Destination Point Research Articles

The boundary node method for multi‐robot multi‐goal path planning problems

AbstractIn this paper, we investigate the multi‐goal path planning problem to find the shortest collision‐free path connecting a given set of goal points located in the robot working environment. This problem combines two sub‐problems: first, optimize the sequence of the goal points located in the free workspace; second, compute the shortest collision‐free path between goal points. In this study, a genetic algorithm is used to optimize the sequence of the goal points that the robot should visit. Once the sequence of the goal points is available, our developed method called Boundary Node Method (BNM) is applied to generate an initial collision‐free path between every pair of the sequenced goal points. Subsequently, an additional developed method called Path Enhancement Method (PEM) is used to find an optimal or near‐optimal path by reducing the overall initial path length. In the BNM, the robot and its immediate surroundings are simulated by a nine‐node quadrilateral element, where the centroid node represents the robot's position. The robot moves between goals with boundary nodes in the working environment depending on the boundary node's potential value. The potential value of each point in the working environment is calculated based on the proposed potential function. Additionally, this article investigates the multi‐goal path planning problem for multi‐robot systems, when each goal reached by several robots. Finally, simulations and experiments are performed on a real mobile robot to demonstrate the effectiveness of the developed methods.

Remote Control Rover with Camera and Ultrasonic Sensor

Disasters and danger come without giving us warning so we must always be prepared for every situation. So for handling such situations robots can play an important role as in these situations there can be a huge risk of life too. It is wirelessly and remotely controlled through devices in order to make robots move from source to destination point. In many cases, the ground becomes uneven due to disaster effects, so robots can move through such places easily. With a rover, it becomes convenient to search the area without making any trouble. As in many rescuing operations, humans are widely used to overcome such situations and so for assisting them, robots can be used for gathering data from accidental places.

Design and architecture solutions for service robot waiter with a specialized system of payload stabilization

Application of robotic devices in subject domains, where monotonous routines have to be performed promptly and accurately, is a relevant problem, particularly in complicated epidemiological situations. In this paper the design of robot waiters is analyzed and a design is proposed for stabilization of payload during delivering. Common applied problems in the robotic service domain are associated with the need to use such robots on mostly even, flat surfaces or with the arrangement of special structures that simplify the movement of a robotic device along a given route. The proposed solution potentially provides for cheaper, simpler and more optimized application of the robotic device indoors, inside the restraunt due to the developed buffer mechanism and the system of gyroscopic stabilization of the trays, as well as the implemented control system based on the PID controller and the PWM generator, which ensures the smooth movement of the robot (from the starting point to the destination point). Based on the proposed solution, we get a fully functional robotic device that does not require additional investments in the reconstruction of the restaurant premises, completely replaces the waiter when delivering food and drinks to the client's table, and also attracts new customers due to its novelty and practicality.

Multi-obstacle path planning and optimization for mobile robot

In the past few decades, many results have been achieved in the research of mobile robot path planning, and they have been applied in simple scenarios, such as factory AGV, bank guide robot. However, path planning in highly dense and complex scenarios has become an important challenge for applications. Robots face dense map and complex obstacles and hardly find out an optimal path within a reasonable period, such as unmanned vehicles in freight ports and rescue robots in earthquake environment. Therefore, a multi-obstacle path planning and optimization method is proposed. In order to simplify complex environmental obstacles, the obstacles will be divided into basis obstacles and extension obstacles. Firstly, the basis obstacles and their contour point sets are determined according to the starting point and goal point. Furthermore, the basis obstacles are optimized by convex hulls, and then the corresponding basis point set is obtained. Secondly, the extension obstacles are determined by the basis point set, starting point and goal point, and then the corresponding extension point set is generated. After that, a path planner is designed by the multi-objective D* Lite algorithm for distance and smoothness in order to get reasonable and optimized path in a complex environment. Moreover, the path is smoothed by cubic bezier curves to fit the kinematic model of the robot. Finally, The proposed method conduct comparative experiments with other algorithms to verify its accuracy and computational efficiency of planning in complex environments.

