Safe Movement Research Articles

BPT‐Planner: Continuous Behavior Perception and Robot Trajectory Planner in Pathological Collaboration Experiments

Current research in human‐robot collaboration has yielded promising results, enabling us to predict human motion intentions and facilitate safe robot movement in shared spaces, avoiding collisions. However, achieving truly efficient human‐robot collaboration remains a complex and challenging endeavor. It entails not only imbuing robots with an understanding of human behavior but also empowering them to actively cooperate with human subjects and accomplish specific tasks. Against this backdrop, this paper introduces a comprehensive behavior perception and robot trajectory planner (BPT‐Planner). Leveraging continuous image data as input, the planner utilizes a CNN‐RNN network to predict human subjects' behavioral intentions, thereby gaining deeper insights. Based on these predictive outcomes and human‐robot action pairings, the planner generates a motion space for the robot. Subsequently, through the application of the proximal policy optimization algorithm, the planner dynamically optimizes the robot's action strategies to generate optimal motion trajectories. This optimization process takes into full consideration the behavior of human subjects, ensuring seamless integration of robot movement with human actions. To validate the performance of the BPT‐Planner, we conducted offline training and real‐time online validation in a pathological experiment scenario. Additionally, we conducted comparative experiments using datasets from other scenarios to further corroborate the planner's efficacy. Through the endeavors of this study, we not only showcase the exceptional performance of the BPT‐Planner across diverse scenarios but also affirm its applicability and effectiveness in various application environments. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Obstacle Avoiding Robot using Arduino Uno

An obstacle-avoiding robot has been developed to navigate autonomously in various environments. The robot uses sensors to detect obstacles in its path and adjusts its movement to avoid collisions by either changing direction or stopping. By following a set of simple decision-making rules, the robot can effectively navigate through different spaces. Extensive testing in diverse environments demonstrates its ability to move safely and efficiently. The research aims to improve autonomous robots, with potential applications in home assistance, industrial setting, and other areas requiring safe and reliable navigation. Key Words: Obstacle-avoiding robot, Autonomous navigation sensors, collision avoidance, safe movement, Decision-making rules, reliable navigation

Analysis of the efficiency of suburban passenger transportation on the Lviv railway sections

A developed transport system ensures the population's needs in safe movement and guarantees stable functioning and development of the production sphere. Despite being consistently unprofitable, suburban transportation by railway remains attractive for many population categories. The developed railway network, the low cost of transportation, and the capacity reserve of most sections partially compensate for the existing shortcomings and contribute to further development and open new prospects for attracting additional users of transport services. The main problems of suburban passenger transport, which worsen the quality of the services provided and affect the competitiveness of railway suburban transport compared to other modes of transport, are examined in this article. During the analysis of the schedule of suburban trains departing and arriving at the Lviv station, irrational management decisions were found that left room for improvement. In addition, the load factor of rolling stock on the routes Lviv-Sambir-Sianky and Sianky-Sambir-Lviv was determined, and it dropped to 40% during the follow-up, which indicates inefficient use of rolling stock. The ratio of the time the train stays at the stops to the total duration of the route in suburban traffic is also calculated. The time on the road for the Sambir- Stryi route was 69%, and the technical speed was only 21.7 km/h. The results obtained in the article are also typical for many other monitoring areas, indicating this problem's repeatability. The positive experience of European countries in solving similar problems allows us to react correctly to the situation and take short-term measures to change the dynamics to a positive one without additional time losses. It is also worth noting that a set of measures to improve the services provided to passengers must be carried out according to the requirements of the Technical Specifications for Interoperability, which must be ensured upon joining the European Union. Further investigations should analyze the flow of passengers at all stations and stopping points to identify the main points of creating passenger flow on researching routes.

