Cost Path Research Articles

Solving an industry-inspired generalization of lifelong MAPF problem including multiple delivery locations

In this study, we worked on a new industry-inspired generalization of the multi-agent path finding (MAPF) problem. In this new problem, incoming jobs should be assigned to agents in a way that minimizes the total path cost. In this problem, definition of a job is visiting a certain coordinate by the agent. In addition, the agents must keep the following tasks on their own lists and decide the order of visiting the tasks assigned to them by minimizing the total distance traveled. This new structure can be applied to many real-world problems like cleaning an area with autonomous robots, automated warehouses and area protection with autonomous robots. In solving this problem, three different problems must be solved; to which agent a new job will be assigned, in what order the agents will handle that job, and finally, how agents will act without colliding with each other when the agents have multiple destinations. This problem is different from the job-distribution problems in the MAPF literature since the optimization criterion is total path cost and agents have to decide the order of the jobs to be visited. For this reason, new job-distribution methods that meet these goals are presented in this study. Five new heuristic algorithms that produce fast solutions for job-distribution are presented. Furthermore, we presented one brute force algorithm to find the optimal path-cost solution and compare with the heuristics created. Experiments conducted on benchmark maps have demonstrated that the job-distribution algorithm yielding the best performance resulted in path costs that were at most three to three and a half percent higher compared to the brute force approach. Additionally, the heuristics developed were found to operate at least ten times faster in terms of processing time when compared to the brute force approach. The CBS-D*-lite algorithm was previously presented in the literature and shown to produce good results in incremental environments. With these modifications, agents can have multiple destinations and collisions between the agent paths are avoided.

Follow the shade: detection of optimally shaded pedestrian paths within the historic city center of Thessaloniki

Both the EU Green Deal and the UN SDGs consider the transition to sustainable urban mobility a key priority. Designing cities and public spaces to accommodate pedestrians and not cars has been a core strategy of sustainable urban mobility since the 1990s. Yet, the quality of pedestrian traffic is highly dependent on outdoor meteorological conditions which may encourage or discourage walking. The ongoing global climate change and the local Urban Heat Island (UHI) effect have a combined adverse effect on pedestrian heat stress and associated health risks. Increased heat stress is a significant disincentive to walking and can threaten urban life, especially during summer in hot climates, such as the Mediterranean. Humans have the capacity to adapt to hot weather through physiological responses and changes in clothing, activity and behavior. Pedestrians walking during a hot day will likely pick a comfortable route, i.e. one that it is shaded and easily traversable. This is identified visually at the street level, which makes planning longer comfortable routes in the urban environment a challenging task. The paper presents an easily reproducible methodology for generating optimal pedestrian paths with respect to shading and terrain slope. The methodology is applied on the historic city center of Thessaloniki, Greece. The study utilizes QGIS and the UMEP plugin to generate time series of urban shadow raster data and then calculate the least cost path from any starting point to any destination within the city center. The results are then compared to routes generated by conventional shortest path algorithms.

Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

Today, the importance of enhanced quality of service and energy optimization has promoted research into sensor applications such as pervasive health monitoring, distributed computing, etc. In general, the resulting sensor data are stored on the cloud server for future processing. For this purpose, recently, the use of fog computing from a real-world perspective has emerged, utilizing end-user nodes and neighboring edge devices to perform computation and communication. This paper aims to develop a quality-of-service-based energy optimization (QoS-EO) scheme for the wireless sensor environments deployed in fog computing. The fog nodes deployed in specific geographical areas cover the sensor activity performed in those areas. The logical situation of the entire system is informed by the fog nodes, as portrayed. The implemented techniques enable services in a fog-collaborated WSN environment. Thus, the proposed scheme performs quality-of-service placement and optimizes the network energy. The results show a maximum turnaround time of 8 ms, a minimum turnaround time of 1 ms, and an average turnaround time of 3 ms. The costs that were calculated indicate that as the number of iterations increases, the path cost value decreases, demonstrating the efficacy of the proposed technique. The CPU execution delay was reduced to a minimum of 0.06 s. In comparison, the proposed QoS-EO scheme has a lower network usage of 611,643.3 and a lower execution cost of 83,142.2. Thus, the results show the best cost estimation, reliability, and performance of data transfer in a short time, showing a high level of network availability, throughput, and performance guarantee.

