Path Finding Algorithm Research Articles

Integrating GIS, deep learning, and environmental sensors for multicriteria evaluation of urban street walkability

The walkability of urban streets (WoUS) benefits public health and urban livability, but there is still no consensus on the quantitative and comprehensive evaluation of walkability. However, emerging deep-learning, sensor-network, and GIS technologies suggest the possibility of overcoming previous limitations. Therefore, this study explores a new approach for multicriteria evaluation of WoUS, including physical and perceived walkability. After capturing street-view images, traffic-flow data, and environmental-sensor data covering the streets to be analyzed, we automatically calculate physical walkability from four criteria (walkability index, street quality, traffic flow, and physical environment). We first integrate 2-dimensional analysis of walk score, noise, and light using GIS, then perform 3-dimensional composition calculations of greenery, enclosure, and relative walking width using semantic segmentation, vehicle density using multi-object tracking, and indicator weighting distributions using an analytic hierarchy process. Perceived walkability is evaluated by a hierarchy of walking needs model comprising feasibility, accessibility, safety, comfort, and pleasurability. As an empirical study, we conduct an experiment within Anonymous University for physical and perceived walkability evaluations. The results indicate that with perceived WoUS as a benchmark, the automated physical WoUS results reveal the strengths in data integration and processing that are feasible, reliable, and cost-effective. This was an initial attempt at constructing an integrated research methodology for WoUS from various walking-related variables. This framework could support designers as an assistive tool for generating both quantitative and qualitative judgments from various criteria and recommending walkable pedestrians paths to motivate people to walk further, longer, and more often, rather than providing the shortest paths based on a path-finding algorithm.

(Invited) Towards First-Principles Prediction of Early SEI Formation

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 new 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 formation mechanisms of LEMC as compared to LEDC and 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. These findings furnish fundamental insights into the dynamics of SEI formation and demonstrate a path forward toward a predictive understanding of electrochemical passivation.

Reliable efficient cluster routing protocol based HTDE scheme for UWSN

<p>Underwater sensor networks (UWSNs) are recently recognized as a promising method for monitoring and exploring the underwater environment. Due to real-time remote data monitoring requirements, UWSN has become a preferred network to a large extent. But reliable and efficient secure data transmission to the receiver is one of the most important challenges for UWSNs, often suffer from irreplaceable batteries and high latency for long-distance communications. The most challenging task is extending network life, shortening transmission distances for each node. Thus, this paper presents the reliable efficient cluster routing (RECR) protocol, optimal shortest path finding (OSPF) algorithm and hill transformation data encryption (HTDE) algorithm for UWSNs. The proposed encryption algorithm encrypts the data from the source node, and the RECR protocol is used for reliable data delivery from source to destination. To extend the network's life, RECR employs autonomous underwater vehicle (AUV) from the sink node (SN) for data collection. The OSPF algorithm is used to find the shortest path for data transmission to avoid latency. The proposed RECR protocol, HTDE and OSPF algorithm enhance the secure data transmission efficiency, minimizing the energy consumption and improves the network life time. The proposed protocol decreases end-to-end latency, packet loss.</p>

Comparison of Flow Field and A-Star Algorithm for Pathfinding in Tower Defense Game

Tower Defense is a game genre that uses a pathfinding algorithm. Pathfinding is a way to find a path from one point to another; pathfinding itself has many variants with different scenarios. One of these algorithms is the A-Star algorithm, a well- known and commonly used method for game pathfinding. Another algorithm is the Flow Field algorithm, which is an algorithm that is not widely known and has become the topic in this following research. This research will be carried out by comparing the time required by both algorithms to reach the target point from starting point, and time comparison will be carried out in 3 different scenarios. The result of the research is that the Flow Field algorithm reaches the target faster than A-Star Pathfinding in every scenario carried out in the simulation. This research concludes that the Flow Field algorithm can compete with the A-Star algorithm to find paths in the Tower Defense game.

