Presence Of Obstacles Research Articles

Smart Stick Navigation System for Visually Impaired Based on Machine Learning Algorithms Using Sensors Data

Visually Impaired People (VIP) face significant challenges in their daily lives, relying on others or trained dogs for assistance when navigating outdoors. Researchers have developed the Smart Stick (SS) system as a more effective aid than traditional ones to address these challenges. Developing and utilizing the SS systems for VIP improves mobility, reliability, safety, and accessibility. These systems help users by identifying obstacles and hazards, keeping VIP safe and efficient. This paper presents the design and real-world implementation of an SS using an Arduino Nano microcontroller, GPS, GSM module, heart rate sensor, ultrasonic sensor, moisture sensor, vibration motor, and Buzzer. Based on sensor data, the SS can provide warning signals to VIP about the presence of obstacles and hazards around them. Several Machine Learning (ML) algorithms were used to improve the SS alert decision accuracy. Therefore, this paper used sensor data to train and test ten ML algorithms to find the most effective alert decision accuracy. Based on the ML algorithms, the alert decision, including the presence of obstacles, environmental conditions, and user health conditions, was examined using several performance metrics. Results showed that the AdaBoost, Gradient boosting, and Random Forest ML algorithms outperformed others and achieved an AUC and specificity of 100%, with 99.9% accuracy, F1-score, precision, recall, and MCC in the cross-validation phase. Integrating sensor data with ML algorithms revealed that the SS enables VIP to live independently and move safely without assistance.

Expression of Concern: Impact of cavity tilt angle and magnetic field on the entropy generation of Cu/water nanofluid in a rectangular cavity in the presence of several constant-temperature obstacles

Expression of Concern: Impact of cavity tilt angle and magnetic field on the entropy generation of Cu/water nanofluid in a rectangular cavity in the presence of several constant-temperature obstacles

Pit builders are assisted by walls to find suitable sites for pit construction but are slowed down by obstacles and their initial distance from the destination

Abstract Habitat selection plays a critical role in the survival and reproduction of animals, with implications for all important behaviours. Animals are often limited in their familiarity with their habitat and rely on their senses and the habitat structure to detect suitable sites to settle in. We focus here on a trap‐building predator, the wormlion, which constructs pitfall traps in loose soil, and examine how the distance from suitable microhabitats and the presence of obstacles affect its habitat selection. We conducted four experiments. Experiment 1 assessed the impact of distance on the probability of wormlions reaching a suitable microhabitat. Experiment 2 examined the interaction between distance and obstacles. Experiment 3 investigated the influence of walls on the probability of reaching destinations. Experiment 4 evaluated walls as either leading paths or barriers. Our results showed that increasing distance from the destination negatively affected the probability of wormlions reaching it, with larger individuals more likely to cover greater distances. Obstacles interacted with distance, generally decreasing movement when present. Walls positively influenced orientation, aiding wormlions in reaching suitable sites, either by following the wall or locating the destination along the wall. The function of walls varied, sometimes facilitating movement and sometimes decreasing it, depending on their orientation and context. These findings suggest that walls may serve as navigational aids for wormlions in urban environments, assisting them in locating suitable microhabitats. This has implications for understanding habitat selection in urban ecosystems. Future research should explore the long‐term effects of walls on wormlion survival and habitat quality in more natural settings.

A Level-Set/FEM approach for particle bed growth in Stokes–Darcy suspension filtration

This article presents an innovative approach to modeling particle bed growth through suspension dead-end filtration, a process of paramount importance in various industrial and environmental contexts. More precisely, it focuses on the numerical formulation of a continuous approach based on a Stokes–Darcy coupling. In a Level-Set/FEM frame, this model effectively captures the dynamic evolution of particle beds (cake), taking into account the interaction between suspended particles and porous media. Model validation is also a key focus of this paper. Numerical tests are carried out under various conditions, and their results are compared with analytical solutions and experimental data drawn from the literature. This permits corroboration of the predictive capabilities of the model, confirming the robustness and accuracy of the proposed approach. Firstly, Stokes–Darcy coupled flows are investigated in simplified cases such as rectangular geometries and coaxial cylinders. These scenarios serve as benchmarks to assess the model performance under simple and varied conditions. Additionally, the more challenging case of a three-dimensional anisotropic flow between two ellipsoids is addressed, where the evolution of the cake takes place in three dimensions. Through rigorous analysis and comparison with analytical solutions, the model is proved efficient in capturing the inherent complexities of such scenarios. Finally, richer two-dimensional Stokes–Darcy flows are considered, in the presence of both impermeable and permeable obstacles, representing a crucial step towards modeling real industrial processes. This last study highlights not only the formation of particle-free zones but also the practical relevance of this work.

