Route Planning Technique Research Articles

I-Randomly Exploring Algorithm for Wheeled Mobile Robot Path Planning

An Improved-RRT-based route planning technique with navigable trajectories was proposed. In this research work, the classical RRT technique is enhanced by optimizing the Non-linear equations and extending the mechanism of the technique. At first, the proposed I-RRT Expansion procedure is done and a task called N-action is performed that could identify the navigable trajectory in the configuration space. Then, an efficient extend-based-obstacles avoidance action has been carried out in the chosen navigable trajectory that was traced by the proposed I-RRT Expansion procedure. Finally, a subsequent-operation process was performed to carry out the node trimming process and smoothening of the final optimal path traced by the I-RRT Expansion method. Trajectories that have been generated using the proposed Improved Rapidly-Exploring Random Tree (I- RRT) method not only satisfied direction constraints approach on both start and goal points, but also simultaneously guaranteed the effectiveness and continuity of the trajectory. Consequently, the trajectories produced by the non-linear vehicle systems like Wheeled Mobile Robots (WMRs) are found to be geometrically viable and dynamically feasible. The optimal trajectories for five identical scenarios each of grid size of 100 × 100 and each scenario consisting of different number of nodes (500, 750, 1000, 1250 and 1500) were generated and the proposed method Improved Rapidly-Exploring Random Tree (I-RRT) was applied to all the scenarios and the performances were evaluated. The simulation results illustrate that the time traversed by the wheeled mobile robot and the path lengths traced by the WMR to reach the goal using the proposed I-RRT algorithm in each scenario are reduced considerably when compared to the corresponding time traversed by the wheeled mobile robot and the path lengths traced by the WMR to reach the goal using the existing RRT algorithm.

Adaptive Parallel Seeker Optimization-based Route Planning for clustered WSN in smart cities

Smart city is a phenonmenon that integrates physical and social infrastructures with Information Technology to keep a city's cooperative intelligence under control. Smart cities primarily rely on Wireless Sensor Networks (WSN) to manage and maintain its service offerings. In literature, clustering and multihop routing techniques have been proposed, validated and implemented to reduce the consumption of energy in the network. With this motivation, the current study develops Adaptive Parallel Seeker Optimization-based Energy Aware Route Planning Technique (APSO-EARPT) for clustered WSN in smart cities. The presented APSO-EARPT technique concentrates on appropriate selection of Cluster Heads (CHs) and optimal routes in WSN. To accomplish this, APSO-EARPT model encompasses Weight-Based Clustering Scheme (WBCS) for effective selection of CHs. Then, routing process is performed with the help of APSO algorithm. The proposed APSO-EARPT technique computes a Fitness Function (FF) that comprises of three variables such as Residual Energy (RE), distance to Base Station (BS), and node degree. This fitness function helps in optimal selection of routes in WSN. In order to validate the supremacy of the proposed APSO-EARPT model in terms of network lifespan and energy efficiency, simulations were conducted and the results confirmed the excellent performance of the proposed model.

A new federated genetic algorithm-based optimization technique for multi-criteria vehicle route planning using ArcGIS network analyst

PurposeUsing a real-time road network combined with historical traffic data for Al-Salt city, the paper aims to propose a new federated genetic algorithm (GA)-based optimization technique to solve the dynamic vehicle routing problem. Using a GA solver, the estimated routing time for 300 chromosomes (routes) was the shortest and most efficient over 30 generations.Design/methodology/approachIn transportation systems, the objective of route planning techniques has been revised from focusing on road directors to road users. As a result, the new transportation systems use advanced technologies to support drivers and provide them with the road information they need and the services they require to reduce traffic congestion and improve routing problems. In recent decades, numerous studies have been conducted on how to find an efficient and suitable route for vehicles, known as the vehicle routing problem (VRP). To identify the best route, VRP uses real-time information-acquired geographical information systems (GIS) tools.FindingsThis study aims to develop a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences.Originality/valueFurthermore, developing a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. An adaptive genetic algorithm (GA) is used to determine the optimal time route, taking into account factors that affect vehicle arrival times and cause delays. In addition, ArcGIS' Network Analyst tool is used to determine the best route based on the user's preferences using a real-time map.

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.

Application-Based COVID-19 Micro-Mobility Solution for Safe and Smart Navigation in Pandemics

Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity.