Comparison of Flow Field and A-Star Algorithm for Pathfinding in Tower Defense Game

Comparison of Flow Field and A-Star Algorithm for Pathfinding in Tower Defense Game

Piping auto-routing using key-node generation method in ships

ABSTRACT Previous studies on automatic pipe routing algorithms in ships have typically used pathfinding and optimisation algorithms to satisfy relevant constraints in the field and improve the results of pipe auto-routing. However, the existing path-search algorithm takes a long time to search for nodes that do not need to be searched. Therefore, this study was conducted to identify the key nodes worth searching for when pipes are routed in a ship and accelerate pipe routing by reducing the search nodes. Key nodes in piping routing serve to connect points that require changes in the location of the piping; after node creation, the path search is performed using the A* algorithm the algorithm in the key-node environment proposed in this study and the existing algorithm were compared and verified. Finally, the effectiveness of the proposed algorithm was verified by analyzing the performance of each parameter.

Optimal Control for Unmanned Systems With One-Way Broadcast Communication

Unmanned systems (USs) including unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles have great application prospects in military and civil fields, among which the process of finding feasible and optimal paths for the agents in USs is a kernel problem. Traditional path finding algorithms are hard to adequately obtain optimal paths in real-time under fast time-varying and poor communication environments. We propose an online optimal control algorithm for USs based on a one-way broadcast communication mode under the assumption of a poor communication environment, mobile targets, radars (or sonar), and missiles (or torpedoes). With the principle of receding horizon control, optimal (or suboptimal) paths are then generated by the approximation theory of neural networks and gradient optimization techniques, with low computation requirements. Also, we give a convergence analysis for our algorithm, and show that each agent can reach its target in finite time under some conditions on agents, targets and radar-missiles. Moreover, simulations demonstrate that the agents in USs can generate optimal (or suboptimal) paths in real time using our algorithm while effectively avoiding collision with other agents or detection by enemy radars.

INVESTIGATION OF PATHFINDING ALGORITHMS IN THE FRAMEWORK OF APPLICATION FOR LARGE-SIZED VEHICLE ASSISTANT SYSTEMS

This article is devoted to the study of the problem of performing a reverse maneuver for large-sized vehicles, namely, the path-finding algorithms used to plot the trajectory of movement depending on the type of terrain surrounding static or moving objects using sensors and means of displaying the information received from them. When analyzing the data, it was found that the most suitable option is a graphically modified algorithm A* derived from Dijkstra’s algorithm due to the speed of operation, optimization and the possibility of using realistic turns with smoothing, which is a critical factor when considering an articulated vehicle. Some problems when using the algorithm in real conditions are also considered.

Nested Bee Hive: A Conceptual Multilayer Architecture for 6G in Futuristic Sustainable Smart Cities.

Several smart city ideas are introduced to manage various problems caused by overpopulation, but the futuristic smart city is a concept based on dense and artificial-intelligence-centric cities. Thus, massive device connectivity with huge data traffic is expected in the future where communication networks are expected to provide ubiquity, high quality of service, and on-demand content for a large number of interconnected devices. The sixth-generation (6G) network is considered the problem-solving network of futuristic cities, with huge bandwidth and low latency. The expected 6G of the radio access network is based on terahertz (THz) waves with the capability of carrying up to one terabit per second (Tbps). THz waves have the capability of carrying a large amount of data but these waves have several drawbacks, such as short-range and atmospheric attenuation. Hence, these problems can introduce complications and hamper the performance of the 6G network. This study envisions futuristic smart cities using 6G and proposes a conceptual terrestrial network (TN) architecture for 6G. The nested Bee Hive is a scalable multilayer architecture designed to meet the needs of futuristic smart cities. Moreover, we designed the multilayer network infrastructure while considering the expectations from a network of futuristic smart cities and the complications of THz waves. Extensive simulations are performed using different pathfinding algorithms in the 3D multilayer domain to evaluate the performance of the proposed architecture and set the dynamics of futuristic communication of 6G.