Tomato bacterial wilt disease outbreaks are accompanied by an increase in soil antibiotic resistance

The presence of soil-borne disease obstacles and antibiotic resistance genes (ARGs) in soil leads to serious economic losses and health risks to humans. One area in need of attention is the evolution of ARGs as pathogenic soil gradually develops, which introduces uncertainty to the dynamic ability of conventional farming models to predict ARGs. Here, we investigated variations in tomato bacterial wilt disease accompanied by the resistome by metagenomic analysis in soils over 13 seasons of monoculture. The results showed that the abundance and diversity of ARGs and mobile genetic elements (MGEs) exhibited a significant and positive correlation with R. solanacearum. Furthermore, the binning approach indicated that fluoroquinolone (qepA), tetracycline (tetA), multidrug resistance genes (MDR, mdtA, acrB, mexB, mexE), and β-lactamases (ampC, blaGOB) carried by the pathogen itself were responsible for the increase in overall soil ARGs. The relationships between pathogens and related ARGs that might underlie the breakdown of soil ARGs were further studied in R. solanacearum invasion pot experiments. This study revealed the dynamics of soil ARGs as soil-borne diseases develop, indicating that these ecological trends can be anticipated. Overall, this study enhances our understanding of the factors driving ARGs in disease-causing soils.

Multi-UAVs Tracking Non-Cooperative Target Using Constrained Iterative Linear Quadratic Gaussian

This study considers the problem of controlling multi-unmanned aerial vehicles (UAVs) to consistently track a non-cooperative ground target with uncertain motion in a hostile environment with obstacles. An active information acquisition (AIA) problem is formulated to minimize the uncertainty of the target tracking task. The uncertain motion of the target is represented as a Wiener process. First, we optimize the configuration of the UAV swarm considering the collision avoidance, horizontal field of view (HFOV), and communication radius to calculate the reference trajectories of the UAVs. Next, a novel algorithm called Constrained Iterative Linear Quadratic Gaussian (CILQG) is introduced to track the reference trajectory. The target’s state with uncertainty and the UAV state are described as beliefs. The CILQG algorithm utilizes the Unscented Transform to propagate the belief regarding the UAVs’ motions, while also accounting for the impact of navigation errors on the target tracking process. The estimation error of the target position of the proposed method is under 4 m, and the error of tracking the reference trajectories is under 3 m. The estimation error remains unchanged even in the presence of obstacles. Therefore, this approach effectively deals with the uncertainties involved and ensures accurate tracking of the target.

Spatial deprivation of children with autism spectrum disorders in the arctic city

The inclusion of children with autism spectrum disorders in society is an urgent social task that, due to its complexity, requires new perspectives of scientific research for the sound regulation of inclusive processes. The problem of the study was determined by the contradiction between the social significance of the development of inclusion and the risks of including children with ASD in the urban space existing in the Arctic city. We have introduced the category of spatial deprivation of children with autism spectrum disorders into the research field, and proposed its author's interpretation. The empirical study was aimed at identifying the state of inclusion of children with ASD in the space of the Arctic city. The results of the study showed their significant spatial deprivation in the Arctic city. It manifests itself in relation to public, semi-public spaces, spaces for movement, expressed both in their insufficient accessibility and in a significant limitation of forms of development. The conclusion is made about the low social status of children with ASD in the Arctic city, as well as about the presence of obstacles to their realization of their universal needs. This situation is mainly determined by socio-psychological and organizational factors and objectifies the difficulties of developing inclusive processes.