IMPROVED PARTICLE SWAM OPTIMIZATION FOR CROWD SIMULATION USING HYBRID AGENT REINFORCEMENT LEARNING ALGORITHM

In an emergency route planning technique, simulating the dynamic crowd has route capacity constraints and global target of evacuating all crowd evacuees. To stimulate the crowd, the new arena is developed to know the real-time situation to face the crowd evacuation on exit point. The crowd evacuation is done with the process of Hybrid Agent Reinforcement Learning (HARL) algorithm consisting of Improved Multi-Agent Reinforcement Learning (IMARL) and State-Action-Reward-State-Action (SARSA). In the proposed work, the appropriate route selection mechanism focused on finding optimum evacuation route(s) is done in the first phase. Dynamic crowd can also be evacuated to find its way with the support of the HARL process in the second phase. The proposed HARL method can also be implemented with multi-objective improved particle swarm optimization (IPSO) technique for crowd simulation. The experimented results demonstrate the effectiveness of stability in the HARL process, which provides an improved performance for crowd simulation

Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks

Many modern navigation systems and map-based services do not only provide the fastest route from a source location s to a target location t but also provide a few alternative routes to the users as more options to choose from. Consequently, computing alternative paths has received significant research attention. However, it is unclear which of the existing approaches generates alternative routes of better quality because the quality of these alternatives is mostly subjective. Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps. We also present a web-based demo system that can be accessed using any internet-enabled device and allows users to see the alternative routes generated by the four approaches for any pair of selected source and target. We report the average ratings received by the four approaches and our statistical analysis shows that there is no credible evidence that the four approaches receive different ratings on average. We also discuss the limitations of this user study and recommend the readers to interpret these results with caution because certain factors may have affected the participants' ratings.

High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data

Dockless bike-sharing systems (DLBS) have gained much popularity due to their environmentally friendly features. This study puts forward a distinctive framework for assessing the environmental influences of DLBS in high resolution based on DLBS transaction data. The proposed framework firstly estimates the transport mode substituted by DLBS for each recorded bike-sharing trip by utilizing the route planning techniques of online maps and a well-calibrated discrete choice model. Afterward, greenhouse gases (GHG) emission reductions in every recorded DLBS trip are quantified using Life Cycle Analysis. The proposed framework is applied to an empirical dataset from Shanghai, China. The empirical results reveal that the substitution rates of DLBS to different transport modes have substantial spatiotemporal variances and depend strongly on travel contexts, highlighting the necessity of analyzing the environmental impacts of DLBS at the trip level. Moreover, each DLBS trip is estimated to save an average 80.77 g CO2-eq GHG emissions versus than the situations without DLBS in Shanghai. The annual reduced GHG emissions from DLBS are estimated to be 117 kt CO2-eq, which is substantial and equals to the yearly GHG emissions of over 25,000 typical gasoline passenger vehicles. Additionally, the associations among built environments and GHG emission reductions from DLBS are quantitatively investigated to shed light on the spatial variances in the environmental impacts of DLBS. The results can efficiently support the benefit-cost analysis, planning, and management of DLBS.

Exploring a Novel Electrical-Modeling-Based Route Planning for Vehicle Guidance

This work describes a novel Electrical-Modeling-Based Route Planner (EMBRP) for vehicle guidance within city street networks (maps), which uses an equivalent linear electrical circuit considering traffic flow direction, length, and other physical attributes of the streets as parameters for the mathematical model of the circuit branch resistances. Thus, modeling a city as an electrical circuit results in a system of linear equations, which are solved using a multifrontal method implemented in the Unsymmetric Multifrontal Pack (UMFPACK) library. In addition, a Modified Local Current Comparison Algorithm (MLCCA) is proposed with the aim to find a suitable route meeting the correct traffic flow direction. The EMBRP has the functionality to accept user-defined symbolic models in terms of street parameters extracted from a public database allowing different route planning applications. For instance, low-risk route planning schemes can be explored also routes with multiple origins and a single destination can be plotted using only a single simulation, among other possibilities. The EMBRP is illustrated through the description of nine real case studies. According to the obtained results, suitable planning routes and small computing times are achieved by this proposal. A performance comparison, in terms of memory consumption and computing time, among EMBRP, the heuristic A∗ algorithm and Hspice numeric engine is presented. The smallest computing time was achieved by the EMBRP. The EMBRP can be useful for engineers and researchers studying route planning techniques and new street models for specific applications.

A Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery

The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company.

Multi-criteria route planning with risk contour map for smart navigation

Route planning in maritime transportation is the key to safe, efficient, and smart navigation. We propose a multi-criteria route planning technique for operators to objectively determine the routes according to their intentions. In this study, the navigational traffic risk of a route is quantitatively assessed using a model of a ship. Then, a risk contour map is visualized by structuring the data as absolute danger, hazard factors, and influential factors, which is a framework for route planning. Finally, a multi-criteria route could be modeled by considering the safety, efficiency, convenience, and ability of navigation as the main criteria. The technique assesses each criterion by analyzing the cumulative risk per distance, distance, number of waypoints, and risk gradient of derived routes. In addition, the technique proposes routes by utilizing its algorithm and incorporating contour-based projection and reference points. To verify the proposed technique, we carried out numerical simulations and evaluated actual AIS data. The results show that this technique can not only suggest goal-oriented routes but also assess the used routes. Therefore, the proposed technique can improve the route planning method to be more systematic, which contributes both to smart navigation based on the user's purposes and to future autonomous navigation.

Scalability of Route Planning Techniques

In this paper, we thoroughly analyze the scaling behavior of several state-of-the-art route planning techniques for road networks, all of which rely on preprocessing. One goal is to determine which technique is most suitable to be used on huge networks. To be able to conduct scalability studies in a clean way, we first describe a new kind of road network generator that allows to produce road networks even larger than that of our planet with similar properties as real networks. We then carefully implement several preprocessing-based route planning techniques, as contraction hierarchies, hub labels and transit nodes, to study their space consumption as well as their search spaces in different sized networks. This allows to derive functions that describe their empirical scaling behavior for the first time. We also compare our functions to existing theoretical bounds. We show that several of our results can not be sufficiently explained by the theoretical investigations conducted so far. Hence our results encourage a further look for road network models that allow for better predictions.

ROUTE PLANNING AND OPTIMIZATION OF ROUTE USING SIMULATED ANT AGENT SYSTEM

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The evolutionary technique performs well in simple and less cluttered environments while its performance degrades with large and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation with different environment configurations and is capable of tracking the moving goal and performs equally well as compared to moving target search algorithm.

Optimization of route planning and exploration using multi agent system

This research presents an optimization technique for route planning and exploration in unknown environments. It employs the hybrid architecture that implements detection, avoidance and planning using autonomous agents with coordination capabilities. When these agents work for a common objective, they require a robust information interchange module for coordination. They cannot achieve the goal when working independently. The coordination module enhances their performance and efficiency. The multi agent systems can be employed for searching items in unknown environments. The searching of unexploded ordinance such as the land mines is an important application where multi agent systems can be best employed. The hybrid architecture incorporates learning real time A* algorithm for route planning and compares it with A* searching algorithm. Learning real time A* shows better results for multi agent environment and proved to be efficient and robust algorithm. A simulated ant agent system is presented for route planning and optimization and proved to be efficient and robust for large and complex environments.

Optimization of Route Planning using Simulated Ant Agent System

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation.

Ontology based personalized route planning system using a multi-criteria decision making approach

This study presents a generic ontology-based architecture using a multi-criteria decision making technique to design a personalized route planning system. The real world has become too complex to implement entirely within an information system such as geographic information system (GIS). A route planning technique is an essential geo-related decision support tool, especially in intelligent transportation systems (ITS). In ubiquitous GIS environments, personalization can be accomplished through a user’s preferences stored on mobile appliances. In this manner, personalized and user-centric route planning services using semantic technologies and ontologies perceive user and context models to satisfy user demands and predict their requirements. In the past few years, several studies have been carried out regarding personalized services. However, the existing route finding algorithms suffer from a number of major difficulties, mainly owing to insufficient criteria modeling for a personalized system. Thus, the present study investigates how a user-centric route planning system can be implemented. In order to address this research issue, an ontology-based knowledge modelling technique using an analytic hierarchical process (AHP) is proposed. This technique can facilitate determination of the choice of criteria used for applying an impedance function in the route finding algorithm. From another perspective, AHP explicitly deals with a hierarchy structure and is essentially a theory of measurement and decision making methodology used for combining or synthesizing quantitative as well as qualitative criteria. User-centric results on real data illustrate the strengths of the present approach. It is anticipated that this new technique can be applied to develop new graph algorithms based on semantic web technology and can be used with new semantic graph structures.

무인선박의 항해시스템을 위한 항로계획 기법

For the safety and cost reduction of the navigation in the sea, we need automatic and intelligent system for the ship. For the ship automation, we need a route planning based on GPS and the nautical chart. In this paper, we propose a route planning technique using point of contact of the obstacle and treecreation technique. The efficiency of the proposed technique is proved by comparing with A* search technique that is the most famous search technique for route planning from the optimal point of view.

A computer technique for route planning : Athanassoulis, G. C. and Calogero, V.The Highw. Eng., Vol 21, No 4 (April 1973) pp 4–11

A computer technique for route planning : Athanassoulis, G. C. and Calogero, V.The Highw. Eng., Vol 21, No 4 (April 1973) pp 4–11