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The lunar south polar region’s extreme illumination conditions impose strict energy constraints for solar-powered rover operations. Traditional Sun-synchronous path planning relies on dynamic time-dependent illumination evaluation, leading to high computational costs. We present CIRsE-Net, a spatiotemporal deep learning model that generates 72 h continuous illumination maps from hourly sequential illumination data. The model integrates a lightweight SST-VGG encoder (a customized 14-layer CNN for spatial feature extraction), BiGRU temporal modelling (a bidirectional recurrent network for capturing forward and backward temporal dependencies), and a consistency-aware spatiotemporal attention mechanism. On three different lunar illumination datasets (20 m/pixel, 5 m/pixel, and 20 m/pixel with a 2 m panel height), the model achieves Dice scores up to 0.983 and accuracy up to 0.985. When integrated with an enhanced 3ST-A* planner, the framework converts dynamic path planning into a static search task, reducing computational overhead while preserving path optimality and satisfying slope and illumination constraints. This work provides a validated methodological framework for Chang’E-7 mission planning and future lunar polar exploration missions by transforming dynamic path planning into a static search task. Full article

To address slow convergence, local optimum stagnation, and multi-objective imbalance problems for unmanned aerial vehicle (UAV) three-dimensional (3D) path planning in complex obstacle environments, an improved adaptive two-stage pigeon swarm optimization (IPIO) algorithm is proposed. Firstly, a hybrid initialization strategy integrating Latin hypercube sampling and obstacle avoidance constraints is adopted to improve initial population diversity and the quality of feasible solutions. Secondly, in the map compass stage, a linearly decreasing adaptive map factor and population diversity-based dynamic perturbation strategy are introduced to balance global exploration and local exploitation while preventing premature convergence. In the landmark stage, an inverse fitness weighting elite center updating mechanism and linearly decreasing elite quantity strategy are designed to enhance the guidance of high-quality individuals and accelerate convergence. A multi-objective fitness function integrating path length, obstacle avoidance safety, and flight smoothness is constructed, whose weight coefficients (${\omega }_{\mathrm{L}}=0.3$, ${\omega }_{\mathrm{C}}=0.5$, ${\omega }_{\mathrm{S}}=0.2$) are calibrated through parameter-sensitivity analysis and Pareto frontier comparison across six representative weight combinations. Combining ablation validation for each improved module, single-UAV multi-scenario tests, and preliminary multi-UAV trials, these coordinated improvements realize targeted optimization for UAV 3D flight characteristics. Specifically, the preliminary multi-UAV trials involve three UAVs performing independent trajectory planning in shared obstacle environments without explicit inter-UAV collision avoidance constraints, and the reported improvements are based on single-UAV experiments. Finally, comparative experiments are conducted with a standard 100 × 100 × 50 m space, and varying obstacle densities are demonstrated in six diverse 3D test scenarios, where the proposed IPIO achieves an average path length reduction of 12.8% and 15.3% compared to the standard PIO and PSO, respectively. The average fitness improvement is 14.2% over PIO, 16.8% over PSO, 19.5% over GWO, 24.1% over CO, and 38.7% over CS. Key path-quality metrics include a minimum obstacle clearance of 2.37 m, average smoothness cost of 0.34, average convergence time of 0.60 s, and computational cost of O(N*D*MaxIter). Statistical tests confirm that these improvements are significant (p < 0.05) in all tested scenarios. This study presents an efficient and robust algorithm for autonomous three-dimensional path planning of UAVs in complex obstacle environments. Full article

Urban forests play an important role in improving environmental quality and supporting the well-being of city residents. However, their effective planning and management require comprehensive assessment methods that integrate both spatial characteristics and users’ perceptions. This study evaluates the quality of the Piast Hills urban forest complex in Zielona Góra (Poland) using a methodology that combines field analysis, questionnaire surveys, expert assessment and Space Syntax analysis. Four assessment criteria were adopted: accessibility, safety, recreational appeal and user comfort. These components were integrated into the proposed Synthetic Assessment of Forest Environment Index (SAFEI), while Space Syntax analysis was incorporated as a complementary spatial analysis supporting the interpretation of accessibility-related results and the spatial configuration of the path network. The findings indicate that recreational appeal was the highest-rated aspect of the urban forest, whereas safety received the lowest assessment, highlighting the need to improve lighting, surveillance and wayfinding. The calculated SAFEI value (0.607) indicates a moderately high quality of the investigated urban forest complex. The Space Syntax analysis provided complementary spatial information that was consistent with the accessibility patterns identified through the questionnaire and expert assessment. Unlike most previous studies focusing on individual aspects of urban forests, the SAFEI framework provides an integrated approach that can support evidence-based planning, management and long-term monitoring of urban forests and other urban green spaces. Full article