TSANet: Forecasting traffic congestion patterns from aerial videos using graphs and transformers

Forecasting traffic congestion patterns in lane-less traffic scenarios is a complex task because of the combination of high & irregular vehicle densities, fluctuating speeds, and the presence of environmental obstacles. Existing techniques like vehicle counting and density prediction, which successfully estimate congestion in lane-based traffic, are unsuitable for lane-less traffic scenarios due to the irregular and unpredictable nature of traffic density patterns. To overcome these challenges, we propose traffic states to measure congestion patterns in lane-less traffic scenarios. Each traffic state is characterized by the spatio-temporal distribution of neighbouring road users, including vehicles and motorcyclists. We employ traffic graphs to capture the spatial distribution of neighbouring road users. Also, we propose a novel method for the automated construction of traffic graphs by leveraging the detection and tracking of individual road users in aerial videos. Further, in order to incorporate the temporal distribution, we utilize a transformer model to capture the evolution of spatial traffic graphs over time. This enables us to forecast future spatio-temporal distributions and their associated traffic states. Our proposed model, named Traffic State Anticipation Network (TSANet), can effectively forecast future traffic states by analysing sequences of current traffic graphs, thereby enhancing our understanding of evolving traffic patterns in lane-less scenarios. Also, to address the lack of publicly available lane-less traffic datasets, we introduce EyeonTraffic (EoT), a large-scale lane-less traffic dataset containing three hours of aerial videos captured at three busy intersections in Ahmedabad city, India. Experimental results on the EoT dataset demonstrate the efficacy of our proposed TSANet in effectively anticipating traffic states across diverse spatial regions within an intersection. In addition, we also show that TSANet generalizes well for previously unseen intersections, making it suitable for analysing various traffic scenarios without the need for explicit training, thereby enhancing its practical applicability.

Numerical study on performance enhancement of a solid oxide fuel cell using gas flow field with obstacles and metal foam

This study investigates the impact of gas flow field design on the performance of a solid oxide fuel cell (SOFC). A three-dimensional numerical model of the cell and channel is developed to simulate the use of metal foam as flow distributor, along with the presence of obstacles in the gas flow channels. The model is calibrated using experimental data and applied to simulate four relevant cases combining metal foam and obstacles, compared to a straight channel structure. The results demonstrate the positive impact of flow-field modifications on the distribution of species along the cell's active layers. It is found that, even though the pressure drops are affected, reactant gases are more uniformly distributed across the active electrode of the cell, reducing mass transport losses and enhancing current density. Simulations performed at a cell voltage of 0.7 V indicate that incorporating a metal foam as flow distributor increases the maximum current density by 26 % compared to the conventional straight flow design. Furthermore, combining metal foam with obstacles results in the best performance, achieving a 34 % increase in the maximum current density.

Numerical study on the influence of venting interlayer structure on the explosion venting effects

Catering is an important business format of commercial complexes in Chinese Mainland, and most of its kitchens use natural gas as fuel. However, once natural gas leaks, it is very easy to cause explosion accidents. For kitchens without external windows in large commercial complexes, the horizontally arranged explosion venting interlayers should be installed to lead the explosion vents from the kitchens to the exterior walls of the buildings. The paper conducts modeling research on linear and 90° bend, as well as interlayers containing structural beams (obstacles), and obtains the explosion propagation laws of different explosion venting interlayer structures. After the flame enters the venting interlayer, the presence of obstacles can cause flame acceleration; the peak pressure in the kitchen shows an increasing-sudden decreasing-increasing trend as the distance between obstacles and the explosion outlet decreases. When the obstacle is located 2.5 m away from the interlayer inlet, the maximum flame propagation speed can reach 356.5 m/s. The effects of the bend structure on the propagation process of gas explosion venting have been investigated. The peak overpressure of 90° bend venting interlayer is 1.52 times that of the linear interlayer. However, there is a notable reduction in peak overpressure (approximately 26.6%) observed post-bend. Additionally, an increase in the distance preceding the bend results in an initial rise followed by a decline in peak overpressure, while the shock wave attenuation coefficient displays a trend of first increasing and then decreasing.

Globally Guided Deep V-Network-Based Motion Planning Algorithm for Fixed-Wing Unmanned Aerial Vehicles.

Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method.

Safe Autonomous Docking of Spacecraft: A RRT-combined Output-constrained Recursive Control Method

In addressing the challenges of short-range spacecraft docking in the presence of obstacles and disturbances, it is critical to integrate guidance and motion control to ensure autonomous and reliable operation. Traditional methods that separate these two layers often struggle with accurately tracking predefined paths, increasing the risk of collisions. In light of this, a proposed scheme integrating guidance and control in an organic manner has been put forth. This scheme employs the rapidly-exploring random trees (RRT) algorithm within the guidance layer to generate a collision-avoidance trajectory for the control layer, efficiently navigating the spacecraft towards its target. Then the control layer implements a second-order output-constrained controller by adding a power integrator and a novel barrier Lyapunov function (BLF) together, to guarantee that the tracking error of the predefined trajectory remains bounded and the system asymptotically converges to the target. To account for tracking errors, obstacle radii are expanded during path planning through a dilation constant. Based on theoretical derivation and simulation experiments, the effectiveness and advancement of the proposed method are validated.