Weighted octree-based 3D indoor pathfinding for multiple locomotion types

Accurate 3D pathfinding for multiple locomotion types is crucial for indoor applications. Many of the existing methods rely on the correct topology or the rich semantics of the indoor space, which restricts their usage in unstructured and semantic-less environments. Though space-decomposition methods, such as voxelization, have been applied to support 3D pathfinding, two major defects remain: the first is that voxelization of a coarse resolution brings about discretization artifacts, which is detrimental to the accessibility of wheeled platforms. The second is that the movement behaviors of different locomotion types were not modeled and integrated with a consistent world representation. This paper employs the octree to reconstruct the indoor spaces in multiresolution, especially to differentiate between slopes and steps. A height-related spatial weighting is proposed for modeling the 3D movement cost. The behaviors of typical locomotion types are modeled by the variable-length search in continuous coordinate space and compose a unified hybrid A* pathfinding algorithm. Experiments show this method is robust and can be applied to both structured and unstructured building models. The results are accurate 3D paths conforming to the movement behaviors of multiple locomotion types.

Flexible networked rural electrification using levelized interpolative genetic algorithm

Networked rural electrification is an alternative approach to accelerate rural electrification. Using satellite photos and GIS tools, an electrical distribution network is used to connect villages and properly located generation facilities together to reduce electrification cost. To design the network, optimal paths connecting all node-pairs are identified, followed by finding a network topology that minimizes cost. Earlier work has illustrated that A* (A-star, an optimal path-finding algorithm) is inefficient for this application due to the complex topography in rural areas. The multiplier-accelerated A* (MAA*) algorithm overcomes key performance issues, but, like A*, produces only one path connecting each node-pair. Relying on one path increases project risk because adverse conditions, such as inaccurate GIS estimation, unexpected soil conditions, land-rights disputes, political issues, etc. can occur during implementation. In this paper, a hybrid path-finding method combining genetic algorithm and A* / MAA* algorithm is proposed. The proposed method provides a family of near-optimal paths instead of a single optimal path for routing. A family of paths allows a project implementer to quickly adapt to unexpected situations as new information becomes available, and flexibly change network topology before or during implementation with minimal impact on project cost.

Which MAPF Model Works Best for Automated Warehousing?

Multi-Agent Path Finding (MAPF) algorithms and their variants can find high-quality collision-free plans for automated warehousing under simplified assumptions about the robot dynamics. However, these simplifying assumptions pose challenging implementational issues as the robots cannot follow the plans precisely. Various robust execution frameworks, such as the Action Dependency Graph (ADG) framework, have been proposed to enable the real-world execution of MAPF plans. Under such a framework, it is unclear how the simplifying assumptions affect the performance of the robots. In this work, we first argue that the ADG framework provides the same robustness guarantees as the single-agent framework (where plans are generated independently for each robot and collisions are avoided through a reservation table), which is widely used in industry. We then improve the efficiency of the ADG framework by integrating it with the Rolling-Horizon Collision-Resolution framework to solve MAPF problems with a persistent stream of online tasks. Using the integrated framework, we compare the standard MAPF model with many of its more complex variants, such as MAPF with rotation, k-robust MAPF, and continuous-time MAPF (taking robot dynamics into account). We examine their effectiveness in improving throughput through realistic simulations of warehouse settings with the Gazebo simulator.

Benchmarks for Pathfinding Search: Iron Harvest

Pathfinding is a central topic in AI for games, with many approaches having been suggested. But comparing different algorithms is tricky, because design choices stem from different practical considerations; e.g., some pathfinding systems are grid-based, others rely on a navigation mesh or visibility graph and so on. Current benchmarks mirror this trend, focusing on one set of assumptions while ignoring the rest. In this work we present a new unified benchmark using data from the game Iron Harvest. For 35 different levels in the game we generate several complementary map representations (grid, mesh and obstacle-set) and we provide a common set of challenging instances. We describe and analyse the new benchmark and then compare several leading pathfinding algorithms that begin from different assumption sets. Our goal is to allow researchers and practitioners to better understand the relative strengths and weakness of competing techniques.