Conflict-tolerant and conflict-free multi-agent meeting

In the Multi-Agent Meeting problem (MAM), the task is to find the optimal meeting location for multiple agents, as well as a path for each agent to that location. Among all possible meeting locations, the optimal meeting location has the minimum cost according to a given cost function. Two cost functions are considered in this research: (1) the sum of all agents paths' costs to the meeting location (SOC) and (2) the cost of the longest path among them (MKSP). MAM has many real-life applications, such as choosing a gathering point for multiple traveling agents (humans, cars, or robots).In this paper, we divide MAM into two variants. In its basic version, MAM allows multiple agents to occupy the same location, i.e., it is conflict tolerant. For MAM, we introduce MM*, a Multi-Directional Heuristic Search algorithm, that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) bidirectional search algorithm to the case of finding an optimal meeting location for multiple agents. Several admissible heuristics are proposed for MM*, and experiments demonstrate the benefits of MM*.As agents may be embodied in the world, a solution to MAM may contain conflicting paths, where more than one agent occupies the same location at the same time. The second variant of the MAM problem is called Conflict-Free Multi-Agent Meeting (CF-MAM), where the task is to find the optimal meeting location for multiple agents (as in MAM) as well as conflict-free paths (in the same manner as the prominent Multi-Agent Path Finding problem (MAPF)) to that location. For optimally solving CF-MAM, we introduce two novel algorithms, which combine MAM and MAPF solvers. We prove the optimality of both algorithms and compare them experimentally, showing the pros and cons of each algorithm.1

Cost-efficient forest management for safeguarding Siberian flying squirrel (Pteromys volans) habitats in Central Finland

ABSTRACT Protection of vulnerable species often creates conflicts between land use and conservation management. Particularly challenging is the case of Siberian flying squirrel (SFS; Pteromys volans). SFS favours mature forest habitats, which are often the target for logging, and therefore its protection causes opportunity costs. In our analysis a regional case study was applied as a platform to create alternative forest management scenarios. These scenarios aimed to maintain and improve SFS habitats with varying magnitudes, from no action in SFS habitat improvement to increasing the amount of suitable habitat for SFS. Stand projections for each forest management scenario were modelled with the Motti stand simulator, and the simulated stand structures were further analyzed using specific indexes with Geographic Information System (GIS) methodologies and tools to predict potential nesting and feeding habitats and connectivity for SFS. Connectivity between habitats was assessed with a Least Cost Path analysis. The results showed that some forest management scenarios were more cost-efficient than others in maintaining habitat suitability and connectivity for SFS. Further, with adjusted cutting removals (due to restrictions other than SFS habitat related, mainly recreation) an additional hectare suitable for SFS habitat was considerably more cost-efficient than without the adjustment.

Exchange rates and multicommodity international trade: insights from spatial price equilibrium modeling with policy instruments via variational inequalities.

In this paper, we construct a multicommodity international trade spatial price equilibrium model of special relevance to agriculture in which exchange rates are included along with policy instruments in the form of tariffs, subsidies as well as quotas. The model allows for multiple trade routes between country origin nodes and country destination nodes and these trade routes can include different modes of transportation and transport through distinct countries. We capture the impacts of exchange rates through the definition of effective path costs and identify the governing multicommodity international trade spatial price equilibrium conditions, which are then formulated as a variational inequality problem in product path flows. Existence results are established and a computational procedure presented. The illustrative numerical examples and a case study are inspired by the impacts of the war against Ukraine on agricultural trade flows and product prices. The modeling and algorithmic framework allows for the quantification of the impacts of exchange rates and various trade policies, as well as the addition or deletion of supply markets, demand markets and/or routes, on supply and demand market prices in local currencies, and on the volume of product trade flows with implications for food security.

