Search Results (166)

In many applications, including underwater robotics, the coverage problem requires an autonomous vehicle to systematically explore a defined area while minimizing redundancy and avoiding obstacles. This paper investigates coverage path-planning strategies to enhance the efficiency of underwater gliders particularly in maximizing the probability of detecting a radioactive source while ensuring safe navigation. We evaluate three path-planning approaches: the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and the Optimal Control Problem (OCP). Simulations were conducted in MATLAB R2020a, comparing processing time, uncovered areas, path length, and traversal time. Results indicate that the OCP is preferable when traversal time is constrained, although it incurs significantly higher computational costs. Conversely, MST-based approaches provide faster but fewer optimal solutions. These findings offer insights into selecting appropriate algorithms based on mission priorities, balancing efficiency and computational feasbility. Full article

Outdoor sweeping is a tedious and labor-intensive task essential for maintaining the cleanliness of public spaces such as gardens and parks. Robots have been developed to address the limitations of traditional methods. Coverage Path Planning (CPP) is a critical function for these robots. However, existing CPP methods often perform poorly in large environments, where such robots are typically deployed. This paper proposes a novel CPP framework for outdoor sweeping robots operating in expansive outdoor areas, defined as environments exceeding 1000 square meters in size. The framework begins by decomposing the environment into smaller sub-regions. The sequence in which these sub-regions are visited is then optimized by formulating the problem as a Travelling Salesman Problem (TSP), aiming to minimize travel distance. Once the visiting sequence is determined, a boustrophedon-based CPP is applied within each sub-region. We analyzed two decomposition strategies, Voronoi-based and grid-based, and evaluated three TSP optimization techniques: local search, record-to-record travel, and simulated annealing. This results in six possible combinations. Simulation results demonstrated that Voronoi-based decomposition achieves higher area coverage (average coverage of 95.6%) than grid-based decomposition (average coverage 52.8%). For Voronoi-based methods, local search yielded the shortest computation time, while simulated annealing achieved the lowest travel distance. We have also conducted hardware experiments to validate the real-world applicability of the proposed framework for efficient CPP in outdoor sweeping robots. The robot hardware experiment achieved 84% coverage in a 19 m × 17 m environment. Full article

Photovoltaic (PV) energy has become a pillar of clean energy in rural areas. However, its extensive deployment in regions with geographically dispersed locations and limited road conditions has made efficient inspection a significant challenge. To address these issues, this study proposes a multi-regional PV inspection path planning method based on the crested porcupine optimization (CPO) algorithm. This method first employs a hybrid optimization framework combining a genetic algorithm, Simulated Annealing, and Fuzzy C-Means Clustering (GASA-FCM) to divide PV power stations into multiple regions, adapting to their dispersed distribution characteristics. Subsequently, the CPO algorithm is used to calculate obstacle-avoidance paths, replacing the Euclidean distance in the traditional Traveling Salesman Problem (TSP) with adaptive rural road constraint conditions to better cope with the geographical complexity in real-world scenarios. The simulation results verify the advantages of this method, achieving significantly shorter path lengths, higher computational efficiency, and stronger stability compared to the traditional solutions, thereby improving the efficiency of rural PV inspection. Moreover, the proposed framework not only provides a practical inspection strategy for rural PV systems but also offers a solution to the Multiple-Depot Multiple Traveling Salesmen Problem (MDMTSP) under constrained conditions, expanding its application scope in similar scenarios. Full article

The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also managing robot allocation and travel times, thus extending traditional problems like the Job Shop Scheduling Problem (JSSP) and Traveling Salesman Problem (TSP). Common solution methods include heuristics, metaheuristics, and hybrid methods. However, due to the complexity of these problems, existing models often struggle to provide efficient optimal solutions. Machine learning, particularly reinforcement learning (RL), presents a promising approach by learning from environmental interactions, offering effective solutions for task scheduling. This systematic literature review analyzes 71 papers published between 2014 and 2024, critically evaluating the current state of the art of task scheduling with mobile robots. The review identifies the increasing use of machine learning techniques and hybrid approaches to address more complex scenarios, thanks to their adaptability. Despite these advancements, challenges remain, including the integration of path planning and obstacle avoidance in the task scheduling problem, which is crucial for making these solutions stable and reliable for real-world applications and scaling for larger fleets of robots. Full article