On Merging Agents in Multi-Agent Pathfinding Algorithms

In Multi-Agent Pathfinding (MAPF), the task is to find non-colliding paths for a set of agents. This paper focuses on search-based MAPF algorithms from the Conflict-Based Framework, which is introduced here. A common technique in such algorithms is to merge a group of dependent agents into a meta-agent and plan non-colliding paths for the meta-agent using a low-level MAPF sub-solver. We analyze the patterns that emerge when agents are merged in an arbitrary order. We then introduce policies for choosing which agents or meta-agents to merge to achieve improved efficiency in three algorithms: Independence Detection (ID) and Improved Conflict-Based Search (ICBS), which are optimal, and Priority-Based Search (PBS), which is a fast suboptimal algorithm. Experimental results show a significant improvement in efficiency

Pathfinding method for an indoor drone based on a BIM-semantic model

The application of the drone in construction monitoring, anti-terrorism investigation, and emergency response in large buildings is the development trend of indoor navigation in the future. At present, the indoor three-dimensional (3D) map model based on Building Information Modeling (BIM) is mainly used for indoor pathfinding for pedestrians and ground mobile devices. However, it lacks the spatial description of the vertical dimension, which cannot meet the needs of the navigation application of an indoor drone. Therefore, we propose a pathfinding method for an indoor drone based on a BIM-semantic model. First, the semantic and geometric information in the BIM model is extracted and mapped to voxels to generate an indoor 3D map model called BI3DM. The model subdivides voxel types, which are suitable for subsequent pathfinding for a drone and even pedestrian. Then, a BI3DM-based pathfinding algorithm for an indoor drone is proposed. The algorithm optimizes the path for starting and target locations near the ground, and imports the drone size into the algorithm, which can obtain a more reasonable path. Finally, three types of BIM models are experimented with to verify the effectiveness of the study and its compatibility with pedestrians and ground mobile devices.

Global trajectory optimization, pathfinding, and scheduling for a multi-flyby, multi-spacecraft mission

The UT Austin team presents their methodology and result for GTOC11, constructing a hypothetical Dyson ring using asteroids encountered by 10 motherships departing from Earth. A pathfinding algorithm for the mothership is designed using a fast lookup table and a robust Lambert solver. Sequencing of unique mothership itineraries is performed by approximating the mass delivered to the ring via a linear data-based estimator trained on simulated asteroid-to-ring trajectories. A detailed derivation for the necessary boundary conditions of the constant-thrust-acceleration indirect optimization problem is presented, with key insights about the magnitudes of the co-states. An indirect boundary value solver finds feasible asteroid-to-ring trajectories for each of the 12 phasing locations in a high-performance parallel computing environment. A genetic algorithm informs the sequencing for the ring station arrival order that gives the highest performance index value. UT Austin’s final submitted solution placed 4th in the competition, with a performance index value of 5885.5, 235 asteroids collected, and a minimum build-station mass of 1.1328×1015 kg.

Ship Navigation and Fuel Profiling based on Noon Report using Neural Network Generative Modeling

Harbor craft historical routes contain valuable information on how the experienced crews navigate around the known waters while performing jobs. The noon report logs each job timeframe which can be used to segregate the time-series positional data as routes. Other information from the noon report such as fuel consumption could be associated with a particular job as well. This paper offers a solution to encompass crew navigational experience into neural network models. The variational autoencoder, which is a generative model, can capture the routes into a knowledge base model. The same variational autoencoder is also able to train other neural networks to make predictions of route and fuel consumption based on job metadata (I.e., job duration, activity area, and route classification). The predicted routes could be used as a cost map for pathfinding algorithms such as A* or Dijkstra.

Robotic Delivery System for Multi-Storey Condominium Complexes

In certain environments like condominium complexes, couriers are only allowed to drop off their payloads at designated drop-off stations instead of delivering it directly to the recipient. These regulations have become increasingly popular in condominiums during the Covid-19 pandemic, in part due to government SOPs encouraging their adoption. A partially automated delivery system using robot couriers that deliver the payloads from the drop-off stations directly to the recipient is developed. The system is made up of a central PostgreSQL database, a Webots simulated delivery robot and two Android applications for interfacing with the system. Two individuals operate the system: the system operator and the resident requesting delivery. The resident sends their delivery requests using the Resident android application. The system operator acknowledges the delivery requests, then loads the robot and configures it to carry the package(s) up to the resident’s housing unit through the Operator android application. Navigation and obstacle avoidance are achieved through usage of the A* pathfinding algorithm. To receive the package(s) once the robot arrives, a QR code enabled compartment on the robot is unlocked using the Resident android application once again. The PostgreSQL database and Advantech WISE-PaaS Dashboard are used to display delivery requests, residents’ data and robot status for system operators. NodeRED and MQTT are used to facilitate communications between robot, applications and database. In the end, the simulated robot is able to navigate a virtual environment with minimal faults and communicate properly with the applications and database. Robot speed is set at 0.27 ms-1 when empty, and maximum carrying weight is tested to be 20.5 kg with a delivery time of 1549 seconds for the arbitrary simulated delivery route.

Multi-Agent Path Finding with Temporal Jump Point Search

Temporal Jump Point Search (JPST) is a recently introduced algorithm for grid-optimal pathfinding among dynamic temporal obstacles. In this work we consider JPST as a low-level planner in Multi-Agent Path Finding (MAPF). We investigate how the canonical ordering of JPST can negatively impact MAPF performance and we consider several strategies which allow us to overcome these limitations. Experiments show our new CBS/JPST approach can substantially improve on CBS/SIPP, a contemporary and leading method from the area.

Distributed representation learning and intelligent retrieval of knowledge concepts for conceptual design

Data-driven conceptual design is rapidly emerging as a powerful approach to generate novel and meaningful ideas by leveraging external knowledge especially in the early design phase. Currently, most existing studies focus on the identification and exploration of design knowledge by either using common-sense or building specific-domain ontology databases and semantic networks. However, the overwhelming majority of engineering knowledge is published as highly unstructured and heterogeneous texts, which presents two main challenges for modern conceptual design: (a) how to capture the highly contextual and complex knowledge relationships, (b) how to efficiently retrieve of meaningful and valuable implicit knowledge associations. To this end, in this work, we propose a new data-driven conceptual design approach to represent and retrieve cross-domain knowledge concepts for enhancing design ideation. Specifically, this methodology is divided into three parts. Firstly, engineering design knowledge from the massive body of scientific literature is efficiently learned as information-dense word embeddings, which can encode complex and diverse engineering knowledge concepts into a common distributed vector space. Secondly, we develop a novel semantic association metric to effectively quantify the strength of both explicit and implicit knowledge associations, which further guides the construction of a novel large-scale design knowledge semantic network (DKSN). The resulting DKSN can structure cross-domain engineering knowledge concepts into a weighted directed graph with interconnected nodes. Thirdly, to automatically explore both explicit and implicit knowledge associations of design queries, we further establish an intelligent retrieval framework by applying pathfinding algorithms on the DKSN. Next, the validation results on three benchmarks MTURK-771, TTR and MDEH demonstrate that our constructed DKSN can represent and associate engineering knowledge concepts better than existing state-of-the-art semantic networks. Eventually, two case studies show the effectiveness and practicality of our proposed approach in the real-world engineering conceptual design.