Retrospective-Based Deep Q-Learning Method for Autonomous Pathfinding in Three-Dimensional Curved Surface Terrain

Path planning in complex environments remains a challenging task for unmanned vehicles. In this paper, we propose a decoupled path-planning algorithm with the help of a deep reinforcement learning algorithm that separates the evaluation of paths from the planning algorithm to facilitate unmanned vehicles in real-time consideration of environmental factors. We use a 3D surface map to represent the path cost, where the elevation information represents the integrated cost. The peaks function simulates the path cost, which is processed and used as the algorithm’s input. Furthermore, we improved the double deep Q-learning algorithm (DDQL), called retrospective-double DDQL (R-DDQL), to improve the algorithm’s performance. R-DDQL utilizes global information and incorporates a retrospective mechanism that employs fuzzy logic to evaluate the quality of selected actions and identify better states for inclusion in the memory. Our simulation studies show that the proposed R-DDQL algorithm has better training speed and stability compared to the deep Q-learning algorithm and double deep Q-learning algorithm. We demonstrate the effectiveness of the R-DDQL algorithm under both static and dynamic tasks.

Identification of Urban Clusters Based on Multisource Data—An Example of Three Major Urban Agglomerations in China

Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies.

CCPF-RRT*: An improved path planning algorithm with consideration of congestion

Path planning is essential for robots to efficiently execute jobs in challenging settings. In this paper, we propose a new algorithm, called potential function-based sampling heuristic optimal path planning considering congestion (CCPF-RRT*), extending the standard Rapidly-exploring Random Tree star (RRT*) to address the issues of slow convergence, high path cost, and how to plan suitable paths in the presence of congested regions in the environment. First, the CCPF-RRT* algorithm adds the artificial potential field to RRT*, which can drastically cut down on the number of iterations and speed up convergence. Second, a movement cost function that takes congestion intensity and path length into account when evaluating a connection between two path nodes is created. This function is then used to direct the growth of new nodes and the updating of parent nodes so that the algorithm takes congestion intensity and path length into account when generating paths. Third, in order to reduce the path cost, the algorithm establishes parent nodes for random nodes by combining the benefits of the F-RRT* algorithm. The movement cost function is added to the process of establishing parent nodes to optimize the path cost in order to prevent the creation of parent nodes that force the path to traverse through crowded areas. In experimental simulation results, the algorithm outperforms RRT*, Q-RRT*, PQ-RRT*, and F-RRT* not only in terms of initial solution and quick convergence speed, but is also able to design an ideal path with the lowest movement cost in a crowded environment using the movement cost function.

GIS‐enabled historiography to determine travel routes during the Western Han period via agent‐based models and least‐cost path analysis

AbstractGIS‐enabled historiography allows us to shed light on missing or poorly understood aspects of historical events. Here, we use agent‐based models (ABMs), least cost path analysis (LCPA), and space–time paths to recreate and evaluate possible modes and routes of travel undertaken by Shi Rao, a Western Han official. A diary was found in Shi Rao's tomb that includes information about his travels over 1 year (11 BCE), including start and end times of his journeys. But it leaves out details regarding modes of transport or exact routes. Using Tan Qixiang's historical atlas, we digitized river networks and utilized modern topographic data to digitally recreate the landscape as it was during Shi Rao's travels. This was then used to evaluate possible journeys via rivers (using ABMs) and roads (using LCPA), and compared to historical speed of boats, carts and horses, with findings indicating that horseback may have been the viable option.

UGV Parking Planning Based on Swarm Optimization and Improved CBS in High-Density Scenarios for Innovative Urban Mobility

The existence of information silos between vehicles and parking lots means that Unmanned Ground Vehicles (UGVs) repeatedly drive to seek available parking slots, resulting in wasted resources, time consumption and traffic congestion, especially in high-density parking scenarios. To address this problem, a novel UGV parking planning method is proposed in this paper, which consists of cooperative path planning, conflict resolution strategy, and optimal parking slot allocation, intending to avoid ineffective parking seeking by vehicles and releasing urban traffic pressure. Firstly, the parking lot induction model was established and the IACA–IA was developed for optimal parking allocation. The IACA–IA was generated using the improved ant colony algorithm (IACA) and immunity algorithm. Compared with the first-come-first-served algorithm (FCFS), the normal ant colony algorithm (NACA), and the immunity algorithm (IA), the IACA–IA was able to allocate optimal slots at a lower cost and in less time in complex scenarios with multi-entrance parking lots. Secondly, an improved conflict-based search algorithm (ICBS) was designed to efficiently resolve the conflict of simultaneous path planning for UGVs. The dual-layer objective expansion strategy is the core of the ICBS, which takes the total path cost of UGVs in the extended constraint tree as the first layer objective, and the optimal driving characteristics of a single UGV as the second layer objective. Finally, three kinds of load-balancing and unbalanced parking scenarios were constructed to test the proposed method, and the performance of the algorithm was demonstrated from three aspects, including computation, quality and timeliness. The results show that the proposed method requires less computation, has higher path quality, and is less time-consuming in high-density scenarios, which provide a reasonable and efficient solution for innovative urban mobility.