Fixed-link manipulators have limited adaptability to changes in task locations and obstacle layouts, while sequential mechanism design and trajectory planning restrict their coordinated performance. This study proposes a surrogate-assisted bilevel optimization method for a four-degree-of-freedom PRRR variable-link-length manipulator. The three link lengths are treated as outer-layer mechanism variables, whereas B-spline control points and trajectory duration are optimized in the inner layer subject to joint, motion, endpoint, and collision constraints. An objective-decoupled surrogate predicts trajectory duration, path length, jerk cost, and minimum clearance, and is embedded in an adaptive reference vector-guided multi-operator multi-objective beluga whale optimization algorithm. The framework combines inverse-kinematics prescreening, surrogate evaluation, high-fidelity trajectory re-optimization, dense constraint verification, and preference-based decision-making. Blind-test, ablation, and high-fidelity verification results show that the method efficiently identifies high-quality, physically feasible mechanism–trajectory candidates. Factorial analysis of an obstacle-constrained handling task indicates that trajectory optimization primarily improves smoothness and clearance, whereas mechanism adaptation redistributes joint motion and further enhances overall trajectory quality. Physical experiments demonstrate the executability of the selected mechanism–trajectory solutions without observed cylinder collision or joint-limit activation in the tested trials. These results demonstrate that the proposed framework provides an effective approach to task-adaptive mechanism–trajectory co-optimization in constrained environments. Full article

Information in the Glider Snake Optimizer (GSO) propagates through a leader–predecessor chain. This structure is simple, but it can lose diversity when adjacent agents converge to the same region. The proposed Multi-Strategy Glider Snake Optimizer (MSGSO) retains the original GSO update and subsequently applies nonlinear dynamic polynomial mutation (NDM), elite opposition-based learning (EOBL), and quadratic interpolation (QI). MSGSO was evaluated on CEC 2019 and CEC 2022 with 30 matched random seeds and 15,000 objective evaluations for every algorithm–problem pair. The experiments included seven alternative optimizers and all single, pairwise, and three-operator GSO variants. Across the 34 benchmark problems, MSGSO significantly outperformed GSO on 31 and showed no significant loss. It nevertheless ranked third in each external comparison: L-SHADE led CEC 2019 and CEC 2022 at $D=10$, while CMA-ES led CEC 2022 at $D=20$. The ablation attributed most of the gain to NDM; NDM–GSO led the GSO variants on CEC 2019, and NDM–QI–GSO led at both CEC 2022 dimensions. In the UAV study, MSGSO returned 29 feasible paths in 30 runs in Scenario 1 and feasible paths in every run in the other two scenarios. It led the feasibility-first ranking in Scenarios 1 and 2 and placed third in Scenario 3. Thus, MSGSO improves its parent algorithm, although the full three-operator sequence is not consistently the best configuration. Full article

This work presents the design, construction and operation of the SKD-1, a modular skid-steer unmanned ground vehicle (UGV) developed as a low-cost research platform for mobile robotics applications. The platform integrates a differential skid-steer drive system, a Raspberry Pi-based onboard computer, and a microcontroller-based control layer implemented using an STM32 microcontroller. The sensing system includes light detection and ranging (LiDAR), global navigation satellite system (GNSS), and an inertial measurement unit (IMU), enabling experiments in localization, mapping, and autonomous navigation. The software architecture is based on the Robot Operating System (ROS) 2 framework, relying on standard ROS 2 packages for perception, mapping, and path planning, with the custom hardware-interface layer being the only non-standard software component. The mechanical and electronic subsystems were designed with a modular architecture that facilitates maintenance, sensor replacement, and hardware upgrades. The primary contribution of this work is the open-hardware design, integration, and documentation of a reproducible robotics testbed, motivated by the prohibitive cost of commercial platforms in resource-constrained research contexts. Indoor and outdoor experiments—covering velocity-tracking, SLAM, and waypoint-navigation trials—demonstrate the functional integration of the sensing, actuation, and computing subsystems. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Aiming at the vulnerability of nodes within collaborative task execution links and the slow response of link reconfiguration under limited communication conditions and sudden equipment failure scenarios at sea, and noting that existing approaches mainly rely on static optimization or offline planning without an integrated online reconfiguration framework, this paper proposes an A-star enhanced Multi-Objective Collaborative Reconfiguration (A-MOCR). By constructing a node state probability model and a multi-layer complex network framework, the proposed method sets maximization of task completion probability and network resilience as dual optimization objectives, and leverages the NSGA-III algorithm to generate primary links and corresponding backup link pools. Link reconfiguration is activated through real-time monitoring of node anomaly indicators; available surviving routes from the backup pool are prioritized for matching. If matching cannot be achieved, local A-star search or global path replanning will be initiated. Comparative simulation experiments against two baseline approaches demonstrate that the proposed A-MOCR achieves comparable mission success rates and link survival duration while significantly reducing the average reconfiguration time by over 50%. With equivalent task success probability, A-MOCR completes link reconfiguration within shorter time and maintains steady performance advantages under varying communication coverage parameters. Unlike conventional methods that require global re-planning after each failure, A-MOCR decouples offline optimization from online matching, enabling millisecond-level switching. The method realizes millisecond-level link switching and provides effective technical support for the adaptive reconfiguration of collaborative task execution links for marine multi-platform systems. Full article

