Path Deviation Research Articles

The Split-Half Reliability and Construct Validity of the Virtual Reality-Based Path Integration Task in the Healthy Population.

The virtual reality (VR)-based path integration task shows substantial promise in predicting dementia risk. However, the reliability and validity in healthy populations need further exploration. The present study investigates the relationship between task indicators and brain structures in a healthy population using a VR-based navigation task, particularly the entorhinal cortex (EC) and hippocampus. Sixty healthy adults were randomly recruited to perform a VR-based path integration task, the digit span task (DST), and an MRI scan. The indicators of the VR-based path integration task were calculated, including the absolute distance error (ADE), degree of angle deviation (DAD), degree of path deviation (DPD), and return time (Time). The reliability of the above indicators was then estimated using the split-half method and Cronbach's alpha. Correlation and regression analyses were then performed to examine the associations between these indicators and age, general cognitive ability (DST), and brain structural measures. ADE, DAD, and DPD showed reasonable split-half reliability estimates (0.84, 0.81, and 0.72) and nice Cronbach's alpha estimates (0.90, 0.86, and 0.96). All indicators correlated with age and DST. ADE and DAD were sensitive predictors of hippocampal volume, and return time was a predictor of EC thickness. Our findings demonstrate that the VR-based path integration task exhibits good reliability and validity in the healthy population. The task indicators are age-sensitive, can capture working memory capacity, and are closely related to the integrity of individual EC and hippocampal structures.

Mechanical Behavior of Soil Foundations Reinforced with Geosynthetics Exposed to Normal and Reverse Fault Ruptures

Surface fault ruptures damage structures which are located at the intersecting zones of active faults. It is essential to consider the undesirable effects of surface fault ruptures when designing structures. Geotechnical measures such as reinforced soil foundations effectively mitigate the hazards related to surface faults. The present work conducted a series of tests on foundations reinforced with geosynthetics, including geogrids, geocells and geogrid-geocell layers. These tests simulated the behavior of 1.5 m-wide strip footings located in 6-m thick alluvium that had been displaced 60 cm. A total of 12 disparate tests in terms of the number and type of reinforcement were conducted at a scale factor of 10. Image analysis of the results indicated desirable behavior for reinforced soil foundations in terms of reduced angular distortion, uniform settlement and deviation of the fault path. For normal fault rupture, the angular distortion of foundations reinforced by one geogrid layer, one geocell layer, one geogrid-geocell layer or two or three geo-grid layers decreased by 60%, 30%, 70%, 80% and 80%, respectively. These results also revealed that an increase in the number of geogrid layers to more than two layers caused an insignificant decrease in angular distortion. The decrease in angular distortions observed for soil foundations reinforced by one geogrid layer, one geocell layer and one geogrid-geocell layer were 7%, 16% and 40%, respectively, for reverse faulting. The performance of a reinforced soil foundation subjected to normal faulting was more acceptable than that for reverse faulting.

Time-optimal obstacle avoidance of autonomous ship based on nonlinear model predictive control

Autonomous shipping has been identified as the way forward in the maritime transport. However, the time-optimal path planning, anti-disturbance trajectory tracking and obstacle avoidance are still ongoing challenging problems, which have not been properly addressed for autonomous ship. To fill the knowledge gap, we propose a novel nonlinear model predictive control (MPC), which integrates the time-optimal path planning, anti-disturbance tracking and obstacle avoidance. The proposed controller is designed as a 2-level hierarchical controller. In the high level, a planned path considering time minimum and obstacle avoidance is generated by nonlinear MPC in spatial formulation. A spatial reformulation is adopted to express manoeuvring time as a function mathematically. In the spatial coordinate, the manoeuvring time is minimised by MPC to generate a reference path for tracking. In the low level, vessel tracks the time-optimal planning trajectory by nonlinear MPC with extended Kalman filter in temporal formulation. The deviation of the tracking path in longitudinal direction is 15% ship length and the deviation in width direction is 1% under disturbances. The obstacle avoidance is implemented by using the proposed control method, and the tolerance of obstacle avoidance is 2L (ship length) to meet the safety requirement.