Checking the coupling between overhead crane hook and steel ladle trunnion in Steelmaking Plant using convolutional neural networks

Overhead cranes are equipment designed for the efficient and safe movement of large loads and are widely used in the industry. They play a very important role in logistics and production by allowing the flow of materials, machinery, and products from one location to another, whether within a factory, warehouse, shipyard, or any other industrial environment. These equipment have a simple mechanical structure, consisting of a main beam equipped with wheels that allow movement along tracks. A hoist is suspended under the main beam, which can also move along it, and is used to lift and lower loads. The element that couples the load to the lifting system varies according to the type of load.In this article, we explore the coupling system commonly used in the steel industry for handling steel ladles through overhead cranes, called hooks and bails. This study proposes the use of artificial intelligence (AI) and machine learning techniques, through computer vision based on convolutional neural networks to detect the coupling, segmenting both the hook and the bail and thereby determining the precise alignment of the assembly before lifting. This contributes to validating the operator's visual information, reducing possible human errors, and promoting greater safety for both the process and the people involved.

Traffic stream characteristics at mid-block locations with un-controlled pedestrian crossings: a case study of Bengaluru city

Abstract At un-signalized midblock locations, the haphazard and uncontrolled crossing of pedestrians affects the vehicular traffic on the road, its speed, flow and density. This also affects the level of service on the road. This article presents a case study to analyze the same. As a process of transportation system planning to identify the cause and to give short-term and long-term remedies for the safe movement of pedestrians without causing hindrance to the traffic stream a case study was carried out in a metropolitan city of Bengaluru (Karnataka). Six un-signalized midblock locations with different roadway conditions in the city were studied. Analysis through the ANOVA test shows that pedestrian crossing at such locations significantly affects the flow characteristics. The speeds of vehicles as well as traffic flow rate are found to reduce while vehicular density is found to increase. The effect on heavy vehicles (trucks, buses) is found to be insignificant. The stream speed reduction in the range of 7% to 44% is observed at the pedestrian crossing sections compared to the stream speeds at sections without pedestrian crossings of the same study locations. The capacity of the roadway under consideration decreases only if pedestrian crossings are significant (more than 7% of total traffic). The effect is, further, pronounced with an increase in the number of pedestrians crossing per unit time. Pedestrians crossing with baggage are found to have more effect as compared to those crossing the road stretches without baggage. The study highlights the importance of proper planning, design, construction and maintenance of pedestrian crossing facilities and barriers in the metropolitan city. A multi-criteria decision-making method (MCDM) called Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) was adopted to prioritise the planning and improvement of the study locations based on different criteria to minimize the effect of such pedestrian crossings.

Framework of Scan to Building Information Modeling for Geometric Defect Localization in Railway Track Maintenance

Railway transportation plays a vital role in modern society, enabling the safe and efficient movement of people and goods over long distances. To ensure the longevity and safety of a railway infrastructure, the regular maintenance of tracks is crucial. Traditional track inspections, conducted manually to monitor geometric parameters and to identify defects, are time-consuming, labor-intensive, and prone to human error. Current Scan-to-BIM frameworks for railway maintenance also lack standardized methods for extracting geometric parameters that can be easily integrated into Building Information Modeling (BIM). Additionally, the Industry Foundation Classes (IFC) standard, used for BIM data exchange, does not support storing parameter values at specific chainage points along the track, limiting defect localization. A framework is proposed to address these challenges by standardizing the extraction of geometric parameters from point cloud data and ensuring seamless integration with BIM. The framework calculates parameters at station chainage points and generates additional chainage points along the track, associating the data with the corresponding chainage. A case study demonstrates the framework’s ability to enhance defect localization, using the EN 13848-5 European Standard to identify defects at specific chainages. Ultimately, this approach contributes to the more effective lifecycle management of railway tracks.

AUTOMATED INDOOR ACTIVITY MONITORING FOR ELDERLY AND VISUALLY IMPAIRED PEOPLE USING QUANTUM SALP SWARM ALGORITHM WITH MACHINE LEARNING