Navigating large, unfamiliar campuses can be challenging for visitors, even with campus maps available. To address this issue, this study proposes an intelligent, autonomous campus navigation system to help users reach their destinations efficiently. The proposed system integrates computer vision, LiDAR, speech recognition, global positioning, and path-planning technologies to provide accurate, user-friendly guidance in complex environments. A YOLO-based neural network performs real-time building recognition, while a speech recognition module interprets users’ spoken destination requests and commands. GPS data are mapped to campus map coordinates to improve localization accuracy, and Dijkstra’s algorithm computes optimal navigation paths. All components are integrated into a graphical user interface that provides real-time visual feedback, including recognized building names and current location. Experimental results demonstrate that the proposed system achieves reliable building recognition, accurate speech understanding, and effective route planning, significantly reducing navigation time for users unfamiliar with the campus. The proposed framework not only enhances smart campus navigation but also shows strong potential for extension to other large-scale environments such as hospitals and shopping malls. Full article

A collision-free path is not sufficient for visibility-dependent mobile robot tasks: a moving obstacle can block the camera–target line of sight and cause inspection or visual-servoing failure even when the robot remains physically safe. Maintaining visual contact with targets is therefore important in Industry 4.0 environments, yet visibility-preserving maneuvers can conflict with navigation progress and collision avoidance. This work presents the Visibility-Informed Safety and Target Awareness framework with control barrier function filtering and occlusion-evasive local replanning (VISTA-CBF+ELR). The architecture combines visibility-risk planning, target-bearing control, an ELR supervisor, and a CBF quadratic program that keeps collision constraints hard while relaxing field-of-view and occlusion requirements through slack. Counterproductive interventions are limited through persistence, benefit–cost and feasibility gates, progress protection, bounded dwell, recovery, and cooldown. In locked factory simulations, redesigned VISTA achieved 67% and 73% strict-goal success under clean and nominal sensing, whereas Visibility-CEM-2D achieved 87% and 86% but with lower clearance. In matched Gazebo trials, strict success was 19/30 for redesigned VISTA, 26/30 without ELR, and 16/30 for Nav2 Smac+MPPI; zero-clearance collisions were 8/30, 3/30, and 14/30, with no difference surviving multiplicity correction. A separate CEM stress test sustained 6.875 Hz optimization, missed 26.31% of 100 ms deadlines, and held commands on 31.35% of ticks. The results demonstrate repair of the ELR pathology and conditional visibility-risk reduction while exposing safety–visibility trade-offs, transfer limitations, and real-time constraints. Full article

Canopy occlusion, dynamic vegetation, structural degeneracy, and implicit terrain risks make stable localization and task-adaptive planning difficult for resource-constrained orchard robots. This paper proposes CitraNav, a lightweight navigation method for global navigation satellite system (GNSS)-denied orchards. It separates stable geometric evidence for localization from short-lived semantic evidence for planning, preventing semantic observations from accumulating in the global map. For localization, a hierarchical voxel map selects its resolution according to local structure and light detection and ranging (LiDAR) sampling characteristics, while cross-frame reliability and observability constraints suppress updates from transient vegetation and weakly observable directions. For planning, synchronized color and depth observations form a local semantic risk point cloud. A model predictive path integral (MPPI) planner combines task-dependent semantic costs with exact-footprint collision checking against currently detected obstacles. In simulation, CitraNav achieved a mean translational localization root mean square error (RMSE) of 0.075 m. Compared with geometric point-cloud planning, semantic planning reduced the collision rate by 71.4% and increased weed coverage 4.72-fold. Across 14 real-world sequences spanning farm-road, lawn, forest, and orchard environments, CitraNav achieved mean translational and heading RMSEs of 0.151 m and 1.13°, respectively, while using 72.3–87.4% fewer geometric map cells than the comparison methods. The complete perception–planning pipeline operated at 20.3–32.7 frames per second on an edge platform. These results suggest that CitraNav offers a balanced approach to localization stability, task-adaptive planning, and computational efficiency in complex orchard navigation. Full article

