Path Finding Algorithm Research Articles

Heuristic-based vehicle arrangement for ro-ro ships

In this paper, a two-level search strategy fused with an improved no-fit polygon algorithm and improved bat algorithm is proposed to obtain the layout points of multiple vehicles. Additionally, a space–time scheduling strategy is proposed using the Improved D*Lite Algorithm (ID*Lite) and improved Bezier curve to generate the trajectories of individual vehicles. Furthermore, a conflict resolution strategy is introduced to address the collision conflict problem during multi-vehicle scheduling. The proposed strategies aim to achieve the objectives of shortest scheduling time and smallest array space in order to reduce the time and space cost of vehicle loading on ro-ro carriers. Through a comparison with other algorithms (e.g., at high loading pressure, Scenario 3), the strategies presented in this paper demonstrate advantages such as higher deck utilization (92.234%), shorter path length, fewer collision conflicts, and more balanced weighting. Mainstream methods only focus on a certain part of vehicle transportation, we not only use more stable parking spot generation algorithm and parking order generation algorithm, but also more high-speed multi-vehicle path-finding algorithm, path optimization strategy, multi-vehicle scheduling strategy, which innovatively form a complete vehicle planning process, and at the same time, as an algorithmic framework that can be generalized to any 2d or 3d scenarios.

Detection of Putative Ligand Dissociation Pathways in Proteins Using Site-Identification by Ligand Competitive Saturation.

Drug efficacy often correlates better with dissociation kinetics than binding affinity alone. To study binding kinetics computationally, it is necessary to identify all of the possible ligand dissociation pathways. The site identification by ligand competitive saturation (SILCS) method involves the precomputation of a set of maps (FragMaps), which describe the free energy landscapes of typical chemical functionalities in and around a target protein or RNA. In the current work, we present and implement a method to use SILCS to identify ligand dissociation pathways, termed "SILCS-Pathway." The A* pathfinding algorithm is utilized to enumerate ligand dissociation pathways between the ligand binding site and the surrounding bulk solvent environment defined on evenly spaced points around the protein based on a Fibonacci lattice. The cost function for the A* algorithm is calculated using the SILCS exclusion maps and the SILCS grid free energy scores, thereby identifying paths that account for local protein flexibility and potential favorable interactions with the ligand. By traversing all evenly distributed bulk solvent points around the protein, we located all possible dissociation pathways and clustered them to identify general ligand unbinding pathways. The procedure is verified by using proteins studied previously with enhanced sampling molecular dynamics (MD) techniques and is shown to be capable of capturing important ligand dissociation routes in a highly computationally efficient manner. The identified pathways will act as the foundation for determining ligand dissociation kinetics using SILCS free energy profiles, which will be described in a subsequent article.

Modern Methods and Algorithms of Path Planning for Autonomous Mobile Robots, Their Objective Functions

The current situation in the world of research algorithms for path planning by autonomous robots shows that there are a large number of algorithms and their modifications that require researchers and engineers of autonomous robotics to have a fine understanding of the fundamental aspects of their functioning. The general goal of the research is to conduct an analysis of existing algorithms, methods and their modifications from the point of view of the component of the mathematical apparatus, namely their target functions. The research examines 38 modern algorithms and methods of path planning by autonomous robots. Considered the importance and criticality of understanding the objective function for the field of robotics. Examples illustrating the importance of this aspect are given. In the process of research, algorithms are presented in the form of formalized generalized mathematical formulas taking into account possible minimization/maximization, their improvements and improvements. For each considered algorithm and method, a conclusion is given regarding its target function. In the study, the methods of "Dynamic games" are separately highlighted and the application of the fundamental "Method of solving functions of A.O. Chikryi" for path planning is considered. In general, information on the advantages and importance of understanding the mathematical apparatus of pathfinding algorithms by autonomous robots for solving scientific problems is provided. The generalized results are summarized in a single comparative table, which provides the main formalized function of the algorithms, reveals the main idea, advantages/disadvantages, the scope of application and an example of use. The comparative table is presented in the form of a generalized auxiliary reference element of the study.