A computational framework for studying energetics and resource management in sea turtle migration and autonomous systems

Sea turtles complete migrations across vast distances, covering entire ocean basins. To track these migrations, satellite tracking tags are attached to their shells. The impact of these tags must be considered to ensure that turtles’ natural behavior is not artificially and adversely impacted through tag-related drag, and that the data collected by a small sample of sea turtles accurately represents the larger population. Additionally, it can be difficult to study animal energetics in the field over large migration distances. In this work, we modify a computational behavior model to study how satellite tracking tags affect turtle migration behavior. Our agent based model contains synthetic magnetic field environments that are used for navigation cues, an ocean current, resource distributions that represent locations of food, and an agent that attempts to migrate to several different goals. The agent loses energy as it progresses, and searches for the resource distributions to replenish itself. Our novel simulation framework demonstrates the relationship between an agent’s available energy capacity, its energy consumption based on mechanical power expended, and its ability to navigate to all migratory goal points. This study can be utilized to (1) probe the impacts of an animal’s energy capacity and foraging behavior on its resulting navigation and ecology, (2) guide future satellite tag designs, and (3) develop usage recommendations for a suitable tracking tag based on the type of experiment being conducted. Our model can be expanded beyond sea turtles to study other marine species (e.g., sharks, whales). Additionally, this model could be expanded to other domains within the marine environment. For example, it could be modified to examine design trade-offs in remotely operated vehicles (ROVs), which share many of the same operational constraints as sea turtles and other migratory species.

Reactive navigation under a fuzzy rules-based scheme and reinforcement learning for mobile robots.

Robot navigation allows mobile robots to navigate among obstacles without hitting them and reaching the specified goal point. In addition to preventing collisions, it is also essential for mobile robots to sense and maintain an appropriate battery power level at all times to avoid failures and non-fulfillment with their scheduled tasks. Therefore, selecting the proper time to recharge the batteries is crucial to address the navigation algorithm design for the robot’s prolonged autonomous operation. In this paper, a machine learning algorithm is used to ensure the extended robot autonomy based on a reinforcement learning method combined with a fuzzy inference system. The proposal enables a mobile robot to learn whether to continue through its path toward the destination or modify its course on the fly, if necessary, to proceed toward the battery charging station, based on its current state. The proposal performs a flexible behavior to choose an action that allows a robot to move from a starting to a destination point, guaranteeing battery charge availability. This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.

Methodology for Determining the Nearest Destinations for the Evacuation of People and Equipment from a Disaster Area to a Safe Area

Floods are the most frequent natural disasters in the world. In the system of warning and flood protection of areas at risk of flooding in the event of its occurrence, it seems advisable to initially work out the possibility of evacuating the population, animals, equipment, material values, etc. In this article, a methodology for determining destinations (points of destination) for the evacuation of people and equipment from a predicted flood zone (of a natural disaster) to a safe area is proposed based upon the criterion of the shortest possible distance. In the paper, a scenario is considered that involves the contours of the flood zone boundaries for several variants of the intensity of the probable development of future events (with the aid of geoinformation technologies), and the coordinates of the objects to evacuate are permanent and known in advance. With the known coordinates of the objects and the closest points of the boundary of the predicted flood zone, the shortest distances can be calculated. Based on these calculations, the appropriate destinations for evacuation are determined. The proposed methodology can be used for flood forecasting and flood zone modeling to assess the economic and social risks of their aftereffects and to allow the public, local governments, and other organizations to better understand the potential risks of floods and to identify the measures needed to save lives and avoid damage to and loss of property and equipment. This methodology, in contrast to known approaches, allows the determination of the nearest locations for the evacuation of people and equipment from a flood zone (of a natural disaster) to safe areas, to be determined for several variants, depending on the possible development of future events. The methodology is algorithm-driven and presented in the form of a flowchart and is suitable for use in the appropriate software. The proposed methodology is an introduction to the next stages of research related to the determination of safe places for evacuation of people and their property (equipment) to safe places. This is especially important in case of sudden weather events (flash floods).

The dynamics of doubt: Short-term fluctuations and predictors of doubts in personal goal pursuit.