Analysis of Path Distribution Characteristics and Safety Impact of Pedestrians Crossing in the Advance Right-Turn Lane

Pedestrian crossing chaos has become one of the significant problems of urban traffic nowadays, and the irregular behavior of pedestrians affects traffic operation and regional safety to a certain extent. Aiming at the safety of pedestrian crossing in the advance right-turn lane, the pedestrian path deviation (PPD) is proposed to quantitatively describe the pedestrian crossing path deviation and analyze the impact of PPD on pedestrian crossing safety. With four advance right-turn lanes at an intersection in Qingdao as the investigation site, the spatiotemporal information of pedestrians and motor vehicles is collected to analyze the characteristics of pedestrian path distribution and the influencing factors of pedestrian crossing under the factors of traffic flow and environment. The pedestrian-vehicle conflict characteristics are analyzed from the perspective of conflict rate and deceleration-to-safety. The severity level of human-vehicle conflict is classified according to the cumulative frequency of safety deceleration, and the severity model of human-vehicle conflict based on ordered probit is established. The results show that buildings such as large shopping malls and transportation hubs have an attractive effect on pedestrians. Under the influence of unilateral buildings, the proportion of irregular use of pedestrian crosswalks reaches 46.9%. There is a significant positive correlation between pedestrian flow and PPD index, and the increase of pedestrian flow causes the aggravation of pedestrian path deviation. The results of the analysis of PPD with pedestrian-vehicle conflict rate and conflict severity show that the increase of PPD significantly increases the risk of pedestrian-vehicle conflict occurrence; in addition, motor vehicle speed and PPD have significant effects on the severity of pedestrian-vehicle conflict, and the increase of both vehicle traffic speed and PPD causes the aggravation of the severity level of pedestrian-vehicle conflict. Combined with the above findings, the PPD index has some value in quantifying pedestrian spatial violations as well as regional safety evaluation, and the findings can provide theoretical guidance for the establishment of traffic facilities.

Design of a Flexure-Jointed Linkage in a Quadruped Walking Robot

Flexure-jointed linkages (FJLs) have distinct advantages, such as ease of manufacturing and assembly, no backlash, and light weight designs, over their rigid counterparts. However, the synthesis and analysis of FJLs are more complicated, especially when the compliant flexures have large deflections. This article addresses the optimal design of a large-deflection FJL, which is used as the leg of a compliant quadruped robot. The proposed FJL originates from a rigid six-bar linkage to mimic the foot path of walking mammals by replacing the passive revolute joints with two-segment combined flexible beams. Its kinematic and strain energy models are derived and verified by numerical simulations. Subjecting to material failures and geometrical constraints, the optimization of the FJL is carried out by minimizing the path deviation and strain energy to reduce needed input power, leading to the development of a quadruped walking robot. Experimental tests on the output path of the FJL and the motion capability of the robot are implemented. The results demonstrate the effectiveness of the derived models in predicting the deflections of the FJL and the feasibility of applying compliant linkages for legged robots.

Beliefs, Aggregate Risk, and the U.S. Housing Boom

Endogenously optimistic beliefs about future house prices can account for the path and standard deviation of house prices in the U.S. housing boom of the 2000s. In a general equilibrium model with incomplete markets and aggregate risk, agents form beliefs about future house prices in response to shocks to fundamentals. In an income expansion with looser credit conditions, agents are more likely to underpredict house prices and revise up their beliefs. Matching the standard deviation and steady rise in house prices results in homeownership becoming less affordable later in the boom as well as consumption dynamics that match the data.

A Game-Learning-Based Smooth Path Planning Strategy for Intelligent Air–Ground Vehicle Considering Mode Switching

