Shortest Path Planning Research Articles

Spatial Geometric Transformations and Shortest Path Planning in Plantscape Design

Abstract In this paper, the point features of spatial geometric transformation images in plant landscape design are extracted according to Harris corner point detection algorithm, and then the images related to the extracted feature points are matched in both directions. After that, the path planning in plant landscape design is constructed by using topological map, and finally the influencing factors and the shortest path of plant landscape design are analyzed by using multi-factor evolutionary algorithm (MFEA), and the design effect diagram is evaluated at the same time. The results showed that the relative importance of the four factors “geology, features, elevation and slope” affecting the plant landscape design were 0.2:0.5:0.2:0.1 and 2:5:2:1, respectively, in terms of the mean ratio of the weights and the ratio of the weights of the weighted superposition of the raster map layers. Among the three optimized routes obtained by feature point matching, route 1 has the shortest distance and is in the lowest cost interval, which meets the expectation of shortest path planning while controlling the cost.The standard deviation and entropy maxima of the visual communication effect of the MFEA method on the plant landscape design are 0.9851 and 0.9659 bit, respectively, and the mean value of the peak signal-to-noise ratio is the highest at 1.6903 dB.It shows that the MFEA method has the highest regional color difference and contrast of the spatial geometric transformation based plant landscape design images designed under the method, and the overall design effect is better.

A Review of Research on Shortest Path Planning Algorithms for Mobile Robots

A Review of Research on Shortest Path Planning Algorithms for Mobile Robots

A Shortest Distance Priority UAV Path Planning Algorithm for Precision Agriculture.

Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions. To address these challenges, this study proposes an enhanced Q-learning algorithm designed to optimize UAV path planning by combining static and dynamic obstacle avoidance features. A shortest distance priority (SDP) strategy is integrated into the learning process to minimize the distance the UAV must travel to reach the target. In addition, the root mean square propagation (RMSP) method is used to dynamically adjust the learning rate according to gradient changes, which accelerates the learning process and improves path planning efficiency. In this study, firstly, the proposed method was compared with state-of-the-art path planning techniques (including A-star, Dijkstra, and traditional Q-learning) in terms of learning time and path length through a grid-based 2D simulation environment. The results showed that the proposed method significantly improved performance compared to existing methods. In addition, 3D simulation experiments were conducted in the AirSim virtual environment. Due to the complexity of the 3D state, a deep neural network was used to calculate the Q-value based on the proposed algorithm. The results indicate that the proposed method can achieve the shortest path planning and obstacle avoidance operations in an orchard 3D simulation environment. Therefore, drones equipped with this algorithm are expected to make outstanding contributions to the development of precision agriculture through intelligent navigation and obstacle avoidance.

Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots.

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

Multi-objective Optimization-Based Algorithm for Selecting the Optimal Path of Rural Multi-temperature Zone Cold Chain Dynamic Logistics Intermodal Transportation

The road network in rural areas is complex and the infrastructure is relatively backward. The multi-temperature zone cold chain logistics involves agricultural products with different temperature requirements, which requires considering the transportation cost, carbon emission cost, refrigeration cost, and time cost of different temperature zones during path planning, thereby increasing the difficulty of path planning. Therefore, a multi-objective optimization-based algorithm for selecting the optimal path of rural multi-temperature zone cold chain dynamic logistics intermodal transportation is proposed. Based on the analysis of the multi-temperature cold chain collection and distribution model based on multimodal transportation, a multi-objective optimization model is constructed. This model aims to minimize transportation costs, carbon emission costs, refrigeration costs, time costs, and maximize logistics quality, while satisfying constraints such as transfer schedule times, the number of transport mode conversions, transport mode selection, and time continuity. To solve this model, an improved NSGA-II algorithm is adopted, which combines an improved mutation operator, congestion distance calculation, and the C-W saving algorithm to achieve the optimal transport path solution. Additionally, ArcGIS software is used to implement the shortest path planning based on real road networks. The experimental results show that by selecting the road-rail combined transport mode and adopting the D1–D6–D10 transport path, it is possible to transport fresh agricultural products from location A to the distribution center at location B, with the lowest Pareto fitness value. Furthermore, the algorithm's effectiveness is further verified by completing the end-of-life fresh agricultural product distribution task with four multi-temperature refrigerated vehicles. The study also finds that extending or shortening the latest service time window for customers, although it leads to a decrease or increase in the optimal value of the algorithm's objective function, has little impact on the average distribution time and transport vehicles. These findings provide new theoretical and practical guidance for the path selection of multimodal transportation in multi-temperature cold chain logistics, with significant theoretical and application value.

An Efficient Approach for Shortest Path Planning in Automotive Navigation System

Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.

Research on Shortest Path BFS Strategy in Multi-AGV Scheduling System

With the increasing maturity of automated guided vehicles (AGV) technology and the widespread application of flexible manufacturing systems, enhancing the efficiency of AGVs in complex environments has become crucial. This paper analyzes the challenges of path planning and scheduling in multi-AGV systems, introduces a map-based path search algorithm, and proposes the BFS algorithm for shortest path planning. Through optimization using the breadth-first search (BFS) algorithm, efficient scheduling of multiple AGVs in complex environments is achieved. In addition, this paper validated the effectiveness of the proposed method in a production workshop experiment. The experimental results show that the BFS algorithm can quickly search for the shortest path, reduce the running time of AGVs, and significantly improve the performance of multi-AGV scheduling systems.

Shortest Path Planning and Efficient Fuzzy Logic Control of Mobile Robots in Indoor Static and Dynamic Environments

Efficient navigation in dynamic environments is a critical skill for of mobile robots, where obstacles can stochastically appear. This paper presents a complete navigation and control system that integrates effective path optimization and motion control capabilities for mobile robots evolving in indoor static and dynamic environments. This system consists primarily of two layers. In the Optimization Layer (Global planner), a Deterministic Constructive Algorithm (DCA) quickly generates the shortest path, as a sequence of nodes, to get to the goal position while avoiding the static obstacles. The Control layer (Local planner) employs an Efficient Fuzzy Logic Controller (EFLC) to continuously guide the robot around the detected dynamic obstacles and drive safely the robot along the intended path. Simulations conducted on various maps with different complexities demonstrate the effectiveness of the DCA planner. Finally, validations using V-REP software show the strength of the proposed EFLC that mimics human reasoning for mobile robots navigating in dynamic environments.

Research on Shortest Path Planning and Smoothing Without Obstacle Collision Based on Moving Carrier

In response to the challenges of path planning in complex scenarios, to overcome the influence of optimal path determination by the precision of grid map sizes, and to escape the uncertainty in solving by intelligent algorithms, this paper designs a method for obtaining an adjacency matrix based on node planning of shortest path diagrams with polygonal obstacles and then uses the Dijkstra algorithm to get the shortest path. For irregular curved obstacles, an edge straightening method is proposed. To enhance the applicability of the path, this paper introduces the constraint of minimum turning radius. It researches path smoothing under obstacle conditions based on arcs and straight lines, providing practical solutions for different scenarios. Considering the need to maintain a safe distance due to the size of the moving carrier and the deviation in trajectory tracking, this paper conducts an expansion analysis of obstacles. It obtains the trajectory on the arc after edge straight line fitting, followed by further smoothing treatment. The method proposed in this paper demonstrates excellent accuracy and robustness in path planning through simulation verification, proving its practicality and effectiveness in complex environments.

A&I-ED-TSP: Association and Integration Encoder–Decoder for Traveling Shortest Path Planning

A&I-ED-TSP: Association and Integration Encoder–Decoder for Traveling Shortest Path Planning

Optimization of computer-assisted teaching mode in universities based on Dijkstra’s algorithm

Abstract In the context of digitalizing big data and information, optimizing the teaching model of colleges and universities is a key concern in the education sector. This paper optimizes the shortest path planning of the Dijkstra algorithm around the Floyd algorithm and ant colony algorithm, constructs the model of a computer-aided teaching system in colleges and universities through the Dijkstra algorithm, and constructs the system model indexes from three aspects: students’ cognitive ability, students’ learning performance, and students’ learning interest. A sample of 100 college students was randomly selected, and the accuracy performance of the samples was compared by the Dijkstra algorithm and improved Dijkstra, respectively. The accuracy rate of the improved Dijkstra algorithm fluctuated from 83% to 90%, with a variable rate of 7%, which was relatively small. Then the Dijkstra algorithm was used to analyze the weighting of the cognitive ability index, learning achievement index, and learning interest index. In the cognitive ability index, the memory ability weight of 34% performed better compared with the other five, the good weight in the learning achievement index performed better compared with the other categories, and the more interesting weight in the learning interest index performed better overall compared with the other five categories. This study has guiding reference value for optimizing computer-assisted teaching mode in colleges and universities to improve teaching quality and efficiency of colleges and universities.

Coverage path planning of unmanned surface vehicle based on improved biological inspired neural network

Coverage path planning of unmanned surface vehicle based on improved biological inspired neural network

Marine Predators Algorithm Based on Adaptive Weight and Chaos Factor and Its Application

Aiming at the problems of uneven distribution of initialized populations and unbalanced exploration and exploitation leading to slow convergence, low convergence accuracy, and easy to fall into local optimality of marine predators algorithm (MPA), a marine predators algorithm based on adaptive weight and chaos factor is proposed (ACMPA), the algorithm is applied to the traveling salesman problem (TSP), and the shortest path planning and research are carried out for the traveling salesman problem. Firstly, the improved adaptive weight strategy is used to balance the exploration and exploitation stage of the algorithm and improve the convergence accuracy of the algorithm. Secondly, the chaos factor is used to replace the random factor, and the ergodicity of the chaos factor is used to make it easier for predators to jump out of local optimization and enhance the optimization ability of the algorithm. Finally, 10 benchmark test functions, the CEC2015 test set, and the CEC2017 test set are used to evaluate the effectiveness of the ACMPA. The results show that, compared with the other four intelligent optimization algorithms, the improved ACMPA achieves better results in both mean and standard deviation, and the algorithm has a better effect on the shortest path problem.

Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

The trajectory planning for Unmanned aerial vehicles (UAVs) in the dynamic environments is a challenging task. Many restrictions should be taken into consideration, including dynamic terrain collision, no-fly zone criteria, power and fuel criteria and so on. However, some methods treat dynamic restrictions as static in order to reduce cost and obtain efficient and acceptable paths. To achieve optimal, efficient and acceptable paths, we first use a high-dimension matrix, extended hierarchical graph, to model the unexplored dynamic grid environment and to convert weather conditions to passable coefficient. The original shortest path planning task is then translated to plan the safest path. Therefore, we propose a new forward exploration and backward navigation algorithm, matrix alignment Dijkstra (MAD), to pilot UAVs. We use matrix alignment operation to simulate the parallel exploration process of all cells from time t to \(t+1\). This exploration process can be accelerated on a GPU. Finally, we recall the optimal path according to the navigator matrix. In addition, we can achieve UAV path planning from one source cell to multiple target cells within one single run. We validate our method on a real dynamic weather dataset and get competitive results in both efficiency and accuracy. We analyse the performance of MAD on a grid-based benchmark dataset and artificial maze data. Simulated results show MAD is especially helpful when the grid-based environment is large-scale and dynamic.

Low-Frequency Trajectory Map Matching Method Based on Vehicle Heading Segmentation

Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle’s travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms.

Shortest path planning of a data mule in wireless sensor networks

Shortest path planning of a data mule in wireless sensor networks

Hardware Schemes for Autonomous Navigation of Cooperative-type Multi-robot in Indoor Environment

This study proposes about the autonomous navigation of a multi-robot for transportation in indoor environments. This transportation is integrated with three foldings of VLSI architectures; they are 1) shortest optimal path planning 2) behavioral control between multi-robot with leadership swapping methods as per dynamic conditions and 3) obstacle avoidance by multi-robot. The hardware schemes have been designed for navigation of multi-robot with shortest path planning, based on an extended Dijkstra algorithm along with the Delaunay triangulation method. The behavioral control mechanism between the multi-robot is another challenge at the time of navigation and obstacle avoidance at both static and dynamic conditions in real-time scenario. The leader and follower approaches are deployed for cooperation between multi-robot to accomplish the task. The VLSI architectures are proposed for multi-robot navigation in the warehouse-type indoor environment. It is developed using Verilog HDL, simulated and synthesized with Xilinx Vivado 17.1. The Zynq-7000 SoC ZC702 FPGA is used as the target device.

Sublinear search spaces for shortest path planning in grid and road networks

Shortest path planning is a fundamental building block in many applications. Hence developing efficient methods for computing shortest paths in, e.g., road or grid networks is an important challenge. The most successful techniques for fast query answering rely on preprocessing. However, for many of these techniques it is not fully understood why they perform so remarkably well, and theoretical justification for the empirical results is missing. An attempt to explain the excellent practical performance of preprocessing based techniques on road networks (as transit nodes, hub labels, or contraction hierarchies) in a sound theoretical way are parametrized analyses, e.g., considering the highway dimension or skeleton dimension of a graph. Still, these parameters may be large in case the network contains grid-like substructures—which inarguably is the case for real-world road networks around the globe. In this paper, we use the very intuitive notion of bounded growth graphs to describe road networks and also grid graphs. We show that this model suffices to prove sublinear search spaces for the three above mentioned state-of-the-art shortest path planning techniques. Furthermore, our preprocessing methods are close to the ones used in practice and only require expected polynomial time.

Shortest path planning for mobile chargers

This paper studies the shortest path that a mobile charger should travel when charging a wireless rechargeable sensor network. Under the condition that the latitude and longitude of each node in the wireless rechargeable sensor network are known, we combine the actual working conditions of the mobile charger to establish a TSP problem model and use two improved modern optimization algorithms and three common optimization algorithms to solve. By comparing the results, we conclude that the improved genetic algorithm can obtain the shortest path length of 11485 m with the least number of iterations. The improved genetic algorithm has better applicability to the problem in this background.

A Time-Based Intra-Memory Computing Graph Processor Featuring A* Wavefront Expansion and 2-D Gradient Control

A mixed-signal, time-based 65-nm application-specific integrated circuit is developed for solving shortest-path problems. Digital circuits are collocated with the memory as intra-memory computing. The core follows similar principles from wave routing and, additionally, incorporates a gradient on the periphery of the core to implement the A* algorithm predicted distance heuristic. A leading pulse is propagated from start nodes and is asynchronously latched in neighboring vertex cells and pushed to its four neighbors. Applications include collision avoidance for self-driving cars, shortest path planning, and scientific computing, and are shown to be scalable across many cores. The chip achieves 559 million traversed edges per second at 105 × improved energy efficiency compared with existing platforms such as field-programmable gate array and CPU. The processor operates nominally at 1.79 ns per node with peak power consumption of 26.4 mW.