Elderly and vision-impaired indoor activity monitoring uses sensor technology to monitor interaction and movement in the living area. This system can identify deviations from regular patterns, provide alerts, and ensure safety in case of emergencies or potential risks. These solutions enhance quality of life by promoting independent living while offering peace of mind to loved ones and caregivers. Visual impairments have become the chief part of these problems over the past few years. Usually, visually impaired people seek help from others to keep doing their daily tasks. An automated human activity recognition (HAR) technique could enhance a vision-impaired person’s transaction activity and safe movement. HAR using deep learning (DL) includes training neural networks to automatically detect and classify various activities carried out by humans based on sensor information. By leveraging DL approaches, the HAR system could accurately identify and classify activities, including sitting, standing, walking, or running, from the information captured by sensors or wearable devices. This system is integral to smart home automation, healthcare, and sports analytics. This study develops an Automated Fractal Quantum Salp Swarm Algorithm with Machine Learning based HAR (QSSA-MLHAR) for assisting elderly and visually impaired people. The QSSA-MLHAR technique uses sensory input data to identify human activities in diverse classes. In the QSSA-MLHAR technique, the min–max scalar is primarily used to scale the input data. Besides, the QSSA-MLHAR technique applies a mixed extreme learning machine (M-ELM) model to classify various human actions. The QSSA-based hyperparameter tuning process is used to improve the detection results of the M-ELM system. A wide range of simulations was performed to ensure the better performance of the QSSA-MLHAR method. The comprehensive outcomes assured that the QSSA-MLHAR technique performs well compared to other models.

CVT-based swarming techniques for convex and non-convex cases

In the multi-robot swarm control algorithm, the traditional rigid formation algorithm has low formation redundancy, and it is difficult to flexibly adapt to the external environment. In this study, centroidal Voronoi tessellation (CVT) was used to control a group of robots for forming the required flexible formations and increasing the structural redundancy and internal carrying capacity of the multi-robot system. When the CVT area is divided in a non-convex environment, the Voronoi and non-convex areas will overlap, which will cause the robot to collide with the obstacles and hinder the optimization of the cost function. In this study, constrained CVT was used to solve the problem of region overlap in non-convex environments. The proposed path planning algorithm ensured the safe movement of the robot and explored the applicability of the algorithm in different scenarios. Finally, the performance of the algorithm in the non-convex area was verified and discussed by analyzing the simulation and experimental results for the convex and non-convex environments. The results showed that the constrained CVT could control the robot to generate the formation as quickly as possible in the non-convex area, and it was easy to dynamically switch the formation according to the external environment.

Efficacy of Plant Tissue Culture Techniques for Eliminating Black Mulberry Idaeovirus (BMIV) from Infected Black Mulberry (Morus nigra).

Obtaining virus-free plants is a crucial step in disease management that enables reliable and profitable fruit farming. The present study applied various in vitro virus elimination protocols, including apical shoot culture, chemotherapy, thermotherapy, cryotherapy, and their combination, to eliminate black mulberry Idaeovirus (BMIV) from sour black mulberry. First, a shoot tip (0.5-2 mm) culture protocol was optimized, and four ribavirin concentrations (0, 10, 20, and 30 mg/L) were investigated over five weeks as a form of chemotherapy (ch). For the first thermotherapy treatment (Ch + Th1st), chemotherapy treatment was followed by a gradual increase in the temperature (24-33 °C). In another experiment (Th2nd + Ch), in vitro shoots were incubated in the dark for two weeks at two different temperatures (35 ± 1 °C and 37 ± 1 °C, for one week each). Subsequently, the shoot tips were incubated with various ribavirin doses. Finally, cryotherapy (Cr) was used with or without immersing the shoot tips in liquid nitrogen. A two-step RT-PCR was performed to assess the presence of the virus in 7-8-week-old in vitro plants. Th2nd + Ch significantly increased the shoot tip burst and plant survival/morphogenesis compared to the other treatments. Except for the application of cryotherapy, the protocols eliminated BMIV in different proportions, and the highest virus elimination rate (50%) was obtained by applying 30 mg/L ribavirin during the Ch + Th1st treatment. These findings are essential in preventing the dissemination of the virus and enabling the safe movement of germplasm around the world.