Numerous missions in both civil and military fields involve the pursuit-evasion problem of vehicles. With vertical take-off and landing capability, the intelligent air–ground vehicle expands its feasible path to 3-D space, which has great advantages in the pursuit. This vehicle requires adequate path planning to obtain an optimal 3-D path and further improve the pursuit efficiency. The planning process of the air–ground vehicle currently faces the technical difficulties of acquiring the proper takeoff timing and position while optimizing the planning trajectory, especially in a complex environment with dense obstacles. To solve the above issues, a game-learning-based smooth path planning strategy for the intelligent air–ground vehicle considering mode switching is proposed in this article. First, a new reward function of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm, considering the influence of flight obstacle crossing parameters, is presented to explore the short forward track distance. Second, in the update rule, the pursuit-evasion game acts in the mode switching decisions. During interactive learning between the vehicle and environment, this game constantly updates the Nash equilibrium solutions for mode switching and gets a series of switching decisions of the pursuer vehicle (ego vehicle). Third, a double-yaw correction for path smoothing modification is proposed to reduce turning points and avoid local path deviations. This modification provides heuristic information for the exploration of the environment, which significantly speeds up the convergence speed of the algorithm. Finally, the proposed strategy is verified on a 1000 m*1000 m map with 0–200 m obstacle height. Results show that this strategy is effective to decrease the 253-m distance compared with the traditional reinforcement learning algorithm and has a faster convergence speed. The number of trajectory direction changes is 36% less than that of the game-learning algorithm only considering mode switching. The unreasonable large angle turns are eliminated.

Dynamic Lane Tracking Control of the Commercial Vehicle Based on RMPC Algorithm Considering the State of Preceding Vehicle

In order to improve the adaptability of the lane keeping control system to complex environments, a dynamic lane tracking control strategy of the commercial vehicle based on the robust model predictive control (RMPC) algorithm is proposed considering the state of the preceding vehicle. An RMPC controller is designed with path deviation and control increment as the objective function. The model predictive control problem is transformed into a min–max optimization problem. The linear matrix inequality (LMI) is used for the optimal solution to obtain the optimal control quantity. The strategy to improve the safety and comfort dynamically in the process of lane keeping is designed by adjusting the weight coefficient matrix of RMPC based on fuzzy theory. The results of the simulation and HiL test show that the RMPC controller can meet the requirement of adjusting the lane tracking process dynamically according to the state of the preceding vehicle, which keeps the balance between safety and comfort.

Reshaping Local Path Planner

This letter proposes a path planner that reshapes a global path locally in response to sensor-based observations of obstacles in the environment. Two fundamental concepts enable the resultant algorithm (a) a path-following <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">synthetic vehicle</i> whose steering actions are non-myopically optimized to result in a smooth traversible path that meets path curvature constraints, and (b) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path-aware</i> turning moment-field that enables obstacle avoidance while eluding the typical local-minimum-induced stagnation associated with potential field methods. The use of the combination of the two concepts results in a reduced action space over which optimization needs to be performed towards minimizing the path deviation subject to obstacle avoidance, and thus results in an efficient algorithm that can be implemented online. We demonstrate the algorithm in simulations as well as in field experiments, performing real time local path planning and obstacle avoidance on two different vehicle platforms (an ackerman steering vehicle two-axled vehicle, and a differential-steering 4-axled vehicle) in an unstructured off-road terrain.

A Study of AGV Collaboration with IoT Concept to Avoid a Collision at Warehouse Intersection

Technology nowadays is always changing very fast. Automated tools are used to face the advanced technology in the warehouse industry, such as automated guided vehicles (AGV). It becomes a common daily vehicle when working in the warehouse, especially on the shop floor. It means collaboration between AGVs is needed and has become important. This paper suggests how to avoid collisions between AGVs and how to avoid obstacles is a practical problem if two or more AGVs interact with each other. This is a common need that should be addressed. As a result, a more effective system design should be created and implemented. This paper used a three-tier internet of things (IoT) structure to avoid collisions and reduce path deviation to design an AGV collaboration system. The IoT was used to authorize AGVs to communicate with each other by transmitting position messages. In this situation, AGV would be aware of other AGV positions and would decide what to do next. According to the findings, two AGVs can avoid colliding by knowing each other's positions in any path scenario by recognizing obstacles through Light Detection and Ranging (LIDAR) detection. This paper designed an AGV collaboration system using the Robot Operating System (ROS) and conducted an experiment to test its practicality. The experiment simulated a warehouse intersection in the real world. This study used the statistical ANOVA method to identify motion planning parameters concerning final AGV position error values.

