quality of the optimal paths at small number of iterations. Meanwhile, searching the initial path slowly limits its application scenarios. To overcome these problems, a directionally biased variable step APF-RRT* (DBVS-APF-RRT*) algorithm is proposed in this paper. Firstly, a novel directionally biased variable step sampling strategy is used in RRT* algorithm to quickly generate the initial path. Then, global random sampling and key region sampling strategies are added to improve global search ability and optimal path quality. At the same time, an artificial potential field (APF) method is introduced to improve the ability of obstacle avoidance. In addition, we propose a pruning strategy based on triangular inequality with direct connection of goal points to reduce the number of redundant nodes and shorten the path length. Finally, DBVS-APF-RRT* algorithm is compared with RRT*, Bias-RRT*, Informed-RRT* and Bias-P-RRT* algorithms to verify its superiority of optimal path quality, stability of the algorithm and its rapidity of finding the initial path.

Quality Competition Among Internet Service Providers

Internet service providers (ISPs) have a variety of quality attributes that determine their attractiveness for data transmission, ranging from quality-of-service metrics such as jitter to security properties such as the presence of DDoS defense systems. ISPs should optimize these attributes in line with their profit objective, i.e., maximize revenue from attracted traffic while minimizing attribute-related cost, all in the context of alternative offers by competing ISPs. However, this attribute optimization is difficult not least because many aspects of ISP competition are barely understood on a systematic level, e.g., the multi-dimensional and cost-driving nature of path quality, and the distributed decision making of ISPs on the same path. In this paper, we improve this understanding by analyzing how ISP competition affects path quality and ISP profits. To that end, we develop a game-theoretic model in which ISPs (i) affect path quality via multiple attributes that entail costs, (ii) are on paths together with other selfish ISPs, and (iii) are in competition with alternative paths when attracting traffic. The model enables an extensive theoretical analysis, surprisingly showing that competition can have both positive and negative effects on path quality and ISP profits, depending on the network topology and the cost structure of ISPs. However, a large-scale simulation, which draws on real-world data to instantiate the model, shows that the positive effects will likely prevail in practice: If the number of selectable paths towards any destination increases from 1 to 5, the prevalence of quality attributes increases by at least 50%, while 75% of ISPs improve their profit.