Pedestrian safety behavior modeling method in human-vehicle-intelligence shared road space

In the context of the development of autonomous vehicles sharing road resources with traditional traffic participants in the future, modeling the safe behavior of pedestrians in the shared road space between people, vehicles and intelligence is crucial for the simulation development and reliability and safety testing of autonomous vehicles. To meet this demand, the safe movement behavior of pedestrians is first analyzed, and on this basis, the traditional pedestrian social force model is improved to construct a momentum model for the interactive movement of pedestrians with other pedestrians, traditional vehicles and autonomous vehicles in the shared road space; then, the safety parameters of the model are calibrated using a data set of human-vehicle interactive movement and a genetic algorithm, and a typical shared road space scenario is built to verify the effectiveness of the model. The simulation results show that the model can simulate the real safe movement behavior of pedestrians, and has better simulation effects when used in tasks such as human-vehicle-intelligence shared road space simulation.

Аналіз підходів щодо оцінювання просочування опадів у ґрунт узбіччя автомобільних доріг

Introduction. The article investigates the impact of atmospheric precipitation on road structures, specifically focusing on water infiltration into the roadside soil along highways, which is a critical aspect of road infrastructure maintenance. Ensuring the reliable operation of the roadway is becoming increasingly important due to rising traffic loads and climate changes that result in increased precipitation. Continuous moisture penetration into the road embankment can lead to road degradation, creating risks for safe traffic movement and necessitating frequent repairs. It is essential to develop and implement effective methods for assessing moisture infiltration into the soil, which will help ensure the longevity and cost-efficiency of road operation. Problem Statement. With climate change and increasing intensity of precipitation, the issue of assessing its impact on road structures becomes more relevant. Moisture accumulation in the road embankment can disrupt the stability of the soil foundation, reducing the load-bearing capacity of the road surface. Research aimed at analyzing rainfall infiltration and its effect on road structures will allow the implementation of effective solutions to maintain the operational condition of roads.

Systems of autonomous running of urban electric transport

The autonomous running system (ARS) is crucial for urban electric transport, ensuring the movement of electric rolling stock even when access to the contact network is lost owing to emergencies or repair work. Thanks to the ARS, the vehicles can maintain their set speeds, providing passengers with comfortable and safe movements. The autonomous running allows vehicles to remain mobile and accessible to passengers, even without power supply from the contact network. ARS increases the reliability and efficiency of urban electric transport, ensuring a more stable operation of the transportation system in the city. This article aims to review the transport systems of urban electric transport and assess their development potential by integrating an ARS into the rolling stock.

Safe and Reliable Movement of Fast LiDAR-based Self-driving Vehicle

Classification of objects is an important technique for autonomous ground vehicles to identify a surrounding environment and execute safe path planning. In this paper, a method based on horizontal segmentation is proposed to detect cone-shaped objects in vehicle’s vicinity using a LiDAR sensor. A captured point cloud is divided into five layers based on height information, and the division of detected objects into two groups, cones and others, has been made using classifiers available in MATLAB toolboxes. To separate the classified conical objects into four types used to mark the route, an algorithm for their recognition was developed and used. The proposed solution, verified by navigation experiments in real conditions using an unmanned racing car, has gave good results, i.e., a high rate of cone-shaped objects classification, a short processing time and a low computational load. The performed tests have allowed also to diagnose the causes of incorrect classification of objects. Thus, the experimental results indicated that the approach presented in this work can be used in real time for autonomous, collision-free driving along marked routes.

Снижение числа несчастных случаев и аварий при управлении поездом в автоматическом режиме

Railroad transport is the main element of economic development of Russian regions; it ensures reliable communication between industrial enterprises and raw material supply bases in Russia. Transportation of enterprise employees and freights is an integral part of the successful functioning of any industrial company. Ensuring safe conditions of train traffic is a matter of high priority for all participants in the transportation process. The article provides external and internal parameters affecting the safety of rolling stock. The impact of external factors, such as weather conditions, optical visibility, illumination, and air transparency on the operation of technical vision systems when driving a train in automatic mode have been analyzed. Taking the impact of these factors into account is necessary to limit the speed of rolling stock in the sectors of low visibility and sharp reduction of visibility range of obstacles, which is crucial to ensure safe train movement when calculating the braking curve. Such a dependence requires the introduction of correcting coefficients into algorithms of the train movement model and considering the correction when calibrating and configuring computers and devices of the technical vision system. In order to determine the internal parameters of rolling stock, an algorithm for the calculation of the coordinates of a technical object has been proposed. For a moving object, the coordinates are calculated on the orthodromic trajectory using the existing dependence between the current longitude and latitude of the object and the speed projections measurements of the rolling stock on the axis of the geographic coordinate system. The proposed options to determine the rolling stock parameters improve the quality and accuracy of determining speed and coordinates and reduce computing and hardware costs aboard a locomotive, which, in turn, affects the time of decision-making to ensure optimal control of rolling stock and prevention of accidents and emergencies.