A novel path following approach for autonomous ships based on fast marching method and deep reinforcement learning

Path following is one of the indispensable tools for autonomous ships, which ensures that autonomous ships are sufficiently capable of navigating in specified collision-free waters. This study proposes a novel path following approach for autonomous ships based on the fast marching (FM) method and deep reinforcement learning (DRL). The proposed approach is capable of controlling a ship to follow different paths and ensuring that the path tracking errors are always within a set range. With the help of the FM method, a grid-based path deviation map is specially produced to indicate the minimum distance between grid points and the path. Besides, a path deviation perceptron is specifically designed to simulate a range sensor for sensing the set path deviation boundaries based on the path deviation map. Afterwards, an agent is trained to control a ship following a circular path based on the DRL. Particularly, the approach is validated and evaluated through simulations. The obtained results show that the proposed method is always capable of maintaining high overall efficiency with the same strategy to follow different paths. Moreover, the ability of this approach exhibits a significant contribution to the development of autonomous ships.

An Approach for Predicting Stiffness of a 5-DOF Hybrid Robot for Friction Stir Welding

This paper presents an approach for stiffness identification of a 5-DOF hybrid robot named TriMule for friction stir welding. The novelty of this approach is to visualize the realistic robotic system that considers the elastic deflections of all joints and links as a fictitious robotic system that only considers the elastic deflections of joints along their axes. Thus the number of parameters to be identified is reduced significantly. The stiffness identification is essentially implemented by the following four steps: (1) formulating the Cartesian stiffness model that maps the joint compliances of the fictitious system to the deflection twist of the end-effector, (2) parameterizing the joint compliances by a set of polynomial functions in terms of nominal actuated joint variables, (3) constructing the multiple linear regression equations by projecting the measured elastic deflections from the operational space onto the joint space, and (4) estimating the polynomial coefficients by robust biased estimation. The stiffness identification experiment and offline compensation experiment for the tool path deviations are carried out on a prototype machine to demonstrate the effectiveness of the proposed approach.

Improvement of thermo‐mechanical and fatigue crack growth resistance of tire sidewall compound by introducing syndiotactic polybutadiene

AbstractThermoplastic elastomer (TPE) has potential to meet stringent requirements of reduced rolling resistance coefficient (RRc) of tires by lowering the hysteresis of the compound. Syndiotactic polymers could impart their reinforcing nature to the matrix, enhancing compound modulus, hysteresis and fatigue cut growth (FCG) properties with an added advantage of compound weight reduction. In this study, two grades of VCR (Vinyl Cis Rubber) with 12% (VCR412) and 17% (VCR617) of syndiotacticity have been introduced in a 50/50 Natural rubber (NR)/cis‐Butadiene rubber (BR) based sidewall compound. 50% and 100% replacement of BR by VCR have been experimented in modified formulations from where 15 phr of carbon black is also withdrawn. This modification led to reduction of 24% and 18% in loss factor (tan δ) at 70°C in VCR412 and VCR617 respectively in comparison with reference compound. Improvement in 12% dynamic stiffness is also found by employing 50 phr of VCR617. Homogeneous dispersion of VCR in rubber matrix is confirmed by Atomic Force Microscopy (AFM) analysis. Fatigue Crack Growth (FCG) rate of sidewall compound with VCR617 reveals lower crack propagation realized by crack path deviation. Complete replacement of BR by VCR617 lead to optimized thermo‐mechanical properties related to hysteresis, dynamic stiffness and fatigue resistance.

Participation of Soccer Training Improves Lower Limb Coordination and Decreases Motor Lateralization.

Athletes, who display less lateralization, are considered to be more successful in their sports. Therefore, it is important to test the lateralization profiles of the athletes to determine future prospects. Soccer is one of the sports where lateralization plays an important role because performing the passes and kicks with either foot may increase the success rate. Improved lower limb coordination is also very essential to perform the soccer skills more efficiently. Thus, the purpose of this study was to investigate the motor lateralization profiles of youth soccer players and to compare the same lateralization to non-athletes. A total of 28 healthy youth (14 soccer players) aged between 14 and 16 years voluntarily participated in this study. All participants were right-footed and were asked to hit the targets with their either foot maintaining accuracy in a custom-made virtual reality interface. Final position error (FPE) and foot path deviation from linearity (FPDL) were calculated to test motor lateralization for each foot and group. Two-way Mixed Model ANOVA was conducted for each dependent variable. Results indicated significant differences for FPDL between groups, while there were no significant differences between groups and within feet for FPE. Nonathletes had significantly higher FPDL with their nondominant foot compared to their dominant foot, which was not observed among soccer players. Overall, nonathletes' movements were more curvature path compared to soccer players, thus, can be considered as less coordinated. As soccer players did not show a difference between their feet on FPDL and performed better than nonathletes, soccer participation can improve lower limb coordination as well as alter motor performance and lateralization.