Methodology for Identifying Optimal Pedestrian Paths in an Urban Environment: A Case Study of a School Environment in A Coruña, Spain

Improving urban mobility, especially pedestrian mobility, is a current challenge in virtually every city worldwide. To calculate the least-cost paths and safer, more efficient routes, it is necessary to understand the geometry of streets and their various elements accurately. In this study, we propose a semi-automatic methodology to assess the capacity of urban spaces to enable adequate pedestrian mobility. We employ various data sources, but primarily point clouds obtained through a mobile laser scanner (MLS), which provide a wealth of highly detailed information about the geometry of street elements. Our method allows us to characterize preferred pedestrian-traffic zones by segmenting crosswalks, delineating sidewalks, and identifying obstacles and impediments to walking in urban routes. Subsequently, we generate different displacement cost surfaces and identify the least-cost origin–destination paths. All these factors enable a detailed pedestrian mobility analysis, yielding results on a raster with a ground sampling distance (GSD) of 10 cm/pix. The method is validated through its application in a case study analyzing pedestrian mobility around an educational center in a purely urban area of A Coruña (Galicia, Spain). The segmentation model successfully identified all pedestrian crossings in the study area without false positives. Additionally, obstacle segmentation effectively identified urban elements and parked vehicles, providing crucial information to generate precise friction surfaces reflecting real environmental conditions. Furthermore, the generation of cumulative displacement cost surfaces allowed for identifying optimal routes for pedestrian movement, considering the presence of obstacles and the availability of traversable spaces. These surfaces provided a detailed representation of pedestrian mobility, highlighting significant variations in travel times, especially in areas with high obstacle density, where differences of up to 15% were observed. These results underscore the importance of considering obstacles’ existence and location when planning pedestrian routes, which can significantly influence travel times and route selection. We consider the capability to generate accurate cumulative cost surfaces to be a significant advantage, as it enables urban planners and local authorities to make informed decisions regarding the improvement of pedestrian infrastructure.

Efficient Motion Primitives-Based Trajectory Planning for UAVs in the Presence of Obstacles

The achievement of full autonomy in Unmanned Aerial Vehicles (UAVs) is significantly dependent on effective motion planning. Specifically, it is crucial to plan collision-free trajectories for smooth transitions from initial to final configurations. However, finding a solution executable by the actual system adds complexity: the planned motion must be dynamically feasible. This involves meeting rigorous criteria, including vehicle dynamics, input constraints, and state constraints. This work addresses optimal kinodynamic motion planning for UAVs in the presence of obstacles by employing a hybrid technique instead of conventional search-based or direct trajectory optimization approaches. This technique involves precomputing a library of motion primitives by solving several Two-Point-Boundary-Value Problems (TPBVP) offline. This library is then repeatedly used online within a graph-search framework. Moreover, to make the method computationally tractable, continuity between consecutive motion primitives is enforced only on a subset of the state variables. This approach is compared with a state-of-the-art quadrotor-tailored search-based approach, which generates motion primitives online through control input discretization and forward propagation of the dynamic equations of a simplified model. The effectiveness of both methods is assessed through simulations and real-world experiments, demonstrating their ability to generate resolution-complete, resolution-optimal, collision-free, and dynamically feasible trajectories. Finally, a comparative analysis highlights the advantages, disadvantages, and optimal usage scenarios for each approach.

