Path Conflicts Research Articles

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.

Multi-Robot Navigation System Design Based on Proximal Policy Optimization Algorithm

The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to provide an innovative and effective method for global path planning of multi-robot navigation. It can plan paths with fewer path conflicts for all robots so that the overall navigation time for the robots to complete all tasks can be reduced. Compared with existing methods of global path planning for multi-robot navigation, this study proposes new perspectives and methods. It emphasizes reducing the number of path conflicts first to reduce the overall navigation time. The system consists of a localization unit, an environment map unit, a path planning unit, and an environment monitoring unit, which provides functions for calculating robot coordinates, generating preselected paths, selecting optimal path combinations, robot navigation, and environment monitoring. We use topological maps to simplify the map representation for multi-robot path planning so that the proposed method can perform path planning for more robots in more complex environments. The proximal policy optimization (PPO) is used as the algorithm for deep reinforcement learning. This study combines the path selection method of deep reinforcement learning with the A* algorithm, which effectively reduces the number of path conflicts in multi-robot path planning and improves the overall navigation time. In addition, we used the reciprocal velocity obstacles algorithm for local path planning in the robot, combined with the proposed global path planning method, to achieve complete and effective multi-robot navigation. Some simulation results in NVIDIA Isaac Sim show that for 1000 multi-robot navigation tasks, the maximum number of path conflicts that can be reduced is 60,375 under nine simulation conditions.

A hierarchical solution framework for dynamic and conflict-free AGV scheduling in an automated container terminal

Container terminals worldwide are experiencing their transitions into automated and intelligent terminals in the face of the ever increasing container handling demand and cost pressure. A key to cost-effective operations in automated container terminals is the efficient AGV scheduling algorithm that enables on-time fulfillment of container loading and discharging tasks. In this paper, we study an integrated task assignment and path planning problem for AGV scheduling in an automated container terminal. We propose a hierarchical solution framework to empower dynamic AGV scheduling, where the higher level employs a reinforcement learning algorithm for dynamic task assignment and the lower level makes use of a tailored path generation algorithm to generate low-cost and conflict-free paths for AGVs to serve the tasks. Additionally, we propose a container matching heuristic and a two-layer grid map to enhance the learning ability of the reinforcement learning algorithm. We compare the performance of the hierarchical solution framework against various benchmark methods on problem instances of practical scales. The results show that our approach is effective in reducing task delays and mitigating path conflicts, making the task assignment and path planning decisions more applicable for AGV scheduling in an automated container terminal.

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

PurposeThis study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.Design/methodology/approachIn this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.FindingsThe experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.Originality/valueIn this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

Research on Path Planning of Handling Robot in Chinese Medicine Factory

The path planning of handling robot in Chinese medicine factory is the key problem to improve production efficiency and reduce labor cost. In view of the complex layout characteristics in the handling process of Chinese medicine plant, this paper studies and improves the existing path planning algorithm. Firstly, through the analysis of the production environment of the Chinese medicine factory, a suitable map model was established. Then, combined with the operation environment of the handling robot in the Chinese medicine factory, the handling robot has A large number of nodes and many kinds of paths. The improved A* algorithm is used to plan the moving path of the robot. The effectiveness and practicability of the proposed path planning algorithm are verified by Matlab simulation test. Finally, the path conflict problem is solved.

Scheduling of Multi-AGV Systems in Automated Electricity Meter Verification Workshops Based on an Improved Snake Optimization Algorithm

Automated guided vehicles (AGVs) are one of the core technologies for building unmanned autonomous integrated automated electric meter verification workshops in metrology centers. However, complex obstacles on the verification lines, frequent AGV charging, and multi-AGV collaboration make the scheduling problem more complicated. Aiming at the characteristics and constraints of AGV transportation scheduling for metrology verification, a multi-AGV scheduling model was established to minimize the maximum completion time and charging cost, integrating collision-avoidance constraints. An improved snake optimization algorithm was proposed that first assigns and sorts tasks based on AGV-order-address three-level mapping encoding and decoding, then searches optimal paths using an improved A* algorithm solves multi-AGV path conflicts, and finally finds the minimum-charging-cost schedule through large neighborhood search. We conducted simulations using real data, and the calculated results reduced the objective function value by 16.4% compared to the traditional first-in-first-out (FIFO) method. It also reduced the number of charges by 60.3%. In addition, the proposed algorithm is compared with a variety of cutting-edge algorithms and the results show that the objective function value is reduced by 8.7–11.2%, which verifies the superiority of the proposed algorithm and the feasibility of the model.

A Study of the Global Topological Map Construction Algorithm Based on Grid Map Representation for Multirobot

In some large environments where humans and machines coexist, such as smart factories, restaurants, and hotels, multiple mobile robots system have certain advantages over single mobile robot in terms of task complexity, execution efficiency, and system robustness. However, path conflicts and blockages may occur among robots without effective global coordination method, especially in some special areas like narrow straight roads (N-S-R) and crossroads (C-R). To solve this problem, using the global topology map might be a good solution. Therefore, this paper proposes a method called Surplus Topology Generation Algorithm (<i>STGA</i>) for constructing a global topological map based on grid map representation. On the basis of grid maps, we designed <i>Node-Filter</i> to generate enough topological nodes, and simplified the node set by <i>Node-Selection</i>. In special areas such as N-S-R and C-R, which are prone to path conflicts and blocking scenarios, we extracted the Harris corner point and combined them to select the corner point combinations that characterize some special features like passage entrance, etc. Then, feature nodes that can characterize the special features were obtained by establishing mapping relationships between nodes and corner point combinations. Finally, the global topology map was obtained after establishing the reachability relationships between nodes. The simulation experiment results showed that the topological maps generated by <i>STGA</i> had obvious advantages in the richness of topological relationships. Besides, the topological maps also showed certain advantages compared to grid maps for the planning efficiency and security of global paths. Furthermore, the rich topological nodes on the global topological map were feasible in solving path conflicts of multiple robots. <i>Note to Practitioners</i>&#x2014;Multi-robot system has better efficiency and system robustness, but how to solve the path conflicts among robots is a problem. We propose a method to solve multi-robot conflicts with global topological maps. This topological map is constructed by STGA. The grid map environment is discretized into a set of topological nodes, which have been used to maintain the spatial transitions of each robot in the environment. When robots encounter unavoidable path conflicts, they are scheduled with the help of topological nodes to achieve orderly and efficient collaboration.

Collaborative Planning Method for Flexible Production Workshop Equipment and AGV Trolley Based on Artificial Intelligence Algorithms

&lt;p&gt;This article proposes a multi-objective function that includes AGV running time, production workshop energy consumption, and machine running efficiency, in response to the problems of path conflicts, single planning objectives, and isolation of planning stages in the current flexible production workshop AGV car planning. Then, the flying mouse algorithm is used to solve the problem using multiple functions. In order to avoid falling into local optima during the solving process, a simulated annealing strategy is incorporated into the flying mouse algorithm. Finally, taking the production of new energy vehicle on-board batteries as an example, a collaborative planning analysis was conducted using the method proposed in this paper. The results showed that the algorithm proposed in this paper can save 30% of running time and improve machine operating efficiency by 22.7%. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Decentralized local energy trading with cooperative energy routing in energy local area network

Local energy trading is envisioned as a promising energy management approach for distributed renewable energy sources. To efficiently facilitate the novel local energy trading, the concept of energy local area network (e-LAN) has been proposed as an innovative framework for future community power grid, which aims to improve the efficiency of end-to-end power delivery by the novel technique of energy routing. Under this framework, a proper market clearing approach is required for e-LAN to manage decentralized local energy market and coordinate end-to-end energy routing paths. In this paper, a decentralized market clearing approach with cooperative energy routing is proposed for local energy market in e-LAN. Due to the nonconvexity of the original market clearing problem, the proposed approach formulates a new problem for market clearing in e-LAN, which is approximately equivalent to the original problem. The new problem consists of two subproblems, a market clearing problem and a cooperative energy routing problem. For the market clearing problem, this paper proposes a dual decomposition based approach to clear local energy market in a decentralized way so as to avoid privacy violation. The cooperative energy routing problem is solved by proposing a Nash bargaining based approach, where each trading pair can obtain more benefits from cooperation than from noncooperation. Simulation results are presented to verify the effectiveness of the proposed approach in avoiding physical constraint violation, resolving path conflicts and improving social welfare.

Global path conflict detection algorithm of multiple agricultural machinery cooperation based on topographic map and time window

Path planning is one of the crucial problems of multi-machine cooperative navigation. In the context of regional farmland operation, multiple agricultural machines often need to complete multiple tasks together. Studies on global path conflict detection algorithms based on topographic maps and time windows have been conducted to solve the global path conflict problem of multiple agricultural machinery cooperation in the farmland operation environment. First, the global path conflict problem of multiple agricultural machinery cooperation was analyzed. The constraints of time window conflict detection and the time parameters to be considered in calculating the time window were proposed. Then, the global path preplanning was performed based on a topographic map and Dijkstra algorithm. According to the path preplanning results, the global path conflict was detected based on the time window. Finally, the global path conflict detection algorithm was simulated on MATLAB with the topographic map of Zhuozhou Experimental Farm as an example. The global path preplanning results show that the four paths overlapped in different degrees at nodes 1, 2, 7, 11, 15, 20, and 27. Path 3 and Path 1 were inclusive interval-type conflicts, and Path 4 and Path 1 were node-type conflicts. Path 3 adopted the change path strategy after the conflicting paths were replanned. The path was changed from [15-11-7-2-1-5-10-14-18-31] to [15-21-24-18-31]. Path 4 adopted the waiting strategy, and its paths did not change. Although the four paths still had different degrees of path overlap, there were no time conflict in the overlapping part of the replanned path. The simulation results show that the global path optimization and path conflict detection of multiple agricultural machinery cooperation could be realized based on the topographic map and time window. Moreover, the conflict resolution strategy with the least time could be obtained to achieve a safe, efficient, and conflict-free global path of multiple agricultural machinery cooperation. Thus, a foundation for further solving the path planning of multiple agricultural machinery cooperation in the complex operation environment of regional farmland is laid.

Safety Verification of Multiple Industrial Robot Manipulators with Path Conflicts Using Model Checking

Software development for robotic systems is traditionally performed based on simulations, manual code implementation, and testing. However, this software development approach can cause safety issues in some scenarios, including multiple robots sharing a workspace. When different robots are executing individual planned tasks, they may collide when not adequately coordinated. Safety problems related to coordination between robots may not be encountered during testing, depending on timing, but may occur during the system’s operation. In this case, formal verification methods can provide a more reliable means to ensure the safety of robotic systems. This paper uses the formal method of model checking for the safety verification of multiple industrial robot manipulators with path conflicts. We give comparative results of two model-checking tools applied to a system with two robot manipulators. Whole workflows, from requirement specification to testing, are presented.

Peer-to-peer energy trading in energy local area network considering decentralized energy routing

Peer-to-peer (P2P) energy trading is perceived as a promising energy management mechanism for decentralized renewable energy sources. The concept of energy local area network (e-LAN) is proposed as a new system framework for future microgrids to facilitate P2P trading where the end-to-end power delivery is achieved by energy routing. Decentralized P2P trading in e-LAN requires a perfectly competitive market and flexible end-to-end power delivery. In this paper, a new P2P trading model incorporating decentralized energy routing is proposed for e-LAN. The proposed model is composed of a P2P energy market and a loss compensation market. In the P2P energy market, a tripartite graph model is proposed to represent transactions where trading decisions and energy routing are jointly considered for each participant to pursue its optimal benefit. Considering the size problem and nonlinear weights of the tripartite graph, an extended depth-first branch-and-bound algorithm is proposed to prune low-efficiency paths. Based on the pruned tripartite graph, the competitive P2P market is captured by a multi-leader multi-follower Stackelberg game model. A loss compensation market is established to coordinate energy routing paths planned in a decentralized manner. By means of setting the price for utilizing each transmission line, the loss compensation market avoids network congestion and ensure a normal operation of e-LAN. Simulation results show that the proposed model can bring higher utilities for P2P participants compared with the existing P2P trading models in e-LAN. Additionally, the effectiveness of the proposed model in resolving path conflicts in a decentralized fashion and improving computation efficiency, are verified.

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within lay-time, which effectively reduces the handling cost for the container terminal and ship owners. The QC waiting caused by automated guided vehicles (AGVs) delay in the uncertain environment can be alleviated by dynamic scheduling optimization. A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems, in which the scheduling scheme determines the starting and ending nodes of paths, and the choice of paths between nodes affects the scheduling of subse-quentAGVs. This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime. A dynamic optimization algorithm, including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm, is designed to solve the optimal AGV scheduling and path schemes. A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.

Hillel David Soifer and Alberto Vergara (eds.), Politics after Violence: Legacies of the Shining Path Conflict in Peru (Austin, TX: University of Texas Press, 2019), pp. viii + 383, $45.00 hb.

Hillel David Soifer and Alberto Vergara (eds.), Politics after Violence: Legacies of the Shining Path Conflict in Peru (Austin, TX: University of Texas Press, 2019), pp. viii + 383, $45.00 hb. - Volume 54 Issue 4

Review of pedestrian trajectory prediction methods

Urban driverless vehicles will inevitably interact with pedestrians in the process of driving. In order to avoid path conflict with pedestrians, the research on pedestrian trajectory prediction is of great significance. This paper mainly summarizes the technical classification and research status of pedestrian trajectory prediction at this stage in detail. According to the different modeling methods, the existing trajectory prediction methods are divided into shallow learning-based trajectory prediction methods and depth learning based trajectory prediction methods. The advantages and disadvantages of the depth learning based trajectory prediction methods are compared, and the current mainstream pedestrian trajectory prediction public dataset is summarized, and the performance of the mainstream pedestrian trajectory prediction methods is compared in the dataset. Finally, the challenges and development trend of pedestrian trajectory prediction at this stage are prospected.

Integrated Task Allocation and Path Coordination for Large-Scale Robot Networks With Uncertainties

Artificial intelligence-enhanced autonomous unmanned systems, such as large-scale autonomous robot networks, are widely used in logistic and industrial applications. In this article, we address the integrated task assignment, path planning, and coordination problem applied for large-scale robot networks with the existence of uncertainties. In particular, a novel generalized conflict graph is designed which encodes the traveling time cost of the subsequent path planning result of each task-robot assignment and also includes the predicted path conflicts of each two assignments. An integrated optimization problem which aims to minimize the total traveling cost and potential path conflicts simultaneously is first formulated and then transformed into a linear programming instance to obtain the optimal solution. In particular, to satisfy the real-time requirement in large-scale systems, a greedy solution is presented which has the near-optimal performance but can decrease the computational complexity by orders of magnitude. The optimality, scalability, robustness, and efficiency of our approach are demonstrated by comprehensive comparisons with existing state-of-the-art approaches. Note to Practitioners—With the development of artificial intelligence techniques, large-scale autonomous robot networks are increasingly used in the logistic warehouses, unmanned container terminals, and intelligence transportation systems. This article considers the large-scale networks with hundreds or even thousands of unmanned robots which are implemented in lifelong transportation systems with uncertainties existed in practical execution process. Our main concept is to simultaneously minimize the total time cost of all the tasks and the potential motion conflicts among all the robots in the subsequent execution stage, thus alleviating robot congestions, balancing traffic distributions, increasing system efficiency, and improving the robustness and scalability. Lifelong simulations with thousand robots illustrate that our approach can reduce more than 30% of the time steps consumed for coordinating robot motion conflicts, and in the meantime, the throughput, and overall system efficiency are improved. However, simulation results show that the system improvement decreases in the presence of extreme high uncertainties (such as temporary motion and communication failures of the robot), due to the inaccurate conflict prediction in the integrated optimization stage. Our future work includes the deep-learning-based traffic evolution prediction and the online reallocation and planning in highly dynamic scenarios.

Traffic management and resource allocation for UAV-based parcel delivery in low-altitude urban space

This research proposes a framework of Unmanned Aircraft Vehicles (UAV) system traffic management in the context of parcel delivery in low-altitude urban airspace, including clustering-based UAV path planning, Unmanned Aircraft System Traffic Management (UTM) with conflict detection and resolution (CD&R), and mechanism design for airspace resource allocation. For UAV path planning, we develop a procedure by first clustering a large variety of obstacles that arise from building heights and terrain topology and can impede UAV flying. Based on the clustered obstacles, Saturated Fast-Marching Square (Saturated FM2) algorithm is then employed to generate optimal and alternative paths for each UAV mission. While identifying the optimal and alternative paths does not consider UAV traffic interactions, several traffic management models are proposed to efficiently allocate spatial and temporal airspace resources to UAV missions. The UTM models determine the departure time and the path to take for each UAV flight while resolving path conflicts from different perspectives. Specifically, four UTM models are proposed: Sequential Delay (SD) Model, Sequential Delay/Reroute (SDR) Model, Full Optimization (FO) Model, and Batch Optimization (BO) Model. Among the four models, the BO model is of particular interest as it strikes a balance between seeking a system optimum solution and maintaining computational tractability. Given that traffic management requires private information from UAV operators, the Vickrey-Clarke-Groves (VCG) mechanism is further adapted to the UTM context, in which airspace resource allocation is performed in conjunction with a payment scheme to incentivize truthful private information reporting by UAV operators. Extensive numerical analysis is conducted with San Francisco as the case study area. The results show the effectiveness of the proposed framework, particularly the scalability of the BO model. We also find that payment by a UAV flight under the adapted VCG mechanism depends critically on traffic density and the extent of interaction the UAV flight has with other flights.

Peer-to-Peer Energy Trading With Energy Path Conflict Management in Energy Local Area Network

The increasing penetration of distributed renewable generations has given rise to a novel energy management mechanism, peer-to-peer (P2P) energy trading. The concept of Energy Internet (EI) is proposed as a new energy system framework to facilitate P2P energy trading where all distributed electrical devices are interconnected via energy routers (ERs). A proper market clearing approach is required to coordinate decentralized decision making in P2P market for demand-supply balance and feasible end-to-end energy delivery. In this paper, a decentralized market clearing mechanism considering energy path conflict management is proposed for P2P energy trading in energy local area network (e-LAN). An adjusted conflict-based search (ACBS) algorithm is proposed to deal with the non-convexity of the social welfare maximization problem where the nonconvex problem is transformed into multiple independent convex problems. Considering the privacy protection of participants, a dual decomposition based approach is proposed to solve these convex problems in a decentralized manner where self rationality of each participant can be satisfied by maximizing its personal welfare. In addition, energy path conflicts caused by decentralized optimization is resolved by imposing penalty fee and observing the rule of energy sharing maximization. Numerical simulations are presented to verify the effectiveness of the proposed market clearing approach.

Benefits of the Tracers Test Technique in Improving the Performance of Group Wells

This study aims to determine the benefits of the interwell tracer test technique in improving the streamline simulation on the existing array's re-look. It also analyses the best scheme for the injection spot to enhance oil recovery. This study's subject parameters are limited to the tracer's breakthrough time, produced concentration, cumulative production, and pathline movement. The results showed that previous studies distinguished the correlation between injectors and producers with the development of a new pathline that conveys a scheme of water-flood for the application. Furthermore, several developments of water-flood schemes have been executed for better oil recovery in the mature fields worldwide. The vigorous simulation model is an effort to imitate the actual field capable of enhancing the character's understanding and helping the waterflood to rinse the oil trap or the unswept pocket. Unlike a Cartesian model, the streamline conveys an enhanced portrait of the transmissibility reservoir in terms of pressure-driven. The streamline model suggests the injector's preferred position to unlock any unswept oil in the formation and minimize the water path conflict, which leads to over injection in some regions. The expected outcome is the ultimate oil increment with the original technique associated with re-patterning the wells appropriately to gain residual oil saturation in the virgin alleyway.

Multi-objective automated guided vehicle scheduling based on MapReduce framework

During material handling processes, automated guided vehicles (AGVs) pose a path conflict problem. To solve this problem, we proposed a multi-objective scheduling model based on total driving distance and waiting time, and used the A* path planning algorithm to search the shortest path of AGV. By using a speed control strategy, we were able to detect the overlap path and the conflict time. Additionally, we adopted an efficient MapReduce framework to improve the speed control strategy execution efficiency. At last, a material handling system of smart electrical connectors workshop was discussed to verify the scheduling model and the speed control strategy combined with the MapReduce framework is feasible and effective to reduce the AGV path conflict probability. The material handling system could be applied in workshop to replace manual handling and to improve production efficiency.