A Study of AGV Collaboration with Internet of Things Concept for Collision Avoidance at Warehouse Intersection

With advance of technology, automated guided vehicle (AGV) will become common daily vehicles, which means collaboration between AGVs are more and more important. This paper suggests that how to make AGV avoid collisions with other AGVs is a practical problem, and it is a common need and should be solved if there are two or more AGVs interacting with others. Therefore, a better system design should be developed and implemented.&#x0D; This paper used a three-layer structure of internet of things (IoT) to design an AGV collaboration system, to avoid collisions with other AGVs in this system, and to decrease path deviation. The concept of IoT was applied for AGV communicating with other AGVs through sending AGV position messages. In such a case, AGV would know other AGV positions and decide which actions to take. This paper used Robot Operating System (ROS) to design an AGV collaboration system, and conducted an experiment to verify the feasibility of this system. The experiment simulated the intersection of warehouse in the real world. According to the results, two AGVs can avoid collisions by knowing positions of each other in every path scenario. When two AGV both followed straight paths, the deviation was the smallest. When one AGV turned left and another turned right, it might have largest deviation.

Effect of geometric size deviation induced by machining on the vibration fatigue behavior of Ti‐6Al‐4V

AbstractTi‐6Al‐4V plates were machined simultaneously to achieve the reduced section of specimens with a wire‐cut electrical discharge machine (EDM). Geometric shape errors due to the cutting path deviation were discovered on many specimens after the final manufacture. High‐cycle fatigue tests were carried out on these symmetrical and asymmetrical shaped specimens. Probabilistic treatment was used to examine the occurrence probability of one or two cracks, and the fatigue failure mechanisms were investigated using scanning electron microscope (SEM). The objective of this work was to highlight the impact of the geometric size deviation on fatigue life and crack growth.

USING EYE TRACKING SYSTEM FOR AIRCRAFT DESIGN – A FLIGHT SIMULATOR STUDY

The authors of this paper investigated applications of eye tracking in transport aircraft design evaluations. Piloted simulations were conducted for a complete flight profile including take-off, cruise and landing flight scenario using the transport aircraft flight simulator at CSIR-National Aerospace Laboratories. Thirty-one simulation experiments were carried out with three pilots/engineers while recording the ocular parameters and the flight data. Simulations were repeated for high workload conditions like flying with degraded visibility and during stall. Pilot’s visual scan behaviour and workload levels were analysed using ocular parameters; while comparing with the statistical deviations from the desired flight path. Conditions for fatigue were also recreated through long duration simulations and signatures for the same from the ocular parameters were assessed. Results from the study found correlation between the statistical inferences obtained from the ocular parameters with those obtained from the flight path deviations. The authors of this paper investigated an evaluator’s console that assists the designers or evaluators for better understanding of pilot’s attentional resource allocation.

Starting CT-guided robotic interventional oncology at a UK centre.

A commercially available CT-guided robot offers enhanced abilities in planning, targeting, and confirming accurate needle placement. In this short communication, we describe our first UK experience of robotic interventional oncology procedures. We describe the device, discuss installation, operation, and report upon needle insertion success, accuracy (path deviation; PD and tip deviation; TD), number of adjustments, complications, and procedural success. Nine patients (seven males), median age 66 years (range 43-79) were consented for biopsy or ablation between March and April 2021. Needle placement in biopsy was more accurate than ablation (median 1 vs 11 mm PD and 1 vs 20 mm TD) and required fewer adjustments (median 0 vs 5). No complications arose, and all procedures were successful (diagnostic material obtained or complete ablation at follow-up). Short procedure times and very high levels of accuracy were readily achieved with biopsy procedures, although tumour ablation was less accurate which likely reflects higher procedural complexity. Achieving highly accurate robotic biopsy with is feasible within a very short time span. Further work is required to maximise the potential of robotic guidance in tumour ablation procedures, which is likely due to higher complexity giving a longer learning curve.