Influence of triangular obstacles on droplet breakup dynamics in microfluidic systems

Microfluidic devices with complex geometries and obstacles have attracted considerable interest in biomedical engineering and chemical analysis. Understanding droplet breakup behavior within these systems is crucial for optimizing their design and performance. This study investigates the influence of triangular obstacles on droplet breakup processes in microchannels. Two distinct types of triangular obstructions, positioned at the bifurcation (case I) and aligned with the flow (case II), are analyzed to evaluate their impact on droplet behavior. The investigation considers various parameters, including the Capillary number (Ca), non-dimensional droplet length (L*), non-dimensional height (A*), and non-dimensional base length (B*) of the triangle. Utilizing numerical simulations with COMSOL software, the study reveals that the presence of triangular obstacles significantly alters droplet breakup dynamics. Importantly, the shape and location of the obstacle emerge as key factors governing breakup characteristics. Results indicate faster breakup of the initial droplet when the obstacle is positioned in the center of the microchannel for case I. For case II, the study aims to identify conditions under which droplets either break up into unequal-sized entities or remain intact, depending on various flow conditions. The findings identify five distinct regimes: no breakup, breakup without a tunnel, breakup with a tunnel, droplet fragmentation into unequal-sized parts, and sorting. These regimes depend on the presence or absence of triangular obstacles and the specific flow conditions. This investigation enhances our understanding of droplet behavior within intricate microfluidic systems and provides valuable insights for optimizing the design and functionality of droplet manipulation and separation devices. Notably, the results emphasize the significant role played by triangular obstacles in droplet breakup dynamics, with the obstacle’s shape and position being critical determinants of breakup characteristics.

A Novel Approach to Image Retrieval for Vision-Based Positioning Utilizing Graph Topology

Abstract. This research introduces a novel approach to improve vision-based positioning in the absence of GNSS signals. Specifically, we address the challenge posed by obstacles that alter image information or features, making retrieving the query image from the database difficult. While the Bag of Visual Words (BoVW) is a widely used image retrieval technique, it has a limitation in representing each image with a single histogram vector or vocabulary of visual words, i.e., the emergence of obstacles can introduce new features to the query image, resulting in different visual words. Our study overcomes this limitation by clustering the features of each image using the k-means method and generating a graph for each class. Each node or key point in the graph obtains additional information from its direct neighbors using functions employed in graph neural networks, functioning as a feedforward network with constant parameters. This process generates new embedding nodes, and eventually, global pooling is applied to produce one vector for each graph, representing each image with graph vectors based on objects or feature classes. As a result, each image is represented with graph vectors based on objects or feature classes. In the presence of obstacles covering one or more graphs, there is sufficient information from the query image to retrieve the most relevant image from the database. Our approach was applied to indoor positioning applications, with the database collected in Bolz Hall at The Ohio State University. Traditional BoVW techniques struggle to properly retrieve most query images from the database due to obstacles like humans or recently deployed objects that alter image features. In contrast, our approach has shown progress in image retrieval by representing each image with multiple graph vectors, depending on the number of objects in the image. This helps prevent or mitigate changes in image features caused by obstacles covering or adding features to the image, as demonstrated in the results.

MODELING THE DISTRIBUTION OF EMERGENCY RELEASE PRODUCTS AT A NUCLEAR POWER PLANT UNIT

Despite the fact that much attention is paid to the safe operation of nuclear power plants, there is a possibility of an accident with the release of radionuclides. This is especially true in Ukraine, where there is a threat of the damage to nuclear reactors as a result of military operations. It is impossible to research the distribution of products emergency releases radioactive substances in laboratory conditions. Therefore, the only tool for the development predicting of an accident is the modeling the spread of a radionuclides cloud. The purpose of the research is a modeling the distribution of emergency release products in a nuclear power plant unit, suitable for the operative assessment of a development an accident. Results of the research: The mathematical model of the distribution emission products of a nuclear power plant has been developed, which takes into account the value of the initial activity of emission products, the rate of the settling radioactive particles, the wind speed components, the intensity changes radionuclide emission over time. The technique for solving the boundary value problem of modeling in conditions of a complex shape of the computational domain, taking into account the presence of obstacles to the spread of emission products has been developed. The use of the velocity potential equation in evolutionary form allows us to speed up the calculation process. The chosen splitting scheme of an alternating-triangular method allows to find the speed potential according to the explicit form at each splitting step. This allowed software implementation of the CFD model. The visualized models of the emission cloud distribution allow to determine the radiation situation in any place of the emission product distribution zone. The developed model makes it possible to quickly predict the development of an accident in space and time, which makes it possible to take measures to protect people from exposure in the shortest possible time. Conclusions: The obtained emission cloud propagation models and their visualization make it possible to determine the state of environmental pollution under various initial conditions during the development of the accident.