This study addresses the multi-robot task allocation (MRTA) problem for logistics robots operating in zone-picking warehouse environments. With the rapid growth of e-commerce and the Fourth Industrial Revolution, logistics robots are increasingly deployed to manage high-volume order fulfillment. However, efficiently assigning tasks to multiple robots is a complex and computationally intensive problem. To address this, we propose a five-step optimization framework that reduces computation time while maintaining practical applicability. The first step calculates and stores distances and paths between product locations using the A* algorithm, enabling reuse in subsequent computations. The second step performs hierarchical clustering of orders based on spatial similarity and capacity constraints to reduce the problem size. In the third step, the traveling salesman problem (TSP) is formulated to determine the optimal execution sequence within each cluster. The fourth step uses a mixed integer linear programming (MILP) model to allocate clusters to robots while minimizing the overall makespan. Finally, the fifth step incorporates battery constraints by optimizing the task sequence and partial charging schedule for each robot. Numerical experiments were conducted using up to 1000 orders and 100 robots, and the results confirmed that the proposed method is scalable and effective for large-scale scenarios. Full article

Choosing the optimal path in planning is a complex task due to the numerous options and constraints; this is known as the trip design problem (TTDP). This study aims to achieve path optimization through the weighted sum method and multi-criteria decision analysis. Firstly, this paper proposes a weighted sum optimization method using a comprehensive evaluation model to address TTDP, a complex multi-objective optimization problem. The goal of the research is to balance experience, cost, and efficiency by using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to assign subjective and objective weights to indicators such as ratings, duration, and costs. These weights are optimized using the Lagrange multiplier method and integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. Additionally, a weighted sum optimization method within the Traveling Salesman Problem (TSP) framework is used to maximize ratings while minimizing costs and distances. Secondly, this study compares seven heuristic algorithms—the genetic algorithm (GA), particle swarm optimization (PSO), the tabu search (TS), genetic-particle swarm optimization (GA-PSO), the gray wolf optimizer (GWO), and ant colony optimization (ACO)—to solve the TOPSIS model, with GA-PSO performing the best. The study then introduces the Lagrange multiplier method to the algorithms, improving the solution quality of all seven heuristic algorithms, with an average solution quality improvement of 112.5% (from 0.16 to 0.34). The PSO algorithm achieves the best solution quality. Based on this, the study introduces a new variant of PSO, namely PSO with Laplace disturbance (PSO-LD), which incorporates a dynamic adaptive Laplace perturbation term to enhance global search capabilities, improving stability and convergence speed. The experimental results show that PSO-LD outperforms the baseline PSO and other algorithms, achieving higher solution quality and faster convergence speed. The Wilcoxon signed-rank test confirms significant statistical differences among the algorithms. This study provides an effective method for experience-oriented path optimization and offers insights into algorithm selection for complex TTDP problems. Full article

This paper proposes a method for recommending parent–child travel destinations and planning travel routes tailored to children of different ages. The method inputs basic information about the attractions (such as ticket prices, geographical locations, opening hours, etc.) into the system database and intelligently recommends suitable attractions based on user-provided data, including the children’s age, travel time, and trip theme. The paper transforms the route planning problem into a Traveling Salesman Problem (TSP) to optimize the travel route further. It presents an improved single-parent genetic algorithm based on sine chaos mapping (SCM-SPGA) to solve and optimize the shortest path for parent–child trips. Experimental results demonstrate that this algorithm has significant advantages in path planning accuracy and efficiency. The method is applied to a tourism dataset of Hainan, providing more personalized and age-appropriate attraction recommendations for tourists planning a parent–child trip to Hainan and optimizing the travel route. The research shows that the proposed method can effectively meet the personalized needs of parent–child travelers, significantly improving the overall travel experience by offering more tailored, efficient, and enjoyable trip-planning solutions. Full article

The Quantum Snowflake Algorithm (QSA) is a novel metaheuristic for both continuous and discrete optimization problems, combining collision-based diversity, quantum-inspired tunneling, superposition-based partial solution sharing, and local refinement steps. The QSA embeds candidate solutions in a continuous auxiliary space, where collision operators ensure that agents—snowflakes—reject each other and remain diverse. This approach is inspired by snowflakes which prevent collisions while retaining unique crystalline patterns. Large leaps to escape deep local minima are simultaneously provided by quantum tunneling, which is particularly useful in highly multimodal environments. Tests on challenging functions like Lévy and HyperSphere showed that the QSA can more reliably obtain very low objective values in continuous domains than conventional swarm or evolutionary approaches. A 200-city Traveling Salesman Problem (TSP) confirmed the excellent tour quality of the QSA for discrete optimization. It drastically reduces the route length compared to Artificial Bee Colony (ABC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Quantum Particle Swarm Optimization (QPSO), and Cuckoo Search (CS). These results show that quantum tunneling accelerates escape from local traps, superposition and local search increase exploitation, and collision-based repulsion maintains population diversity. Together, these elements provide a well-rounded search method that is easy to adapt to different problem areas. In order to establish the QSA as a versatile solution framework for a range of large-scale optimization challenges, future research could investigate multi-objective extensions, adaptive parameter control, and more domain-specific hybridisations. Full article

In this work, we develop a novel approach for designing the trajectory and controlling the velocity for an unmanned aerial vehicle (UAV) to achieve energy-efficient visual coverage of multiple terrestrial regions. Unlike previous works, our proposed approach allows the UAV to flexibly change both its velocity and its flight altitude during its task tour. To minimize the UAV’s total flight energy consumption during its task tour, we propose a novel four-step approach. The first step devises a simulated annealing (SA)-based searching algorithm to optimize the UAV’s photographing altitude for each region, considering various image resolution requirements and safety requirements across regions. Based on the identified photographing altitudes of all regions, the second step formulates a traveling salesman problem (TSP) and uses an efficient approximate method to determine the visiting order of each region. The third step generates all candidate intra-region trajectories used for visual coverage of each region, of which the optimal one will be decided together with the inter-region trajectory used for transitioning between neighboring regions during the fourth step. Finally, the fourth step employs dynamic programming (DP) and geometry to jointly determine the UAV’s velocity control and complete trajectory during its task tour. Extensive experiments validate the effectiveness and superiority of the proposed approach, compared with several existing methods. Full article

Large Language Models (LLMs) have immensely advanced the field of Artificial Intelligence (AI), with recent models being able to perform chain-of-thought reasoning and solve complex mathematical problems, ranging from theorem proving to ones involving advanced calculus. The success of LLMs derives from a combination of the Transformer architecture with its attention mechanism, the autoregressive training methodology with masked attention, and the alignment fine-tuning via reinforcement learning algorithms. In this research, we attempt to explore a possible solution to the fundamental NP-hard problem of combinatorial optimization, in particular, the Traveling Salesman Problem (TSP), by following the LLM approach in terms of the architecture and training algorithms. Similar to the LLM design, which is trained in an autoregressive manner to predict the next token, our model is trained to predict the next node in a TSP graph. After the model is trained on random TSP graphs with known near-optimal solutions, we fine-tune the model using Direct Preference Optimization (DPO). The tour generation in a trained model is autoregressive one-step generation with no need for iterative refinement. Our results are very promising and indicate that, for TSP graphs up to 100 nodes, a relatively small amount of training data yield solutions within a few percent of the optimal. This optimization improves if more data are used to train the model. Full article

This study develops an MTSP model for multi-UAV delivery optimization from a central hub, proposing a hybrid algorithm that integrates genetic simulated annealing-enhanced clustering with an improved Hopfield neural network to minimize the total flight distance. The proposed methodology initially employs an enhanced fuzzy C-means clustering technique integrated with genetic simulated annealing (GSA) to effectively partition the MTSP formulation into multiple discrete traveling salesman problem (TSP) instances. The subsequent phase implements an enhanced Hopfield neural network (HNN) architecture incorporating three key modifications: data normalization procedures, adaptive step-size control mechanisms, and simulated annealing integration, collectively improving the TSP solution quality and computational efficiency. The proposed algorithm’s effectiveness is validated through comprehensive case studies, demonstrating significant performance improvements in the computational efficiency and solution quality compared to conventional methods. The results show that during clustering, the improved clustering algorithm is more stable in its clustering effect. With regard to path optimization, the improved neural network algorithm has a higher computational efficiency and makes it easier to obtain the global optimal solution. Compared with the genetic algorithm and ant colony algorithm, its iteration times, path length, and delivery time are reduced to varying degrees. To sum up, the hybrid optimization algorithm has obvious advantages for solving a multi-UAV collaborative distribution path optimization problem. Full article