Transformations in prefrontal ensemble activity underlying rapid threat avoidance learning.

The capacity to learn cues that predict aversive outcomes, and understand how to avoid those outcomes, is critical for adaptive behavior. Naturalistic avoidance often means accessing a safe location, but whether a location is safe depends on the nature of the impending threat. These relationships must be rapidly learned if animals are to survive. The prelimbic subregion (PL) of the medial prefrontal cortex (mPFC) integrates learned associations to influence these threat avoidance strategies. Prior work has focused on the role of PL activity in avoidance behaviors that are fully established, leaving the prefrontal mechanisms that drive rapid avoidance learning poorly understood. To determine when and how these learning-related changes emerge, we recorded PL neural activity using miniscope calcium imaging as mice rapidly learned to avoid a threatening cue by accessing a safe location. Over the course of learning, we observed enhanced modulation of PL activity representing intersections of a threatening cue with safe or risky locations and movements between them. We observed rapid changes in PL population dynamics that preceded changes observable in the encoding of individual neurons. Successful avoidance could be predicted from cue-related population dynamics during early learning. Population dynamics during specific epochs of the conditioned tone period correlated with the modeled learning rates of individual animals. In contrast, changes in single-neuron encoding occurred later, once an avoidance strategy had stabilized. Together, our findings reveal the sequence of PL changes that characterize rapid threat avoidance learning.

A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm

The genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection, and it is known for its iterative optimization capabilities in both constrained and unconstrained environments. In this paper, a novel method for GA-based dual-automatic guided vehicle (AGV)-ganged path planning is proposed to address the problem of frequent steering collisions in dual-AGV-ganged autonomous navigation. This method successfully plans global paths that are safe, collision-free, and efficient for both leader and follower AGVs. Firstly, a new ganged turning cost function was introduced based on the safe turning radius of dual-AGV-ganged systems to effectively search for selectable safe paths. Then, a dual-AGV-ganged fitness function was designed that incorporates the pose information of starting and goal points to guide the GA toward iterative optimization for smooth, efficient, and safe movement of dual AGVs. Finally, to verify the feasibility and effectiveness of the proposed algorithm, simulation experiments were conducted, and its performance was compared with traditional genetic algorithms, Astra algorithms, and Dijkstra algorithms. The results show that the proposed algorithm effectively solves the problem of frequent steering collisions, significantly shortens the path length, and improves the smoothness and safety stability of the path. Moreover, the planned paths were validated in real environments, ensuring safe paths while making more efficient use of map resources. Compared to the Dijkstra algorithm, the path length was reduced by 30.1%, further confirming the effectiveness of the method. This provides crucial technical support for the safe autonomous navigation of dual-AGV-ganged systems.

Mapping opportunities and barriers for coexistence between people and pumas in the Argentine Dry Chaco

AbstractAimThe persistence of large carnivore populations depends on their survival outside protected areas, where they often impact local livelihoods through livestock depredation. Understanding the impacts of human behaviour on large carnivores in shared landscapes is thus important but is often overlooked in habitat assessments or conservation planning. We employed an integrated approach that considers human behaviour and landscape structure metrics to assess the potential for human‐puma (Puma concolor) coexistence in the Chaco region, a global deforestation and defaunation hotspot.LocationArgentine Dry Chaco (~490,000 km2).MethodsWe identified suitable puma habitat patches and movement areas using occupancy modelling and combined it with a spatial human‐puma conflict risk model based on interview data to identify ‘safe’ and ‘unsafe’ habitat patches. We then used resistance surfaces to identify ‘safe’ and ‘unsafe’ movement areas, as well as ‘severed’ movement areas where anthropogenic land conversion inhibits movement.ResultsSafe puma habitat patches (i.e., suitable and safe) covered 29% of the region, whereas attractive sinks (i.e., suitable but risky) represented 12%. Movement areas corresponded to 60% of the region, while conflict risk and high landscape resistance undermined connectivity: unsafe and severed movement areas covered 10% and 11% of the region, respectively. Nearly 98% of safe habitat and movement areas occurred outside protected areas.Main ConclusionsWe provide an integrated conceptual framework and spatial explicit template for a three‐pronged conservation strategy to (1) protect safe habitat and movement areas, (2) mitigate livestock depredation in attractive sinks and unsafe movement areas and (3) restore landscape in severed and matrix areas to improve ecological connectivity. This would allow pumas to maintain viable populations while reducing negative impacts on local people. More generally, we show how integrating habitat and conflict risk models can reveal opportunities and challenges for human‐carnivore coexistence beyond protected areas.

Lived experiences of students with blindness in Orientation and Mobility (O&M) skills at Haramaya University, Ethiopia: A phenomenological study

Orientation involves understanding or comprehending the location of the desired destination, while mobility pertains to safe and efficient movement within the environment. This study examines the life experiences of students with blindness at Haramaya University, Ethiopia, in learning and using Orientation and Mobility (O&M) skills. The research used a descriptive phenomenological design, with five participants purposively chosen based on their severity of vision loss and willingness. Based on the findings, three main themes emerged: formal learning and use, informal learning and use, and absence of learning and non-use. The study recommends appropriate teaching aids and techniques to improve O&M skills for individuals with blindness and to enhance the lives of individuals with blindness.

Comparative Study of SFPE and Steering Modes in Pathfinder to Optimise Evacuation Routes

This paper investigates the possibilities of using an agent-based evacuation model to analyse the formation of queues at the exits of enclosed spaces and the evacuation process of people. The aim is to investigate how the density of people in a crowd affects the safe movement of people and how the width, number, and location of exits affect evacuation time. The analysis was carried out using the Pathfinder evacuation model, which provides two modes to simulate the movement of people: steering and SFPE. An enclosed space of 20 m × 30 m was investigated. First, the differences between the modes of the evacuation model to simulate the movement of people were compared. Then, the steering mode with limited door flow was set and the effect of the width, number, and location of the exits with a total width of 4 m on the evacuation time was investigated. The results of this study highlight differences in the simulation of the movement of people according to the different modes and provide valuable information for the design of safe escape routes. Proper design of escape routes can prevent an adverse situation that could arise when evacuating a large number of people.

Identifying priority corridors and bottlenecks for three threatened large mammal species in the oil palm-dominated landscape of Peninsular Malaysia

Agricultural landscape expansion and land development have disrupted wildlife habitats and movement pathways, limiting species’ access to critical resources. Identifying multispecies corridors across human-dominated landscapes is crucial to facilitate safe and unobstructed movement while minimizing negative interactions between humans and wildlife. We conducted a spatial distribution analysis to delineate potential high conflict areas and assess connectivity for the movement of focal species across Peninsular Malaysia. Using conflict data for the Asian elephant (Elephas maximus), Malayan sun bear (Helarctos malayanus), and Malayan tiger (Panthera tigris) spanning an eight-year period (2013–2020), we predicted conflict risk distribution with MaxEnt. We then overlaid the resulting risk maps onto a unified resistance surface and predicted least-cost corridors between high conservation value (HCV) patches. We ranked these corridors by their ecological importance in a plantation landscape. We identified pinch point areas (bottlenecks) to denote locations most critical to species movement along the corridors. Our results suggested that potential high conflict areas were primarily located in eastern and southern Peninsular Malaysia. We identified 89 HCV core patches that were interconnected by 126 linkages. The mean length of the least-cost corridors was 13.41 km. Among the top 10 linkages ranked by centrality value, three crossed commercial plantations for <30 % of their length, and three were highly bottlenecked. This study underscores an ecological approach that considers human–wildlife conflict and habitat connectivity analysis for effective land use planning and critical zone designation.