Optimizing Road Networks: A Graph-Based Analysis with Path-finding and Learning Algorithms

Optimizing Road Networks: A Graph-Based Analysis with Path-finding and Learning Algorithms

Towards Improving the Efficiency of Drug Repurposing by Leveraging Node Promiscuity in Biomedical Knowledge Graphs

To accelerate the time- and labor-intensive processes of drug discovery and repurposing, it is increasingly common to mine knowledge sources for connections between diseases and the drugs that can treat them. In this paper we address the scalability challenge in the connection mining, by introducing algorithms that can be used to find plausible mechanistic connections between drugs and the potentially associated diseases in biomedical knowledge graphs. These connections are then presented to biomedical experts as candidate hypotheses for further studies of whether the drugs can be repurposed to treat the diseases. One challenge that has to be addressed in this effort is the processing of promiscuous knowledge-graph nodes, that is, nodes associated with numerous relationships that may not be unique or indicative of the node properties. As it turns out, the multiplicity of relationships involving promiscuous graph nodes may prevent the aforementioned path-finding algorithms from aiding in drug repurposing. To address the promiscuous-node challenge, we introduce promiscuity scores for nodes and paths in knowledge graphs, and incorporate the scores in the proposed path-finding algorithms. We report experimental results that indicate that paths with low promiscuity scores could be meaningful and of interest to biomedical experts in drug repurposing.

Pseudo- World: The Role of Augmented Reality and AI in Construction and Retrofitting

The construction industry is undergoing a revolution with the integration of augmented reality (AR) and artificial intelligence (AI). These technologies are transforming the way construction projects are planned and executed, leading to increased productivity, enhanced design visualization, and more informed decision-making. Our research focuses on developing Pseudo World, a platform that converts 2D house plans into interactive 3D models, allowing for real-time exploration and collaboration through AR. By combining machine learning techniques, pathfinding algorithms, and graph-based models, our approach optimizes spatial planning and retrofitting decisions. This innovative solution enables immersive visualization, enables more effective defect detection and management, and streamlines construction workflows, thereby positioning AR as a driver of innovation in construction planning.

AGV path planning based on dynamic priority method and environmental weight A-star

In order to plan the shortest path and optimise the search speed, an environmental weight A-star (A*) algorithm is proposed in the paper. This method dynamically adjusts the weights based on the ratio of environmental obstacles at each search, which makes the search time shorter and more stable. To optimise the conflict resolution, the local multi-direction method is introduced on the basis of the one-way map method. This method sets a set of travelling direction rules for the road, combined with the path-finding algorithm, it can get the path that conforms to the rules. The conforming path effectively avoids the opposite direction conflict, and eliminates detour problem. At the same time, a dynamic priority method based on traffic prediction is proposed to solve node conflicts. The method dynamically assigns the priority of AGVs in a node conflict based on the predicted traffic flow. It can improve the conflict resolution efficiency during localised congestion. The results suggest the environmental weight A* algorithm is faster and more stable in searching.

Proposed Outlook based on Different Obstacle Avoidance and Pathfinding Algorithms

Abstract. Currently, a variety of papers explore various directions in automated pathfinding and obstacle avoidance, with scholars developing diverse algorithmic models to address different theoretical frameworks. These models often enhance existing optimization algorithms or combine two distinct approaches to achieve improved results. In practical applications, it is common that no single paper can fully encompass the necessary background knowledge, which highlights the urgent need for a comprehensive article that integrates and enriches this knowledge base. Such a work would effectively address most path planning and obstacle avoidance challenges. This paper employs a literature review method to study automated pathfinding algorithms and obstacle avoidance systems. Its primary aim is to analyze cutting-edge research in these areas, providing a detailed examination and explanation of each relevant paper. Additionally, the paper critiques the current literature and the field as a whole, while also offering insights and prospects for future research directions in automated pathfinding and obstacle avoidance, ultimately contributing to advancements in the discipline.

Local weather routing in avoidance of adverse sea conditions based on reachability theory

In this paper, we put forward a local weather routing framework based on reachability theory to plan a time-optimal risk avoidance path in face of sudden changes in sea conditions. Our framework comprises of two phases, where the escape phase directs the vessel to withdraw from risky heading-speed combinations dictated by MSC.1/Circ. 1228. Subsequently, in the return phase, the algorithm guides the vessel back to its original global route while staying outside dangerous zones. Notably, our application of a four-dimensional state space for the description of a vessel’s configuration can accommodate both the safety and kinematic constraints. In the proposed state space, the path-finding algorithm based on reachability theory can figure out the optimal heading and speed of vessel simultaneously, providing a formal assurance of safety and global time-optimality for returning. To achieve this objective, we formulate and solve a new variational-inequality (VI) governing the forward propagation of the reachable set with both angular and acceleration rate treated as control inputs. Last but not least, we provide a rigorous proof for the proposed VI in the framework of viscosity solution and demonstrate the validity of our approach in two test scenarios including one integrated with a realistic control architecture.

A Biochemistry-Inspired Algorithm for Path Planning in Unmanned Ground Vehicles

Unmanned ground vehicles (UGVs) have gained significant attention due to their extensive applications in both military and civilian sectors. For effective UGV deployment, path planning algorithms must prioritize computational efficiency, solution reliability, and runtime performance while maintaining path quality. Autonomous path planning remains a critical challenge in UGV navigation, as conventional methods, while effective, often suffer from considerable computational overhead. To address this issue, we propose a novel biochemistry-inspired path planning algorithm designed specifically for static grid-based scenarios. MetaPath demonstrates remarkable computational efficiency while maintaining solution quality across different obstacle densities in benchmark environments. Specifically, the algorithm achieves path lengths within ±5% of all benchmark algorithms while dramatically reducing the exploration space, visiting up to 10% of the cells explored by conventional approaches such as A*. This superior efficiency translates into exceptional runtime performance, executing up to 3000 times faster than bio-inspired algorithms like Ant Colony Optimization (ACO) and the Genetic Algorithm (GA), performing nearly three times faster than the widely used A* algorithm, and maintaining competitive performance with efficient algorithms like Breadth-First Search (BFS) and Particle Swarm Optimization (PSO), thereby establishing the algorithm as a highly efficient pathfinding solution. Most notably, MetaPath introduces a novel approach as the first chemistry-inspired pathfinding algorithm, guaranteeing path discovery when one exists within reasonable computational time, a crucial advantage over some benchmark algorithms that may fail to converge or require excessive computational resources in complex scenarios.

(Invited) Prediction of Electrode-Electrolyte Degradation through First-Principles and Data-Driven Reaction Networks

Despite decades of work, there is still considerable uncertainty regarding the major components of the solid-electrolyte interface (SEI) and its dynamic formation mechanism as a function of electrolyte and anode composition. Here we present a our data-driven first-principles framework using a combination of high-throughput calculations, reaction networks, machine learning and microkinetic modeling. Our automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. We explore this landscape using stochastic methods and shortest pathfinding algorithms, which yield the most likely reaction pathways which are then refined with transition state calculations and kinetic information. The results of the framework show promise in being able to automatically recover previous insights on single reaction pathways, as well as successfully predicting the early dynamics and competitive nature of the SEI formation. As examples, we present i) formation mechanisms of LEMC as compared to LEDC and ii) recover the Peled-like separation of the SEI into inorganic and organic domains resulting from rich reactive competition. By conducting accelerated simulations at elevated temperature, we track SEI evolution, confirming the postulated reduction of lithium ethylene monocarbonate to dilithium ethylene monocarbonate and hydrogen gas. Relevant for future reaction networks, we use first-principles methods to uncover new mechanistic understanding of decomposition pathways for LiPF6 and EC, the latter under oxidative conditions. These findings furnish fundamental insights into the dynamics of SEI formation and demonstrate a path forward toward a predictive understanding of electrochemical passivation.

Numerical Solutions to the Variational Problems by Dijkstra’s Path-Finding Algorithm

In this work, we propose the general idea of using a path-finding algorithm to solve a variational problem. By interpreting a variational problem of finding the function that minimizes a functional integral as a shortest path finding, we can apply the shortest path-finding algorithm to numerically estimate the optimal function. This can be achieved by discretizing the continuous domain of the variational problem into a spatially weighted graph. The weight of each edge is defined according to the function of the original problem. We adopt the Moser lattice as the discretization scheme since it provides adjustable connections around a vertex. We find that this number of connections is crucial to the estimation of an accurate optimal path. Dijkstra’s shortest path-finding algorithm was chosen due to its simplicity and convenience in implementation. We validate our proposal by applying Dijkstra’s path-finding algorithm to numerically solve three famous variational problems, i.e., the optical ray tracing, the brachistochrone, and the catenary problems. The first two are examples of problems with no constraint. The standard Dijkstra’s algorithm can be directly applied. The third problem is an example of a problem with an isoperimetric constraint. We apply the Lagrangian relaxation technique to relax the optimization in the standard Dijkstra algorithm to incorporate the constraint. In all cases, when the number of sublattices is large enough, the results agree well with the analytic solutions. In all cases, the same path-finding code is used, regardless of the problem details. Our approaches provide more insight and promise to be more flexible than conventional numerical methods. We expect that our method can be useful in practice when an investigation of the optimal path in a complex problem is needed.

Game Pathfinding System Design based on A-Star Pathfinding Algorithm

Abstract. Pathfinding systems are used in many applications today, including navigation systems, autonomous driving systems, and games. The core part of the pathfinding system is the pathfinding algorithm, A Star pathfinding algorithm is a kind of efficient pathfinding algorithm, which is especially suitable for the design of game pathfinding. In this paper, based on the A Star algorithm, Godot game engine and C Sharp language are used to write a pathfinding program. After comparing different pathfinding methods and the estimated function, the A Star algorithm is improved according to the needs of the actual game pathfinding system, so that the pathfinding program can avoid crossing the obstacles through the diagonal by searching the surrounding obstacles and excluding unavailable nodes, and find a shorter path by using the Euclidean distance estimated function. The program applies the improved A Star algorithm in practice, and finally can complete the basic pathfinding needed in the game, which is practical for the design of the game's pathfinding system.

Conceptual Design of Aircraft System Routing Architectures Using a Bio-Inspired Algorithm

The increasing complexity of aircraft systems and the shift toward electrical aircraft are driving the introduction of entirely new concepts in aircraft systems and airframe design; it is therefore likely that the evaluation of aircraft systems and their integration with the airframe in the conceptual design phase will play an ever more crucial role in determining the feasibility of a newly proposed aircraft. This paper proposes a bio-inspired design algorithm for allocating space within the airframe to interconnections that supply aircraft systems. The proposed method is an adaptation of an existing pathfinding algorithm, with a novel mechanism to avoid overlap between systems that require segregation. The input required for the proposed method is likely to be available at the conceptual design phase. A detailed description of the proposed method is given, followed by a demonstration using a design problem based on an existing aircraft. Results of the demonstration show that the proposed method is capable of handling redundant systems and their segregation by reducing the overlap between different systems.

Trajectory optimization and obstacle avoidance of autonomous robot using Robust and Efficient Rapidly Exploring Random Tree.

One of the key challenges in robotics is the motion planning problem. This paper presents a local trajectory planning and obstacle avoidance strategy based on a novel sampling-based path-finding algorithm designed for autonomous vehicles navigating complex environments. Although sampling-based algorithms have been extensively employed for motion planning, they have notable limitations, such as sluggish convergence rate, significant search time volatility, a vast, dense sample space, and unsmooth search routes. To overcome the limitations, including slow convergence, high computational complexity, and unnecessary search while sampling the whole space, we have proposed the RE-RRT* (Robust and Efficient RRT*) algorithm. This algorithm adapts a new sampling-based path-finding algorithm based on sampling along the displacement from the initial point to the goal point. The sample space is constrained during each stage of the random tree's growth, reducing the number of redundant searches. The RE-RRT* algorithm can converge to a shorter path with fewer iterations. Furthermore, the Choose Parent and Rewire processes are used by RE-RRT* to improve the path in succeeding cycles continuously. Extensive experiments under diverse obstacle settings are performed to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach outperforms existing methods in terms of computational time, sampling space efficiency, speed, and stability.

Estimation of travel time reduction for ice-breaking ships when considering ice information data

Abstract In light of the recent increase in polar shipping and potential future increase with continued climate change reliable routing in ice-covered waters becomes increasingly important for environmental, economic and safety concerns. Dependable route suggestions have the potential to reduce travel times through polar waters significantly. We apply the Anytime Repairing A* pathfinding algorithm to classified Copernicus Sentinel 1 radar images to estimate how much travel times can be reduced. For multiple example scenarios, it is quantified how much the travel time is reduced if a ship follows these suggestions compared to navigating without any ice information available exterior to the visual range (VR). It was found that having ice information available is most beneficial in complex ice situations, where it can reduce travel time by up to 34% for a VR of 2 km.

AI-Driven Traffic Simulation using Unity: Implementing Finite State Machines for Adaptive NPC Behaviour

This research develops an AI-powered traffic simulation using the Unity Engine, leveraging finite state machines (FSM) to enable adaptive and responsive non-player characters (NPCs). The integration of FSM with advanced pathfinding algorithms, such as A*, allows NPCs to dynamically adjust their behavior based on traffic conditions, obstacles, and environmental changes. The experimental results indicate a 25% improvement in route optimization and a 30% reduction in path conflicts compared to conventional static models, demonstrating the robustness of the proposed approach. Optimized navmesh deployment further enhances navigation fluidity, ensuring efficient agent movement in high-density scenarios without compromising system performance. The findings establish the effectiveness of the FSM-driven NPC behavior in simulating realistic traffic environments, contributing both to the advancement of AI applications in game development and urban planning. By providing an interactive platform for traffic management, this simulation offers a practical tool to study congestion patterns and test intervention strategies. In addition, it improves player engagement by fostering emergent gameplay through dynamic NPC interactions. Future work could explore the integration of real-time procedural generation or multiplayer functionality to enrich simulation depth and scalability. This study provides a comprehensive framework that bridges AI-based mechanics with simulation technology, providing significant insights for researchers and practitioners in game design, artificial intelligence, and urban planning.

A new modification to the A* path-finding algorithm to improve its space and time performance

<p>We propose a new modification to the A* algorithm named AA* that significantly improves space and time complexities. In AA*’s forward pass, the node sets (open and closed) are not used, and only the local node neighborhood is saved to take the next move decision. AA* needs a backward pass to bridge and correct gaps and bad decisions made in the forward pass. The work of the backward pass is far less than that of the forward pass, as most of the task has been done. It is shown via empirical experimental work that our proposed AA* algorithm is superior to the classical A* algorithm in the typical three metrics: running time, number of probed nodes, and length of path. Furthermore, our experimental work showed that AA* is suboptimal in terms of length of path compared to the original Dijkstra’s algorithm with an accuracy of 96.95%.</p>

Diverse Self-assembly Pathways in Nematic Compartment Network: Topological Percolation and Pathfinding.

The self-assembly of nematic molecules in microcompartments with unambiguously defined surface anchoring is well predictable and is likely to have a single stable topological structure. Here, in contrast, a confined nematic system comprising an array of microcompartments interconnected by channels is demonstrated, exhibiting diverse molecular assembly pathways leading to the formation of four types of topological structures and twelve different patterns randomly distributed. Intercompartment communication via channels plays a crucial role in the diversity of patterns and distributions. It determines the sizes and structures of domains separated by channel defects. The domain structure, which features a pathfinding algorithm and reverse tree structure, can be modelled by an isotropically directed bond percolation with additional restrictions. This system serves as a model for controlled randomness and restricted growth of networks, with potential applications in anticounterfeit protection as a physically unclonable function (PUF) with multiple-level communication protocols.

Automatic cable routing based on improved pathfinding algorithm and B-spline optimization for collision avoidance

Abstract With the recent growth of electrical and electronic systems such as electric vehicles, the demand for automatic cable routing for electrical wiring design is increasing. However, real industry use cases of automatic cable routing are still rare especially in three-dimensional design. In this study, we propose a new pathfinding algorithm, JPS–Theta*, which combines the existing pathfinding algorithms, Jump Point Search and Theta*, that is better suited for cable routing. In addition, we propose a B-spline optimization algorithm to create natural cable shapes while avoiding collisions. In the experiments, it was found that the proposed pathfinding algorithm complements the existing algorithms and is thought to be more suitable for the purpose of automatic cable routing. Additionally, ant colony optimization for continuous domains, a meta-heuristic algorithm, was successfully used for optimizing the B-spline to obtain cable shapes without collision. Lastly, as a case study, the proposed method was directly applied to the electrical panel design to show its effectiveness. We expect that the proposed method will be able to improve the efficiency and quality of electrical wiring design.