Efficient hybrid enhanced genetic algorithm and ant colony system model for rerouting multimedia message in multiple node–link failures within wireless network

SummaryFailure‐resilient wireless networks have attracted the interest of the research community and have been an open area of concern in the studies of wireless network in recent years. Accordingly, different research on restoration techniques have been carried out to proffer solution to the network component failures. In the same vein, this article proposes a hybridized enhanced genetic algorithm and ant colony system (EGAACS) survivability model, which can instantly resolve node–link failure problems, thus improving the quality of service of the wireless network. The EGAACS is a hybrid model that combines the principles of the enhanced genetic algorithm (EGA) and ant colony system (ACS) models to form a capacity efficiency solution that outperforms the ACS and EGA models in terms of path cost and transmission delay. The resilience of this proposed EGAACS model is verified for different wireless network sizes (20, 30, 40, and 50 node networks). Simulation results show that the proposed EGAACS model generates the best close to optimal paths in terms of the path cost and transmission delay in comparison to the EGA and ACS models. In fact, the performance of the proposed EGAACS model is more conspicuous as the size of the network increases. More importantly, the proposed EGAACS model is suitable for real‐time wireless network as it exhibits moderate computational time complexity.

Quality-Aware Autonomous Navigation with Dynamic Path Cost for Vision-Based Mapping toward Drone Landing

This article presents a novel autonomous navigation approach that is capable of increasing map exploration and accuracy while minimizing the distance traveled for autonomous drone landings. For terrain mapping, a probabilistic sparse elevation map is proposed to represent measurement accuracy and enable the increasing of map quality by continuously applying new measurements with Bayes inference. For exploration, the Quality-Aware Best View (QABV) planner is proposed for autonomous navigation with a dual focus: map exploration and quality. Generated paths allow for visiting viewpoints that provide new measurements for exploring the proposed map and increasing its quality. To reduce the distance traveled, we handle the path-cost information in the framework of control theory to dynamically adjust the path cost of visiting a viewpoint. The proposed methods handle the QABV planner as a system to be controlled and regulate the information contribution of the generated paths. As a result, the path cost is increased to reduce the distance traveled or decreased to escape from a low-information area and avoid getting stuck. The usefulness of the proposed mapping and exploration approach is evaluated in detailed simulation studies including a real-world scenario for a packet delivery drone.

Meta-Routing Paradigm for Robotic Ad-hoc Networks

With the increasing use of robotic networks, communication issues such as maintaining connections between nodes are becoming more prevalent. While previous routing protocols for wireless networks have been developed, they tend to address routing and link maintenance separately. Consequently, the separation leads to increased costs and delays in network communication. Existing routing protocols typically focus on discovering links, connecting them, finding the most efficient path, and reducing costs associated with the path. However, their limitations have led to the development of a new routing mechanism for robotic networks called Meta-Routing. Meta-Routing builds on existing routing protocols by incorporating regular routing of packets and maintenance of links in mobile agent environments. This approach aims to improve efficiency and reduce routing and link maintenance costs. In addition, meta-Routing seeks to minimize communication path costs and the overhead cost associated with discovering a route, repairing a link, or creating a new communication path among nodes. This paper presents a method for achieving Meta-Routing by controlling robot motion based on recognizing the radio frequency (RF) environment through Hidden Markov Models (HMMs) and gradient descent methods. Simulation results show that Meta-Routing, based on controlling individual robot motion, can provide self-healing capabilities in mobile robot networks, decrease network latency, and improve network performance.

NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion.