The Traveling Salesman Problem (TSP) is a classical discrete combinatorial optimization problem that is widely applied in various domains, including robotics, transportation, networking, etc. Although existing studies have provided extensive discussions of the TSP, the issues of improving convergence and optimization capability are still open. In this study, we aim to address this issue by proposing a new algorithm named IDINFO (Improved version of the discretized INFO). The proposed IDINFO is an extension of the INFO (weighted mean of vectors) algorithm in discrete space with optimized searching strategies. It applies the multi-strategy search and a threshold-based 2-opt and 3-opt local search to improve the local searching ability and avoid the issue of local optima of the discretized INFO. We use the TSPLIB library to estimate the performance of the IDINFO for the TSP. Our algorithm outperforms the existing representative algorithms (e.g., PSM, GWO, DSMO, DJAYA, AGA, CNO_PSO, Neural-3-OPT, and LIH) when tested against multiple benchmark sets. Its effectiveness was also verified in the real world in solving the TSP in short-distance delivery. Full article

The vehicle routing problem (VRP), as one of the classic combinatorial optimization problems, has garnered widespread attention in recent years. Existing deep reinforcement learning (DRL)-based methods predominantly focus on node information, neglecting the edge information inherent in the graph structure. Moreover, the solution trajectories produced by these methods tend to exhibit limited diversity, hindering a thorough exploration of the solution space. In this work, we propose a novel Edge-Driven Multiple Trajectory Attention Model (E-MTAM) to solve VRPs with various scales. Our model is built upon the encoder–decoder architecture, incorporating an edge-driven multi-head attention (EDMHA) block within the encoder to better utilize edge information. During the decoding process, we enhance graph embeddings with visitation information, integrating dynamic updates into static graph embeddings. Additionally, we employ a multi-decoder architecture and introduce a regularization term to encourage the generation of diverse trajectories, thus promoting solution diversity. We conduct comprehensive experiments on three types of VRPs: (1) traveling salesman problem (TSP), (2) capacitated vehicle routing problem (CVRP), and (3) orienteering problem (OP). The experimental results demonstrate that our model outperforms existing DRL-based methods and most traditional heuristic approaches, while also exhibiting strong generalization across problems of different scales. Full article

This paper proposes a cutting trajectory planning method for boom-type roadheaders using an improved Nondominated Sorting Genetic Algorithm II (NSGA-II) with an elitist strategy. Existing methods often overlook constraints related to cutterhead dimensions and target sections, affecting section formation quality. We develop a kinematic model for coordinate transformations and design a simplified cutterhead and constraint model to generate feasible cutting points. Bi-objective functions—minimizing cutting trajectory length and turning angle—are formulated as a bi-objective traveling salesman problem (BO-TSP) with adjacency constraints. NSGA-II is adapted with enhancements in adjacency constraint handling, population initialization, and genetic operations. Simulations and experiments demonstrate significant improvements in convergence speed and computation time. Virtual cutting experiments confirm trajectory feasibility under varying postures, achieving high formation quality. A comparison of planned and tracked trajectories shows a maximum deviation of 23.879 mm, supporting autonomous cutting control. This method advances cutting trajectory planning for roadway section formation and autonomous roadheader control. Full article

A Probabilistic Bit (P-Bit) device serves as the core hardware for implementing Ising computation. However, the severe intrinsic variations of stochastic P-Bit devices hinder the large-scale expansion of the P-Bit array, significantly limiting the practical usage of Ising computation. In this work, a behavioral model which attributes P-Bit variations to two parameters, $\alpha$ and $\Delta V$, is proposed. Then the weight compensation method is introduced, which can mitigate $\alpha$ and $\Delta V$ of P-Bit device variations by rederiving the weight matrix, enabling them to compute as ideal identical P-Bits without the need for weights retraining. Accurately extracting the $\alpha$ and $\Delta V$ simultaneously from a large P-Bit array which is prerequisite for the weight compensation method is a crucial and challenging task. To solve this obstacle, we present the novel automatic variation extraction algorithm which can extract device variations of each P-Bit in a large array based on Boltzmann machine learning. In order for the accurate extraction of variations from an extendable P-Bit array, an Ising Hamiltonian based on a 3D ferromagnetic model is constructed, achieving precise and scalable array variation extraction. The proposed Automatic Extraction and Compensation algorithm is utilized to solve both 16-city traveling salesman problem (TSP) and 21-bit integer factorization on a large P-Bit array with variation, demonstrating its accuracy, transferability, and scalability. Full article