Path Search Research Articles

Co-Evolutionary Algorithm-Based Multi-Unmanned Aerial Vehicle Cooperative Path Planning

Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative path planning. With this analysis, a multi-UAV cooperative path planning algorithm based on co-evolution optimization was proposed in this paper. Firstly, by analyzing the meaning of multi-UAV cooperative flight, the optimization model of multi-UAV cooperative path planning was given. Secondly, we designed the cost function of multiple UAVs with the penalty function method to deal with multiple constraints and designed two information-sharing strategies to deal with the combination path search between multiple UAVs. The two information-sharing strategies were called the optimal individual selection strategy and the mixed selection strategy. The new cooperative path planning algorithm was presented by combining the above designation and co-evolution algorithm. Finally, the proposed algorithm is applied to a rendezvous task in complex environments and compared with two evolutionary algorithms. The experimental results show that the proposed algorithm can effectively cope with the multi-UAV cooperative path planning problem in complex environments.

A Combined Reaction Path Search and Hybrid Solvation Method for the Systematic Exploration of Elementary Reactions at the Solid-Liquid Interface.

We present a combined simulation method of single-component artificial force induced reaction (SC-AFIR) and effective screening medium combined with the reference interaction site model (ESM-RISM), termed SC-AFIR+ESM-RISM. SC-AFIR automatically and systematically explores the chemical reaction pathway, and ESM-RISM directly simulates the precise electronic structure at the solid-liquid interface. Hence, SC-AFIR+ESM-RISM enables us to explore reliable reaction pathways at the solid-liquid interface. We applied it to explore the dissociation pathway of an H2O molecule at the Cu(111)/water interface. The reaction path networks of the whole reaction and the minimum energy paths from H2O to H2 + O depend on the interfacial environment. The qualitative difference in the energy diagrams and the resulting change in the kinematically favored dissociation pathway upon changing the solvation environments are discussed. We believe that SC-AFIR+ESM-RISM will be a powerful tool to reveal the details of chemical reactions in surface catalysis and electrochemistry.

Optimal path planning using psychological profiling in drone‐assisted missing person search

AbstractSearch and rescue operations are all time‐sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit has begun the deployment of drones to assist in missing person searches with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of á priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a nonlinear optimization to determine the optimal flight path for a given search area and subject profile. Two optimization solvers were compared; genetic algorithms, and particle swarm optimization. Finally, the most effective algorithm was used to create a coverage path for a real‐life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators.

Chaotic heuristic assisted method for the search path planning of the multi-BWBUG cooperative system

Blended-wing-body underwater gliders (BWBUGs) are low cost and have high endurance. As such, they are highly suitable for underwater search missions. However, the search path planning of BWBUGs presents significant challenges due to the complexities in designing the planning model and solution algorithm. In this study, we propose a novel chaotic heuristic assisted method (CHAM) for search missions involving multi-BWBUG cooperative systems. First, our approach addresses the challenges by introducing a probabilistic sensing model that accurately describes the detection performance of the BWBUG. Then, a search path planning model of the multi-BWBUG cooperative system is developed on the basis of the cumulative detection probability and communication energy consumption ratio. Second, we propose the sine-cosine chaos strategy, which exhibits earlier chaotic state entry and higher ergodicity compared to the sine chaos strategy. Leveraging this, CHAM integrates the sine-cosine chaos strategy with a well-designed hierarchical collaboration mechanism. The proposed algorithm adapts different position update strategies at various stages to enhance convergence speed and avoid local optima. Finally, the simulation experiments and statistical analysis results provide compelling evidence that CHAM excels in performing search path planning for the multi-BWBUG cooperative system, and the comprehensive performance of CHAM is better than that of comparison algorithms.

An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion

Aimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and too many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the obstacle rate coefficient is added to the A* algorithm’s evaluation function to build an adaptive cost function; the search efficiency and path safety are increased by improving the search mode; by extracting key nodes, a global path containing only the starting point, key nodes, and endpoints is obtained. The DWA algorithm’s evaluation function is then optimized and the starting azimuth is optimized based on information from the first key node. The experimental results show that in a static environment, compared with the traditional A* algorithm and the improved A* algorithm, the path length is reduced by 1.3% and 5.6%, respectively, and the turning times are reduced by 62.5% and 70%, respectively; compared with the improved ant colony algorithm in the literature, the turning angle is reduced by 29%. In the dynamic environment, the running time of this fusion algorithm is reduced by 12.6% compared with the other hybrid algorithms.

NT-ARS-RRT: A novel non-threshold adaptive region sampling RRT algorithm for path planning

Rapidly-Exploring Random Tree (RRT) algorithm is a widely used path planning method. However, it suffers from low solution efficiency, no search guidance, poor quality of the obtained path and the problem of obvious reduction of search efficiency in the narrow exit environment, which greatly reduces its performance. To overcome these issues, this paper proposes a novel non-threshold adaptive region sampling RRT (NT-ARS-RRT) algorithm for path planning. First, a map preprocessing method is proposed to reduce the explore space of the random tree and improves the path search efficiency especially at narrow exits. Second, a branch-and-leaf backtracking method is proposed to optimize the selection of the nearest node. Third, a distance threshold connection constraints optimization method between the newest node and the target point is proposed to reduce the generation of unnecessary nodes and thus further improves the algorithm’s speed. Fourth, the initial path is optimized by using a rewiring method based on triangular inequality to improve the path quality. Experimental results show the effectiveness of the proposed algorithm.

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain's Cognitive Mechanism.

Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat's brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain's cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks.

A Multi-Stage Real-Time Fast Search Method for Closed-Loop Paths Based on Grid Hierarchical Partitioning Characteristics

Prior to switching operations, the dispatch automation system is required to be able to search for closed-loop paths quickly in real time. In order to improve the efficiency of closed-loop path search, this paper proposes a multi-stage real-time fast search method for closed-loop paths based on the characteristics of power grid hierarchical partitioning. First, we divide the closed-loop judgement process into three phases: initialization, acceptance of the closed-loop judgement command, and post-operation. Then, we define three types of nodes, including root nodes, same-layer contact nodes, and common nodes, and construct path data models for each type of node in accordance with the node liaison relationship in grid hierarchical zoning. In the initialization phase, we design a method for the automatic generation of real-time nodal path data models and study a hierarchical partitioned closed-loop path search method based on a nodal path data model for the phase of accepting closed-loop judgement commands. Next, we design a fast local correction method for nodal path models after the switch closure and disconnection operations in the post-operation idle phase are performed. Finally, a real power grid model is used as an example to test the above closed-loop path search method, which improves the search efficiency by eight times compared with the conventional breadth-first search method. The method is applicable to real large and complex power grids.

Micro-Factors-Aware Scheduling of Multiple Autonomous Trucks in Open-Pit Mining via Enhanced Metaheuristics

Traditional open-pit mineral transportation systems are typically subject to manual command, frequently leading to vehicular delays and traffic congestion. With the advancement of automation and electrification technologies, this study proposes a highly accurate scheduling method for multiple autonomous trucks in an open-pit mine. This model considers micro-level temporal and spatial factors to tackle the task of scheduling autonomous trucks within open-pit mines. The cost function of the concerned scheduling problem is a comprehensive evaluation of energy consumption, time, and output. Beyond the loading and unloading activities, the model also factors in the charging requirements of autonomous trucks in mining regions. The scheduling model integrates a Voronoi diagram search and optimal spatial path time matching, aiming to provide superior mission planning and decision-making solutions for autonomous trucks in mining regions. For an efficient solution to the scheduling problem, we propose an improved-evolution artificial bee colony (IE-ABC) algorithm. This algorithm improves the global search and re-initialization processes and conducts algorithm ablation experiments to closely examine their impact on optimization. Simulation results across various algorithms, cost function definition strategy, and encoding strategy show that our method can improve scheduling performance in energy consumption and time. Experimental results demonstrate that the proposed model and algorithm can effectively solve the scheduling decision-making problem in an unmanned open-pit mine.

Long-lived spin waves in a metallic antiferromagnet

Collective spin excitations in magnetically ordered crystals, called magnons or spin waves, can serve as carriers in novel spintronic devices with ultralow energy consumption. The generation of well-detectable spin flows requires long lifetimes of high-frequency magnons. In general, the lifetime of spin waves in a metal is substantially reduced due to a strong coupling of magnons to the Stoner continuum. This makes metals unattractive for use as components for magnonic devices. Here, we present the metallic antiferromagnet CeCo2P2, which exhibits long-living magnons even in the terahertz (THz) regime. For CeCo2P2, our first-principle calculations predict a suppression of low-energy spin-flip Stoner excitations, which is verified by resonant inelastic X-ray scattering measurements. By comparison to the isostructural compound LaCo2P2, we show how small structural changes can dramatically alter the electronic structure around the Fermi level leading to the classical picture of the strongly damped magnons intrinsic to metallic systems. Our results not only demonstrate that long-lived magnons in the THz regime can exist in bulk metallic systems, but they also open a path for an efficient search for metallic magnetic systems in which undamped THz magnons can be excited.

Automatic generation method of power grid maintenance operation ticket based on GA-ANN optimization algorithm

The automatic generation method of power grid maintenance operation ticket based on the GA-ANN optimization algorithm is designed. An automatic generation model of power grid maintenance operation ticket, including data communication, power grid information topology, task planning, path search, path selection, operation ticket generation and simulation of rehearsal agent, is built. This paper takes the operation sequence generated by different equipment performing operation tasks as the training sample of the GA-ANN model, obtains the change rule of operation sequence of each equipment agent, and realizes the automatic generation of maintenance operation tickets for different equipment. It is verified by experiments that the time of automatic generation of operation tickets is fast and can complete the maintenance and invoicing of various equipment.

RRT path planning algorithm with enhanced sampling

Due to the low efficiency and low path quality of the RRT algorithm, this paper introduces a sampling-enhanced RRT(SE-RRT) algorithm with four improvements to the RRT algorithm. In the path search phase, the gravitational coefficient is added to improve the efficiency of finding the feasible path without losing randomness. In the path optimization phase, turning points are selected from nodes of the original path by global rewiring. Then the sampling space is established around the turning points for local re-optimization. Finally, a near-optimal path is obtained and can be iterated to the optimal path according to the demand. In the code implementation phase, efficient path collision detection is added to prevent local paths from crossing obstacles.

Performance Analysis Model and Deterministic Routing Decision Algorithm for Broadband Real-Time Services in Wireless Multi-Hop Networks

Wireless multi-hop networks (WMN) are considered as one of promising solutions for broadband real-time service transmission in the Internet of Everything. Due to the timevarying wireless channels, the deterministic communication of services becomes extremely challenging for WMN. In order to comprehensively evaluate the service provisioning capability of WMN, we first propose an end-to-end performance analysis model for reliable transmission of broadband real-time services in WMN based on stochastic network calculus (SNC). We define a general expression for the effective service process that a changing channel provides. The end-to-end delay violation bound and hop-delay relationship are obtained by applying the effective service process and the cascade theorem. Then based on this analysis model, we propose a deterministic routing search algorithm, where the initial path search sets are reduced through the hopdelay relationship. And the routes are obtained from the reduced path sets. The deterministic routing decision algorithm can ensure the bounded end-to-end delay of services with a certain probability. Theoretical and simulation results demonstrate the correctness of the proposed performance analysis model and the effectiveness of the routing algorithm.

Research on the Bias Sampling RRT Algorithm for Supermarket Chain Distribution Routes under the O2O Model

The purpose of this article is to propose the Bias Sampling RRT algorithm and use it as an optimization algorithm for supermarket chain distribution routes under the O2O model. As a retail store in the transformation and upgrading of chain stores, the actual terrain factors in distribution directly affect the timely delivery of goods from online to offline. The Bias Sampling RRT algorithm, as a path search method for time window vehicle routing problems, can find the optimal path that meets time window constraints. The applicability and effectiveness of the Bias Sampling RRT algorithm have been demonstrated through map simulation calculations. The simulation results show that compared with the RRT method, the Bias Sampling RRT algorithm has a shorter distribution path and shorter distribution time. This method is very suitable for the distribution activities of chain supermarkets or single store retail enterprises in the complex transformation and upgrading of actual terrain.

A Multirobot Distributed Collaborative Region Coverage Search Algorithm Based on Glasius Bio-Inspired Neural Network

There are many constraints for a multi-robot system to perform a region coverage search task in an unknown environment. To address this, we propose a novel multi-robot distributed collaborative region coverage search algorithm based on Glasius bio-inspired neural network (GBNN). Firstly, we develop an environmental information updating model to represent the dynamic search environment. This model converts the environmental information detected by the robot into dynamic neural activity landscape of GBNN. Secondly, we introduce the distributed model predictive control method in search path planning to improve search efficiency. In addition, we propose a distributed collaborative decision-making mechanism among the robots to produce several dynamic search sub-teams. Within each sub-team, collaborative decisions are made among the robot members to optimize the solution and obtain the next movement path of each robot. Finally, we conduct experiments in three aspects to verify the effectiveness of the proposed method. Compared with three algorithms in this field, the experimental results demonstrate that the proposed algorithm exhibits good performance in a multi-robot region coverage search task.

A heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN-based position posture map and rapidly-exploring random tree

The environment of tomato bunches is complex, and the fruit volume is relatively large, so manipulator picking-harvest motion planning should consider not only how to pick, but also how to avoid obstacles after picking tomato bunches and harvesting them from the complex environment, which puts forward strict requirements for picking-harvest motion performance. However, existing sampling-based algorithms still have problems of blind expansion and low efficiency. To solve these problems, a heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN (convolution neural network)-based position posture map (PPM) and rapidly-exploring random tree (RRT) is proposed in this paper. This method comprehensively considers the sampling effectiveness of the whole process of picking and harvesting, search speed, path cost and manipulability of the manipulator, combining a sampling-based path planning algorithm and deep learning framework. The method utilizes CNN and FCN(Fully Connected Neural Networks) for generating a heuristic cost map where each voxel has a cost-to-go value toward the target and an optimal target posture value to guide the expansion tree. In addition, the sampling space of obstacle avoidance planning is reduced by a spatial partition method, and the difficulty caused by the increase in the volume of the end-effector clamping fruit is solved by prioritizing the planning of the fruit harvest path and then planning the picking path. On the basis of the above research, a large number of harvest experiments have verified the effectiveness of the proposed algorithm. Research and experimental results show that, based on the PPM-RRT algorithm, the picking-harvest time of a single tomato bunch is 13 s, and the success rate is close to 100%. Compared with state-of-the-art sampling algorithms, the planning time is reduced by more than 70%, the path length is reduced by more than 24%, and the manipulability of the manipulator is increased by 38.9%. The proposed path planning method is effective in complex environments.

Method of Achieving the Goal on a Graph Model with Two Quality Criteria

The features of the construction and application of graph models for solving applied problems are considered. A graph model with two quality criteria is proposed, on which the search for optimal paths between given graph vertices is performed. Each edge of the graph has a weighting factor that determines the number of time unitsrequired to pass this edge. Each vertex can be in one of two states: “open” or “locked”. Initially, all vertices are open, but their states may change in the process of problem solving. The search for a solution is limited by a given time. If during the movement along the chosen route the graph vertices become blocked, it is necessary to look for alternative ways to achieve the goal. A method for constructing a Pareto set from which admissible paths are selected is proposed. The notion of an admissible path on a graph is defined. A procedure for choosing a path from the Pareto set has been developed. Upon completion of the choice, the path is considered optimal for follo wing it from the initial vertex to the target. Situations that can occur in the process of choosing a path and passing along it are presented. Based on the selection procedure, an algorithm for finding optimal paths bet ween given vertices on a graph model has been developed.

Multi-UAV Cooperative and Continuous Path Planning for High-Resolution 3D Scene Reconstruction

Unmanned aerial vehicles (UAVs) are extensively employed for urban image captures and the reconstruction of large-scale 3D models due to their affordability and versatility. However, most commercial flight software lack support for the adaptive capture of multi-view images. Furthermore, the limited performance and battery capacity of a single UAV hinder efficient image capturing of large-scale scenes. To address these challenges, this paper presents a novel method for multi-UAV continuous trajectory planning aimed at the image captures and reconstructions of a scene. Our primary contribution lies in the development of a path planning framework rooted in task and search principles. Within this framework, we initially ascertain optimal task locations for capturing images by assessing scene reconstructability, thereby enhancing the overall quality of reconstructions. Furthermore, we curtail energy costs of trajectories by allocating task sequences, characterized by minimal corners and lengths, among multiple UAVs. Ultimately, we integrate considerations of energy costs, safety, and reconstructability into a unified optimization process, facilitating the search for optimal paths for multiple UAVs. Empirical evaluations demonstrate the efficacy of our approach in facilitating collaborative full-scene image captures by multiple UAVs, achieving low energy costs while attaining high-quality 3D reconstructions.

Study on deep reinforcement learning-based multi-objective path planning algorithm for inter-well connected-channels

Defining inter-well connectivity is very important for the water injection development of carbonate fractured-vuggy reservoirs. However, most conventional methods based on logging or seismic data are subjective. In this research, multi-attribute seismic data were used to characterize the 3D search space, and an improved reward function was designed based on the law of fluid flow and appropriate heuristics. A deep reinforcement learning model based on an improved reward function was designed to search for inter-well connected channels. Selecting the shortest channel length and the largest flow volume were the optimization objectives. We propose the multi-objective evolutionary optimization algorithm with decomposition and differential evolution (MOEA/D-DE) based on an improved mutation operator to automatically obtain the optimal inter-well connected channels. The searched channels can readily show the spatial distribution of multi-scale fractures and caves. The experimental results of the Tahe oilfield show that the improved algorithm effectively enhanced the local convergence performance of the multi-objective algorithm and that the automatic search paths were consistent with the characteristics of seismic static data and tracer test results. Our model can better reflect the spatial distribution of a fracture cavity at different scales and offers important guidance for on-site development adjustment work in the water injection development stage.

What We Think Becomes the Reality of Our Lives

This is a review on the book by Linda Gregory Three Paths to Autonomy and Self-Actualization. Integration of Transactional Analysis, Spirituality, and Quantum Field. The author of the review analyzes the material of the book, namely the integration of the spiritual aspect with Transactional Analysis and quantum field theory. Also the reviewer writes about her own search path, similar to the path of Linda Gregory. This review describes the general content of the book, provides brief descriptions of the main techniques that are presented by Linda Gregory as recommended for achieving results: meditation and practice of Heart Math, necessary to restore and strengthen contact with the Inner Child.