This paper is concerned with the issue of path optimization for manipulators in multi-obstacle environments. Aimed at overcoming the deficiencies of the sampling-based path planning algorithm with high path curvature and low safety margin, a path optimization method, named NA-OR, is proposed for manipulators, where the NA (node attraction) and OR (obstacle repulsion) functions are developed to refine the path by iterations. In the iterations of path optimization, the node attraction function is designed to pull the path nodes toward the center of their neighbor nodes, thereby reducing the path curvature and improving the smoothness. Also, the obstacle repulsion function is developed to push the path nodes out of the potentially unsafe region by generating a repulsive torque on the path nodes, thus improving the safety margin of the motion. By introducing the effect of NA-OR, the optimized path has a significant improvement in path curvature and safety margin compared with the initial path planned by Bi-RRT, which meaningfully enhances the operation ability of manipulators for the applications that give a strong emphasis on security. Experimental results on a 6-DOF manipulator in 4 scenarios demonstrate the effectiveness and superiority of the proposed method in terms of the path cost, safety margin, and path smoothness.

Multi-objective route recommendation method based on Q-learning algorithm

With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q-value table search mechanism, the proposed algorithm can generate the recommended route almost in real time.

Solving the hierarchical windy postman problem with variable service costs using a math-heuristic algorithm

The Hierarchical Windy Postman Problem (HWPP) is an arc routing problem in which an order relation is imposed on the arcs/edges of the graph, and one has to pass through each edge at least once while adhering to the hierarchical priority relations. The tour starts from and ends at a specific node and the aim is to minimize the length of the tour. We consider a variant of the HWPP in which (i) the precedence order of the edge hierarchies is linear and edges within each hierarchy are connected and (ii) the cost of serving each edge decreases with the number of times it is traversed, and we refer to it as HWPP with variable service costs. An integer non-heuristic linear mathematical formulation is proposed, and a solution approach is designed. Our solution heuristic adapts the layer algorithm of Dror et al. (Networks 17:283–294, 1987) but employs an integer mathematical formulation as a sub-procedure instead of the blossom algorithm to find the least cost path between the nodes of the graph. This choice is based on the fact that the blossom algorithm requires a symmetric cost structure while we deal here with the general case of asymmetric cost structure, which makes our problem a windy variant of the postman problem. It should be noted that our problem is not asymmetric in the sense that there are no opposite arcs with different costs but there are edges which have different costs depending on the traversal direction. In order to compare the performance of our heuristic algorithm with respect to the performance of the mathematical model that is solved by the commercial solver Gurobi, 84 test instances are generated having varying sizes and densities and with different number of hierarchies. These test instances are solved by both methods and the generated results show that the proposed heuristic method is much faster and generates better quality solutions.

Cooperative Task Allocation Algorithm of Loitering Munition Based on Exchange Contract

Aiming at the task allocation of loitering munitions, a cooperative task allocation algorithm based on exchange contract is designed considering the target value, threat cost and battle path cost. Compared with the traditional contract net algorithm, its complexity is simplified, the calculation speeds up, and the local optimal problem is solved.

Parallelized Control-Aware Motion Planning With Learned Controller Proxies

Kinodynamic motion planning enables autonomous robots to find efficient paths while minimizing energy expenditure and avoiding hazards in the environment. However, during plan execution, the controller may deviate from the collision-free path found by the planner due to discrepancies between planning and control, causing inaccurate estimation of path costs and potentially collisions with obstacles. While this can be mitigated by incorporating the vehicle controller into planning, these approaches are generally bottlenecked by the high computation cost of simulating the vehicle dynamics and controller. This letter presents the Parallel Closed-Loop RRT* motion planner that uses a fast neural network controller as a substitute for a computationally-demanding controller during planning. Using a neural network controller and parallelizing the planning process makes closed-loop planning tractable for vehicles with nonlinear dynamics and significantly reduces planning time. Experiments on a simulated underwater vehicle with a model predictive controller demonstrate that our approach yields feasible plans that are more likely to be successfully executed without collisions compared to planners that do not consider the controller.

Improved Optimization Strategy Based on Region Division for Collaborative Multi-Agent Coverage Path Planning

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more "cohesive", thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase.