Search Results (142)

Coordinating robot swarms requires jointly solving the interdependent Multi-Robot Task Allocation (MRTA) and Multi-Agent Path Finding (MAPF) problems under strict time and communication constraints, yet most existing methods rely on centralized planning or expose agents’ exact positions. In this study, a fully decentralized algorithm is proposed in which each agent estimates the positions and intended plans of others from broadcast bid values rather than shared coordinates, anticipating conflicts at intersections before moving and dynamically altering its movement or task assignment when it predicts it cannot reach its task in time. The method combines the Priority Inheritance with Backtracking (PIBT) algorithm for collision-free navigation with a novel Interval Type-3 Fuzzy Logic (IT3FL) mechanism for conflict resolution and congestion-aware rerouting. The approach was evaluated across seven benchmark environments against the centralized methods Enhanced Conflict-Based Search (ECBS) and ECBS with Task Allocation (ECBS-TA) and the Consensus-Based Auction Algorithm (CBAA). It reduced path cost by up to 7.10% relative to ECBS in open environments, while centralized methods remained superior in complex corridor-based maps. In the most demanding constrained scenario, it reduced solution cost by up to 47.03% and improved task completion by 35% over CBAA, demonstrating a robust, scalable decentralized alternative. Full article

With growing interest in automated dismantling operations for hazardous environments, automatically planning safe and efficient disassembly sequences is becoming increasingly important. When a large structure is segmented into parts, the removal order must ensure that each part can be extracted safely without destabilising the remaining structure. This paper presents a comparative study of four algorithms for solving the disassembly sequencing problem in two dimensions: First Feasible Random Search (FFRS), Greedy Search (GS), Height-Decreasing Search (HDS), and Stochastic Tree Search (STS). The present study focuses specifically on sequencing feasibility under geometric and physical constraints, namely connectivity, accessibility, and structural stability. The 2D formulation provides a simplified yet computationally efficient testbed for analysing algorithmic behaviour under varying cutting complexities, with the objective of minimising the total removal trajectory length. Results show that while STS consistently finds optimal or near-optimal solutions, its factorial runtime limits scalability. GS produces high-quality solutions efficiently but can become trapped in infeasible configurations, whereas HDS offers strong reliability and speed at the expense of solution quality. Based on these findings, a hybrid height-based backtracking algorithm is proposed as a promising future direction, combining the efficiency of greedy search with the robustness of stochastic exploration. The results provide insight into the relative strengths and limitations of different sequencing strategies and establish a foundation for future extension to more realistic dismantling scenarios, including 3D and radiologically constrained applications. Full article

Multi-objective optimization plays a fundamental role in solving complex engineering design problems characterized by conflicting objectives and nonlinear constraints. In this study, a novel hybrid optimization algorithm, named Multi-objective Lemurs Optimizer-Backtracking Search Algorithm (MOLOBSA), is proposed to improve the exploration and exploitation capabilities of existing metaheuristic methods. The proposed approach integrates the global exploration ability of the Lemurs Optimizer (LO) with the efficient mutation and crossover mechanisms of the Backtracking Search Algorithm (BSA) within a multi-objective optimization framework. The effectiveness of the proposed algorithm is evaluated using the CEC2020 multimodal multi-objective benchmark suite, where its performance is assessed using the PSP and IGDX performance indicators. In addition, the proposed method was successfully applied to the multi-objective design optimization of an I-beam structure, where the objectives were to minimize the structural weight and the maximum displacement under mechanical constraints. The obtained Pareto solutions exhibit better diversity and improved trade-off characteristics compared with those produced by the baseline algorithm. Full article

Unmanned aerial vehicles (UAVs) have wide application prospects in disaster search and rescue, ecological monitoring and environmental inspection tasks, where target search is a key link to realize autonomous task execution. UAVs often face challenges related to limited onboard computational resources and inefficient environmental coverage when used for target search. To address these issues, this paper proposes an autonomous search method for UAVs based on combined lightweight target detection and coverage path planning. In this method, the target search task was decomposed into two core parts: target recognition and path planning. Firstly, in terms of target recognition, the YOLOv8n model was subjected to channel pruning and INT8 quantization to reduce its computational complexity, while HSV space data augmentation was incorporated to enhance recognition robustness in complex environments. Secondly, path planning was formulated as a dual-layer task comprising “spatial coverage + target confirmation.” A grid-based search environment model was constructed, and a coverage path planning strategy was put forward that integrated breadth-first search (BFS) with local greedy optimization to achieve efficient traversal of predefined search areas. Simultaneously, the A* algorithm was employed for path backtracking to cover omitted regions. Finally, a simulation platform for UAV target search was built to validate the recognition performance and search efficiency of the proposed method. The experimental results demonstrated that the proposed method significantly improved the UAV target search efficiency and reduced the path redundancy while ensuring the recognition accuracy, thereby offering an effective solution for autonomous UAV search on resource-constrained embedded platforms. Full article

Large-scale composite equipment manufacturing imposes stringent requirements on the lean management of multi-specification fiber prepreg sheet storage, while existing optimization methods suffer from poor process adaptability, insufficient multi-objective collaborative optimization capability, and low space utilization of static layouts. This study constructs a symmetric optimization framework for multi-objective composite sheet storage to address these critical bottlenecks. Specifically, the multi-dimensional process value of fiber sheets is quantified, and the layered storage optimization problem is transformed into a 0–1 knapsack problem with symmetric constraints. An improved Dynamic Programming–Backtracking (DP-BT) material selection algorithm and an adaptive dynamic programming iterative space optimization algorithm are proposed to achieve a symmetric balance of inter-layer space utilization and global optimization. Experimental validation with actual production data of 17 fiber sheet types verifies that the proposed method enables space optimization for specified layer counts to maximize average space utilization, with the rate rising from 79.4% (initial 4-layer layout) to 95.7% (3-layer) and 99.9% (2-layer), and a peak single-layer utilization of 100%. This framework achieves favorable optimization performance in the target production scenario and provides a referenceable symmetric optimization approach for the lean storage management of similar fiber sheet storage scenarios in composite manufacturing. Full article

Intel Software Guard Extensions (SGX) have been widely studied and adopted in privacy-preserving information systems to enhance the security and privacy guarantees of sensitive data computation. By constructing a protected enclave within the processor, SGX provides hardware-enforced confidentiality and integrity for sensitive data and critical code. Nevertheless, due to inevitable interactions between trusted enclaves and untrusted host environments, SGX applications remain vulnerable to memory corruption attacks. Existing detection techniques exhibit fundamental limitations, including the lack of systematic induction of SGX-specific memory corruption behaviors, the absence of fine-grained parameter-level taint modeling during call-chain construction, and relatively inefficient call-chain exploration strategies over large search spaces. To address these issues, we propose ParaTaintGX, an analysis framework that integrates parameter-level taint states into vulnerability detection. ParaTaintGX constructs fine-grained call-chain nodes that capture both functions and the taint states of their parameters. It further introduces a Multi-node Heuristic Priority Search Algorithm to guide call-chain exploration. In addition, a backtracking-based pruning strategy is applied during path analysis to efficiently identify memory corruption vulnerabilities. Our evaluation demonstrates that ParaTaintGX discovers 12 vulnerabilities across 10 open-source SGX projects, outperforming the best baseline tool by two vulnerabilities. It achieves 19.35% precision, surpassing the most precise existing tool by 8.37 percentage points. These results highlight its superior detection capability and precision. Full article

High-speed railway signaling equipment constitutes safety-critical infrastructure, wherein hardware failures may directly compromise operational safety. During the hardware prototyping and verification stage, structural testing is essential to detect latent faults in digital logic circuits and to ensure compliance with stringent safety integrity requirements. However, conventional test generation methods often suffer from long generation times and excessive test vector volume. To address these challenges, this study proposes a whale optimization-based dynamic compression Automatic Test-Pattern Generation (ATPG) algorithm. The proposed method integrates a discrete whale optimization algorithm (WOA) with a deterministic PODEM framework to dynamically compress generated test vectors. Additionally, a multi-path-sensitized PODEM enhanced with desensitization techniques is introduced to reduce backtracking and improve search efficiency. The proposed algorithm has been applied to the computer interlocking golden model netlist for testing purposes, achieving an impressive fault coverage rate of 100%. Test results from the ISCAS-85 standard circuit indicate that our approach significantly reduces both the length of the vector set and the time required for test generation when compared to traditional PODEMs without vector compression and pseudo-random combined PODEM vector generation methods. This advancement effectively enhances overall vector generation efficiency while maintaining comprehensive fault coverage. Full article

To enhance the autonomous operation capability of deep-sea mining vehicle formations, this study addresses the issues of slow convergence in formation path planning and insufficient obstacle avoidance flexibility under complex environments by investigating a global path planning and local obstacle avoidance strategy based on an improved RRT algorithm*. Through dynamic elliptical sampling, adaptive goal-biased sampling, safe distance detection, and path smoothing optimization, the efficiency and passability of path planning are improved. For the obstacle avoidance of formation members, a priority determination model incorporating local obstacle avoidance, formation contraction, and transformation is designed, and methods such as Gaussian distribution fan-shaped sampling and trajectory backtracking are proposed to optimize the local planning effect. Simulation results show that this method can effectively improve the path planning quality and obstacle avoidance performance of mining vehicle formations in complex environments. Specifically, when in a longitudinal formation, the maximum inter-vehicle error is approximately 15.1%, and the average error is controlled within 3.5%; when in a triangular formation, the maximum inter-vehicle error is approximately 20%, and the average error is controlled within 4.2%, indicating promising application prospects. Full article

Gas leak detection in industrial environments poses critical safety challenges that require algorithms capable of balancing rapid source identification with comprehensive spatial coverage. Conventional approaches using fixed sensor networks provide limited coverage, while manual inspection methods expose personnel to hazardous conditions. This paper presents a novel Gradient-Guided Depth-First Search (GG-DFS) algorithm designed for autonomous mobile robots, which integrates gradient-following behavior with systematic exploration guarantees. The algorithm utilizes local concentration gradient estimation to direct movement toward leak sources while implementing depth-first search with backtracking to ensure complete environmental coverage. We assess the performance of GG-DFS through extensive simulations comprising 160 independent runs with varying leak configurations (1–4 sources) and starting positions. Experimental results show that GG-DFS achieves rapid initial source detection $(9.3\pm 7.3\mathrm{steps};\mathrm{mean}\pm \mathrm{SD})$, maintains 100% coverage completeness with 100% detection reliability, and achieves 50% exploration efficiency. In multi-source conditions, GG-DFS requires 70% fewer detection steps in four-leak scenarios compared to single-leak environments due to gradient amplification effects. Comparative evaluation demonstrates a substantial improvement in detection speed and efficiency over standard DFS, with GG-DFS achieving a composite performance score of 0.98, compared to 0.65 for standard DFS, 0.64 for the lawnmower pattern, and 0.53 for gradient ascent. These findings establish GG-DFS as a robust and reliable framework for safety-critical autonomous environmental monitoring applications. Full article

Path planning aims to find a safe and efficient path from a starting point to an end point, and it has been well developed in fields such as robot navigation, autonomous driving, and intelligent decision systems. However, traditional path planning faces challenges in an uncertain rescuing environment due to limited sensing range and a lack of accurate obstacle information. In order to address this issue, this paper proposes an improved two-stage Theta* algorithm for handling multi-probability obstacle scenarios in unstructured rescue environments. First, a global probability raster map is constructed by integrating historical maps and expert prediction maps with probability weights quantifying the uncertainty in the spatial and temporal distribution of obstacles. Second, a probability-sensitive heuristic function (PSHF) is designed, and a Sigmoid function is used to map the probability field of obstacles, thereby enabling limited penetration in low-risk areas and enforced avoidance in high-risk areas. Furthermore, a multi-stage line-of-sight detection optimization mechanism is proposed, which combines probability soft threshold penetration and backtracking verification to improve the noise robustness. Finally, a hierarchical planning architecture is constructed to separate global probabilistic guidance from local strict obstacle avoidance, ensuring both the global optimality and local adaptability of the path. Extensive simulation results in mine rescue scenarios demonstrate that the proposed method achieves lower path cost and fewer path nodes compared to traditional A*, Dijkstra, and Theta* algorithms, while significantly reducing local replanning overhead and maintaining stable performance across multiple uncertain environments. Full article

Carbon sequestration by vegetation and soil conservation are vital components in balancing greenhouse gas emissions and enhancing terrestrial ecosystem carbon sinks. They also represent an efficient pathway towards achieving carbon neutrality objectives and addressing numerous environmental challenges arising from global warming. Soil and water conservation, as crucial elements of ecological civilisation development, constitute a key link in realising carbon neutrality. This study systematically quantifies and forecasts the spatiotemporal characteristics of carbon sink capacity in soil and water conservation within the study area of Puding County, a typical karst region in Guizhou Province, China. Following a research approach of “mechanism elucidation–model construction–categorised estimation”, we established a carbon sink calculation system based on the dual mechanisms of vertical biomass carbon fixation via vegetative measures and horizontal soil organic carbon (SOC) retention using engineering measures. This system combines forestry, grassland, and engineering, with the aim of quantifying regional carbon sinks. Machine learning regression algorithms such as Random Forest, ExtraTrees, CatBoost, and XGBoost are used for backtracking estimation and optimisation modelling of soil and water conservation as carbon sinks from 2010 to 2022. The results show that the total carbon sink capacity of soil and water conservation in Puding County in 2017 was 34.53 × 10⁴ t, while the contribution of engineering measures was 22.37 × 10⁴ t. The spatial distribution shows a pattern of “higher in the north and lower in the south”. There are concentration hotspots in the central and western regions. Model comparison demonstrates that the Random Forest and extreme gradient boosting regression models are the best models for plantations/grasslands and engineering measures, respectively. The LSTM model was applied to predict carbon sink variables over the next ten years (2025–2034), showing that the overall situation is relatively stable, with only slight local fluctuations. This study solves the problem of the lack of quantitative data on soil and water conservation as carbon sinks in karst areas and provides a scientific basis for regional ecological governance and carbon sink management. Our findings demonstrate the practical significance of promoting the realisation of the “double carbon” goal. Full article

Hybrid AC/DC transmission expansion planning must balance investment cost, supply reliability and AC/DC stability, which challenges conventional mathematical programming and heuristic methods. This paper proposes a multi-objective planning framework based on an improved multi-step backtracking α-Q(λ) reinforcement learning algorithm with eligibility traces and an adaptive learning factor. A tri-objective model minimises annual economic cost, expected power shortage and a comprehensive electrical index that combines electrical betweenness, commutation-failure margin and effective short-circuit ratio. The mixed-integer planning problem is reformulated as an interactive learning process, where the state encodes candidate line construction decisions, the action builds or cancels lines, and the eligibility-trace matrix is used to quantify line importance. Case studies on the Garver-6 system, the IEEE 24-bus reliability test system and a 500 kV regional hybrid AC/DC grid show that, compared with classical Q-learning, the proposed method yields lower annual cost, reduced expected power shortage and improved AC/DC stability; in the 500 kV system, the expected annual power shortage is reduced from 70,810 MWh to 28,320 MWh. Full article

Robots are widely used to carry goods in automated warehouses. Planning collision-free paths for multiple robots which are continuously given new goals is called Lifelong Multi-Agent Pathfinding. In a lifelong environment, conflicts may emerge among the robots, and continuous replanning is needed. We propose, develop, implement, and evaluate the novel approach called the Clustered Reverse Resumable A* (CRRA*) algorithm to enhance the continuous computation of the shortest path from the changing position of a robot to its goal. The Priority Inheritance with Backtracking (PIBT) algorithm is the currently known most efficient algorithm to handle the pathfinding of thousands of robots in a warehouse. The PIBT algorithm requires that in each step each robot evaluates the distances from its surrounding positions to its goal; therefore, we integrate the CRRA* algorithm with the PIBT algorithm to evaluate CRRA*. The evaluation results show that the CRRA* leads to a significant reduction in computation time, especially in larger warehouses where the obstacles form well-separated spaces. At the same time, the degradation in solution quality is minimal. The CRRA* algorithm is more efficient in larger warehouses than the plain Reverse Resumable A* (RRA*) algorithm. The faster computation of slightly suboptimal paths can be useful in many practical applications, especially in situations where real-time planning is more important than finding the optimal paths. CRRA* can also be used as a heuristic in any multi-agent pathfinding solution to obtain a faster, nearly accurate heuristic. Full article

This paper proposes a scheduling approach for multi-type spacecraft operational tasks that can be interleaved, considering constrained space–ground telemetry, tracking, and command (TT&C) resources, as well as task splitting. A mixed-integer linear programming model is formulated to maximize the total task completion reward under service time-window constraints for splittable and unsplittable routine tasks, continuous tracking requirements, coupling relationships between routine and continuous tracking tasks, temporal logic dependencies, visibility constraints, and non-overlapping scheduling conditions. To improve solution efficiency and scheduling performance, a heuristic algorithm that combines priority rules with partial backtracking is developed. Task priorities are determined based on completion rewards, due times, execution durations, and temporal relationships, and scheduling is refined to avoid conflicts with predefined constraints. A partial backtracking mechanism guided by task release times enables effective adjustment when TT&C requirements cannot be satisfied. Comparative experiments with CPLEX and four heuristic algorithms validate the effectiveness of the proposed method. Full article

Autonomous navigation of unmanned aerial vehicles (UAVs) in unknown complex environments requires safe, fast and efficient path planning algorithms. Currently, the two-stage framework of “front-end search and back-end optimization” is widely adopted. However, existing research primarily focuses on path planning performance in high-density obstacle scenarios, lacking effective strategies for large-sized obstacles. Furthermore, the current two-stage framework suffers from issues such as path divergence and reduced flight speed. To address these limitations, this paper proposes a general path planning algorithm with soft constraints for UAVs in high-density obstacle scenarios and large-sized obstacle scenarios. The core of the algorithm involves guiding the UAV trajectory through the establishment of well-defined local target points. The front-end employs an expanded space observer for two observations, constructing a real-time safety region, and integrates flight state information to generate local target points using reinforcement learning. The back-end generates trajectories that allows UAVs to fly towards the local target points at higher speeds through an improved Soft Differential Constrained Minimum Snap (SDC-Minimum Snap) algorithm. For large-sized obstacles, a cost-function-based backtracking and circumvention mechanism is introduced to ensure reliable obstacle avoidance. Simulations and real-world experiments validate the generality and feasibility of the proposed algorithm in both scenarios. Full article