An action crisis denotes an intrapsychic conflict in which a person feels torn between holding on to and letting go of a personal goal. It is characterized by the experience of goal-related doubts. To extend the existing understanding of action crises, we investigated the dynamics of doubts in daily life and addressed two questions: (1) To what extent do doubts with respect to a personal goal fluctuate in daily life? (2) In which situations do individuals primarily have doubts? In an experience sampling study (N = 254), we assessed doubts about a personal goal five times a day over 10 days. Our findings indicate that the level of doubt varies between individuals but also fluctuates within persons. Doubts increase in response to negative goal-related events and decrease in response to positive goal-related events. Individuals with higher (vs. lower) levels of action crisis have particularly high levels of doubt during goal-related (vs. -unrelated) activity. We discuss the implications of these findings for research on goal disengagement and point to the relevance of studying motivational parameters from a dynamic perspective.

The implementation of digital differential analyzer algorithm for route determination’s topographic maps based on android

The route is direction that must be taken from the starting point to the destination point. Determination of the route is usually done for a reference trip that will be taken include the distance, time or slope of the terrain to be passed. In the implementation, the determination of the route is often used by agencies and organizations operating in the open nature where the activities require special expertise that can process geographic information systems. Although the tools and technology for route determination are sufficient, they are still less effective and efficient because to know the distance, time and slope of a terrain on topographic maps are still using conventional calculations, therefore the application of route determination’s topographic maps based android and applying Digital Differential Analyzer algorithm. The algorithm can be used as a line maker to display a slope picture of the field being traversed. This application was developed using the Rational Unified Process (RUP) method which has 4 stages: Inception, Elaboration, Construction, and Transition. This application can be run at least on Android platform version 4.1.

Environmental Performance of Chocolate Produced in Ghana Using Life Cycle Assessment

Ghana is an important cocoa producer and exporter and this production is of high economic importance. Increasing interest in the sustainable productions of cocoa/chocolate necessitated the need to assess the environmental impacts associated with the production of different chocolate variants (extra dark (EDC), dark (DC), milk (MC) and flavoured milk (FMC) in Ghana, including the identification of environmental hotspots for improvement. The life cycle assessment tool was used following the CML_IA and CED impact assessment methods. EDC had the lowest scores for most of the impact categories while FMC was most impactful. For Global Warming Potential (GWP), EDC and FMC were estimated to be 1.61 kg CO2 eq. and 4.21 kg CO2 eq., respectively. CED ranged from 1.44 × 102 to 1.50 × 102 MJ-eq. Chocolate manufacturing phase was generally more impactful than cocoa cultivation due to high emissions from milk and sugar production. The impact scores for 100 g packaged chocolate bar were the lowest in comparison to 300 g chocolate pouches and 12.5 g packaged chocolate strips. GWP for 100 g and 12.5 g were 0.20 kg CO2 eq. and 0.39 kg CO2 eq., respectively. Comparing different destination points for the manufactured chocolate, impact scores for the international destination were similar to those recorded for local destinations. Improvement options are suggested for all phases to ensure more sustainable chocolate production and distribution.

A bioinspired navigation strategy that uses magnetic signatures to navigate without GPS in a linearized northern Atlantic ocean: a simulation study

Certain animal species use the Earth’s magnetic field (i.e. magnetoreception) in conjunction with other sensory modalities to navigate long distances. It is hypothesized that several animals use combinations of magnetic inclination and intensity as unique signatures for localization, enabling migration without a pre-surveyed map. However, it is unknown how animals use magnetic signatures to generate guidance commands, and the extent to which species-specific capabilities and environmental factors affect a given strategy’s efficacy or deterioration. Understanding animal magnetoreception can aid in developing better engineered navigation systems that are less reliant on satellites, which are expensive and can become unreliable or unavailable under a variety of circumstances. Building on previous studies, we implement an agent-based computer simulation that uses two variants of a magnetic signature-based navigation strategy. The strategy can successfully migrate to eight specified goal points in an environment that resembles the northern Atlantic ocean. In particular, one variant reaches all goal points with faster ocean current velocities, while the other variant reaches all goal points with slower ocean current velocities. We also employ dynamic systems tools to examine the stability of the strategy as a proxy for whether it is guaranteed to succeed. The findings demonstrate the efficacy of the strategy and can help in the development of new navigation technologies that are less reliant on satellites and pre-surveyed maps.