A Remote Patient-Monitoring System for Intensive Care Medicine: Mixed Methods Human-Centered Design and Usability Evaluation.

BackgroundContinuous monitoring of vital signs is critical for ensuring patient safety in intensive care units (ICUs) and is becoming increasingly relevant in general wards. The effectiveness of health information technologies such as patient-monitoring systems is highly determined by usability, the lack of which can ultimately compromise patient safety. Usability problems can be identified and prevented by involving users (ie, clinicians).ObjectiveIn this study, we aim to apply a human-centered design approach to evaluate the usability of a remote patient-monitoring system user interface (UI) in the ICU context and conceptualize and evaluate design changes.MethodsFollowing institutional review board approval (EA1/031/18), a formative evaluation of the monitoring UI was performed. Simulated use tests with think-aloud protocols were conducted with ICU staff (n=5), and the resulting qualitative data were analyzed using a deductive analytic approach. On the basis of the identified usability problems, we conceptualized informed design changes and applied them to develop an improved prototype of the monitoring UI. Comparing the UIs, we evaluated perceived usability using the System Usability Scale, performance efficiency with the normative path deviation, and effectiveness by measuring the task completion rate (n=5). Measures were tested for statistical significance using a 2-sample t test, Poisson regression with a generalized linear mixed-effects model, and the N-1 chi-square test. P<.05 were considered significant.ResultsWe found 37 individual usability problems specific to monitoring UI, which could be assigned to six subcodes: usefulness of the system, response time, responsiveness, meaning of labels, function of UI elements, and navigation. Among user ideas and requirements for the UI were high usability, customizability, and the provision of audible alarm notifications. Changes in graphics and design were proposed to allow for better navigation, information retrieval, and spatial orientation. The UI was revised by creating a prototype with a more responsive design and changes regarding labeling and UI elements. Statistical analysis showed that perceived usability improved significantly (System Usability Scale design A: mean 68.5, SD 11.26, n=5; design B: mean 89, SD 4.87, n=5; P=.003), as did performance efficiency (normative path deviation design A: mean 8.8, SD 5.26, n=5; design B: mean 3.2, SD 3.03, n=5; P=.001), and effectiveness (design A: 18 trials, failed 7, 39% times, passed 11, 61% times; design B: 20 trials, failed 0 times, passed 20 times; P=.002).ConclusionsUsability testing with think-aloud protocols led to a patient-monitoring UI with significantly improved usability, performance, and effectiveness. In the ICU work environment, difficult-to-use technology may result in detrimental outcomes for staff and patients. Technical devices should be designed to support efficient and effective work processes. Our results suggest that this can be achieved by applying basic human-centered design methods and principles.Trial RegistrationClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

Multi-robot cooperation and path planning for stick transporting using improved Q-learning and democratic robotics PSO

The highlight of this paper is to propose an innovative method for solving the problem of path planning and synchronization of robot pairs in a known static and complex environment. The problem addresses the stick transportation by robot pairs from the predefined starting position to the pre-assumed goal position. This paper describes the method of computing collision-free paths for multiple numbers of paired robots called twins using a hybrid algorithm with balanced local exploitation and global exploration. The hybrid algorithm is designed by combining the individual benefits of the improved Q-Learning (IQ) and democratic robotics particle swarm optimization (DRPSO). The main intention of IQ is to produce the best position with collision avoidance by executing the best state-action pair. DRPSO algorithm computes the optimal trajectory for the twin robot using the distributed democratic approach and enhances the intensification capability. The twin robots are synchronized to the number of steps, turns, the direction of movement, and distance between both of them in the proposed approach. Computer simulation results reveal the efficacy of the proposed algorithm in both paths travelled and runtime. The experimental and VREP simulation results illustrate that the proposed algorithm offers better synchronization, cooperation, and performance in terms of several parameters such as path length, path deviation, run time, and path smoothness as a replica of the number of turns made. The comparative study with artificial bee colony optimization (ABCO) and imperialist competitive firefly algorithm (ICFA) witnesses the superiority of the hybrid IQ-DRPSO algorithm. Experiments that have been conducted through Arduino bots in a real environment prove the efficiency of the proposed approach. Results obtained from simulation and experiment reveal the supremacy of the proposed algorithm for synchronization, cooperation, and path traveled.