A quantum Lévy flight optimization based peripheral Yolov5 vison for scaling and optimizing autonomous train operations during transmission

In Autonomous Train (AT) operations, predicting future movements of trains to ensure the safety and efficiency of this public transport, is a critical task performed in the yard and terminal stations or between the source station/terminal and destination. Artificial Intelligence (AI) enabled Autonomous Vehicles (AVs) analyse their surroundings and make informed decisions based on the collected information. AVs learn their surroundings through perceptual motion, and predict the movements of various agents, such as humans, bicycles, and animals, through the prediction phase. If the predictions are accurate, AVs take proactive measures, making travel safer for everyone. However, the uncertainty in predicting agent/object’s behaviour remains substantially difficult, despite significant research being conducted in this area. The objective of this study is to investigate the optimisation of AT Operations during Transmission (ATOT), with the goal of reducing wait times and ensuring safety even in the presence of obstacles, particularly animals. Quantum Lévy flight optimisation (QLFO) addresses these concerns on operational scalability of ATs. It generates real-time spatial data to make these operations efficient and to reduce the average waiting times. The YoLoV5 object recognition identifies the potential risks, such as individuals traversing rails or animals lying along the way and either rerouting or halting ATs to prevent crashes. The QLFO detects problems either before or after the train arrives at a junction or terminus and takes proactive measures using YoLov5 evaluation on the satellite image captured over the rail track. Evaluation experiments show that the proposed ATOT achieves a 9.3% reduction in the average waiting time spent.

Does frailty affect barriers to physical activity in patients with symptomatic peripheral artery disease? A cross-sectional study

BackgroundFrailty syndrome is prevalent among many patients experiencing intermittent claudication symptoms. Considering that components of the frailty syndrome can affect both physical and psychological functions, it is likely that barriers to physical activity are heightened in these individuals. AimTo analyze the association between barriers to physical activity and frailty in patients with symptomatic peripheral artery disease (PAD). MethodsThis cross-sectional study included 216 patients with symptomatic PAD (64.8% men, 65.6±9.4 yrs.). Nine personal and 8 environmental barriers to physical activity were investigated through a questionnaire on barriers to practicing physical activity in patients with intermittent claudication. Frailty was defined according to Fried et al. (2001) criteria which included unintentional weight loss, exhaustion, low grip strength, slow walking speed, physical inactivity. Three or more criteria defined frail, one or 2 criteria defined pre-frail, and absence of criteria defined non-frail. Data are presented as median (interquartile range). ResultsFrail and pre-frail patients have more barriers than non-frail patients [frail: 11 (4); pre-frail: 10 (6); non-frail: 8 (7), p = 0.001]. Absence of a companion for physical activity, lack of knowledge and uncertainty about the benefits of physical activity, pain induced by walking and presence of obstacles that worsen leg pain were associated with frail and pre-frail status, independent of sex, age, ankle-brachial index, and total walking distance. ConclusionPatients with PAD who are frail and pre-frail have more barriers to physical activity than non-frail patients. Therefore, specific interventions promoting PA are essential for these patients to improve their health outcomes.

POMDP-based probabilistic decision making for path planning in wheeled mobile robot

Path Planning in a collaborative mobile robot system has been a research topic for many years. Uncertainty in robot states, actions, and environmental conditions makes finding the optimum path for navigation highly challenging for the robot. To achieve robust behavior for mobile robots in the presence of static and dynamic obstacles, it is pertinent that the robot employs a path-finding mechanism that is based on the probabilistic perception of the uncertainty in various parameters governing its movement. Partially Observable Markov Decision Process (POMDP) is being used by many researchers as a proven methodology for handling uncertainty. The POMDP framework requires manually setting up the state transition matrix, the observation matrix, and the reward values. This paper describes an approach for creating the POMDP model and demonstrates its working by simulating it on two mobile robots destined on a collision course. Selective test cases are run on the two robots with three categories – MDP (POMDP with belief state spread of 1), POMDP with distribution spread of belief state over ten observations, and distribution spread across two observations. Uncertainty in the sensor data is simulated with varying levels of up to 10 %. The results are compared and analyzed. It is demonstrated that when the observation probability spread is increased from 2 to 10, collision reduces from 34 % to 22 %, indicating that the system's robustness increases by 12 % with only a marginal increase of 3.4 % in the computational complexity.