Path Planning and Obstacles Avoidance in Dynamic Workspace Using Polygon Shape Tangents Algorithm

This paper presents the design of a path planning system in an environment that contains a set of static and dynamic polygon obstacles localized randomly. In this paper, an algorithm so-called (Polygon shape tangents algorithm) is proposed to move a mobile robot from a source point to a destination point with no collision with surrounding obstacles using the visibility binary tree algorithm. The methodology of this algorithm is based on predicting the steps of a robot trajectory from the source to the destination point. The polygon shapes tangent algorithm is compared with the virtual circles' tangents algorithm for different numbers of static and dynamic polygon obstacles for the time of arrival and the length of the path to the target. The obtained result shows that the used algorithm has better performance than the other algorithms and gets less time of arrival and shortest path with free collision.

Energy optimal motion planning of a 14-DOF biped robot on 3D terrain using a new speed function incorporating biped dynamics and terrain geometry

AbstractIn this paper, a new method is proposed to find a feasible energy-efficient path between an initial point and goal point on uneven terrain and then to optimally traverse the path. The path is planned by integrating the geometric features of the uneven terrain and the biped robot dynamics. This integrated information of biped dynamics and associated cost (energy) for moving toward the goal point is used to define the value of a new speed function at each point on the discretized surface of the terrain. The value is stored as a matrix called the dynamic transport cost (DTC). The path is obtained by solving the Eikonal equation numerically by fast marching method (FMM) on an orthogonal grid, by using the information stored in the DTC matrix. One step of walk on uneven terrain is characterized by 10 footstep parameters (FSPs); these FSPs represent the position of swinging foot at the starting and ending time of the walk, orientation, and state (left or right) of support foot. A walking dataset was created for different walking conditions (FSPs), which the biped robot is likely to encounter when it has to walk on the uneven terrain. The corresponding energy optimal hip and foot trajectory parameters (HFTPs) are obtained by optimization using a genetic algorithm (GA). The created walk dataset is generalized by training a feedforward neural network (NN) using the scaled conjugate gradient (SCG) algorithm. The Foot placement planner gives a sequence of foot positions and orientations along the obtained path, which is followed by the biped robot by generating real-time optimal foot and hip trajectories using the learned NN. Simulation results on different types of uneven terrains validate the proposed method.

Hybrid RRT-A*: An Improved Path Planning Method for an Autonomous Mobile Robots

Although the Basic RRT algorithm is considered a traditional search method, it has been widely used in the field of robot path planning (manipulator and mobile robot), especially in the past decade. This algorithm has many features that give it superiority over other methods. On the other hand, the Basic RRT suffers from a bad convergence rate (it takes a long time until finding the goal point), especially in environments with cluttered obstacles, or whose targets are located in narrow passages. Many studies have discussed this problem in recent years. This paper introduces an improved method called (Hybrid RRT-A*) to overcome the shortcomings of the original RRT, specifically slow convergence and cost rate. The heuristic function of A-star algorithm is combined with RRT to decrease tree expansion and guide it towards the goal with less nodes and time. Various experiments have been conducted with different environment scenarios to compare the proposed method with the Basic RRT and A-star under the same conditions, which have shown remarkable performance. The time consumed to find the path of the worst one of these scenarios is about 4.9 seconds, whereas it is 18.3 and 34 for A-star and RRT, respectively.

Route Optimization of Unmanned Aerial Vehicle by using Reinforcement Learning

The study proposes the machine learning based algorithm for autonomous vehicle. The dynamic characteristic of unmanned aerial vehicle and real time disturbances such as wind current, obstacles are considered. The novelty of the work lies in the introduction of reinforcement learning for achieving optimized path which can be followed by the unmanned aerial vehicles to complete the tour from initial to destination point. The feasible optimal route may be found by incorporating the L algorithm with a reasonable optimality and computational cost when map and current field data are given.

DAMPAK KENAIKAN TARIF GO-RIDE GOJEK TERHADAP PENDAPATAN DRIVER GO-RIDE DI KECAMATAN PADANG SELATAN

he objectives of this study were (1) to analyze the distribution pattern of the go-ride driver base in South Padang District (2) to analyze the reach of go-ride drivers in Padang Selatan District, and (3) to analyze the income of go-ride go-ride drivers before the increase, after the increase, and when the covid-19 epidemic. This type of research is quantitative. The population of this study were motorbike taxi drivers scattered in pdang Selatan sub-district who are members of an active community. With the sampling technique using total sampling technique, with a total of 80 respondents. In this study, researchers found: (1) The distribution pattern of the Go-Ride Driver Base at 07.00-08.00 WIB, 12.00-13.00 WIB, and at 16.00-17.00 WIB is clustered. (2) The reach of go-ride drivers at 07.00-08.00 WIB, 12.00-13.00 WIB, and 16.00-17.00 WIB in the period before the increase, after the increase, and when the Covid-19 pandemic has different periods, the farthest distance is 20.5 km and the closest distance 0.1 km based on point pick up to destination point. (3) from before the tariff increase, after the tariff increase, and when the Covid-19 pandemic the driver's income decreased, this can be seen from the very high income of 42 people with a percentage of 52.50% experiencing a decrease when after the tariff increase it became 11 people with a percentage 13.75%, and when the Covid-19 pandemic the go-ride drivers of South Padang District did not generate very high income.

Comparison and Validation of Optimal Path Lengths in the Presence of Elliptical Forbidden Regions between CGA and V-Rep

This paper presents Comparison and Validation of Optimal path lengths in the presence of Elliptical Forbidden Regions between CGA and V-Rep simulation software. A systematic approach was made for developing results by applying a suitable CGA methodology that meets the solution requirements of routing problems that involve path planning procedures. CGA technique helps in reducing the evaluation time and also directly generates the shortest path for navigation to reach its destination point. The solutions thus obtained by CGA Technique absolutely agreed with the solutions by V-Rep and even produced optimal solutions when compared with V-Rep.

A novel MP-LSTM method for ship trajectory prediction based on AIS data

The accurate prediction of ship trajectory has great significance in maritime transportation. Among all the prediction methods, multi-step prediction has received increasing attention because it can predict both time and position information in the future period. However, the existing methods are either complex or have low prediction accuracy. In order to overcome the limitations, a physical hypothesis is introduced to balance the complexity and the accuracy. The cubic spline interpolation and historical trajectories are used to realize it. The advantages of TPNet and LSTM are combined in the proposed method and four parts are involved: the AIS data preprocessing method, the solutions of destination point and support point, and the uncertainty analysis. The proposed method is not only easy to implement and suitable for real-time analysis, but also has a high prediction accuracy. The case study on a ferry ship in the Jiangsu section of the Yangtze River indicates the validity of the method.

Bone Conduction Auditory Navigation Device for Blind People

A navigation system using a binaural bone-conducted sound is proposed. This system has three features to accurately navigate the user to the destination point. First, the selection of the bone-conduction device and the optimal contact conditions between the device and the human head are discussed. Second, the basic performance of sound localization reproduced by the selected bone-conduction device with binaural sounds is confirmed considering the head-related transfer functions (HRTFs) obtained in the air-borne sound field. Here, a panned sound technique that may emphasize the localization of the sound is also validated. Third, to ensure the safety of the navigating person, which is the most important factor in the navigation of a visually impaired person by voice guidance, an appropriate warning sound reproduced by the bone-conduction device is investigated. Finally, based on the abovementioned conditions, we conduct an auditory navigation experiment using bone-conducted guide announcement. The time required to reach the destination of the navigation route is shorter in the case with voice information including the binaural sound reproduction, as compared to the case with only voice information. Therefore, a navigation system using binaural bone-conducted sound is confirmed to be effective.

A cost-effective GPS-aided autonomous guided vehicle for global path planning

This paper presents a robotic platform of a cost-effective GPS-aided autonomous guided vehicle (AGV) for global path planning. The platform is made of a mechanical radio controlled (RC) rover and an Arduino Uno microcontroller. An installed magnetic digital compass helps determine the right direction of the RC rover by continuously synchronising the heading and bearing of the vehicle. To ensure effective monitoring of the vehicle’s position as well as track the corresponding path, an LCD keypad shield was, further, used. The contribution of the work is that the designed GPS-aided AGV can successfully navigate its way towards a destination point in an obstacle-free outdoor environment by solely relying on its calculation of the shortest path and utilising the corresponding GPS data. This result is achieved with a minimum error possible that lies within a circle of one meter radius around the given destination, allowing the devised GPS-aided AGV to be used in a variety of applications such as landmine detection and removal.