This study proposes a novel hybrid interactive navigation framework for mobile robots, designed to enable robots to operate under dual constraints imposed by both the environment and the speech of accompanying humans in challenging future collaborative working scenarios. By leveraging human perceptual capabilities, the proposed framework significantly enhances the obstacle-avoidance capabilities and flexibility of robots. Specifically, an innovative composition algorithm is introduced to integrate traditional costmap-based navigation with a newly proposed LLM-assisted voice-based interaction method, thereby achieving real-time human–robot collaborative navigation with complementary advantages. Within this framework, robots can not only rely on spatial sensors to avoid obstacles but also follow verbal instructions from humans to bypass hazards that are difficult to detect. Moreover, the volume of speech is innovatively incorporated as a fusion weight, allowing the accompanying human to naturally guide the robot through voice volume modulation. To validate the feasibility and performance of the proposed framework and algorithm, we conducted both simulation and real-world experiments. A series of ablation and comparative studies was conducted to evaluate the merits and limitations of various configurations, ultimately providing optimal configurations based on the results. This work expands the scope of real-time human–robot interaction in navigation, offering new perspectives for future research. Full article

Sampling-based path planning is widely used in autonomous navigation, but Informed-RRT* still relies mainly on geometric ellipsoidal sampling and does not explicitly evaluate local obstacle risk, which can lead to invalid expansion, redundant nodes, and slow convergence. This paper proposes RC-Informed-RRT*, integrating risk-density-aware adaptive corridor sampling, improvement-bound rejection, and search-state-regulated goal bias. The corridor mechanism combines reference-path deviation, obstacle clearance, and local obstacle density; the rejection mechanism filters low-contribution nodes using an optimistic improvement bound; and the goal-bias strategy adapts target-oriented sampling to the search state. Comparative simulations were conducted in sparse, dense, narrow-passage, and W-shaped environments using 50 randomized trials per algorithm with small perturbations of the start/goal positions and obstacle locations. Relative to Informed-RRT*, RC-Informed-RRT* reduced mean planning time by 45.88–74.76% and final node count by 35.92–60.13%, while reducing final path length by 0.75–4.88% and maintaining 98–100% success rates. Sequential ablation and goal-bias sensitivity experiments further support the complementary roles of the three mechanisms and the fairness of the baseline parameter setting. Full article

Dynamic events require coordinated task allocation (TA) and path planning (PP) for multiple unmanned aerial vehicles (UAVs) to maintain executable mission progress. Existing coupled methods often use path information only as a precomputed cost or downstream refinement result, limiting its reuse after dynamic changes. This paper formulates dynamic task allocation and path planning (DTAPP) as a dynamic multi-objective optimization problem considering remaining target value, mission makespan, path feasibility, and execution-state inheritance. A three-role dynamic multi-swarm crow search algorithm (3R-DMCSA) is proposed, in which exploiter, explorer, and diversifier role-based swarms share a crow search-based update structure, feasibility-aware comparison, and leader-selection structure but use TA- and PP-specific encodings, objective preferences, initialization, and dynamic responses. A path matrix connects the layers by storing candidate paths and their attributes, which are fed back to TA, and supporting rolling-horizon leading flight-segment refinement. Experiments involving three dynamic urban scenarios compare the method with five baselines and evaluate its path-matrix feedback and rolling-horizon refinement. Compared with the strongest baseline, our approach improves mission-value acquisition by 10.6%, 16.2%, and 32.0% in the three scenarios, while maintaining near-complete target coverage and reliable flight-segment execution. Path-matrix feedback improves mission-value acquisition by 5.2–26.1% over the configuration without PP-to-TA path feedback, while rolling-horizon segment refinement reduces replanning latency by 48.8–70.5% compared with refining all planned segments without significantly compromising mission performance. Full article

Old urban areas face severe spatial supply–demand contradictions as aboveground space nears saturation while public service demand grows. Developing underground public spaces offers a key solution, yet fragmented development limits overall benefits, necessitating coordinated aboveground–underground development to achieve urban renewal and sustainability. Taking Shanghai’s old urban areas as a case, this study constructs an evaluation system with 17 aboveground indicators across 5 dimensions and 9 underground indicators across 3 dimensions. Using the combination of AHP–entropy weight method for weighting, the coupling coordination degree model, and the obstacle degree model, this study identifies the temporal evolution trends in the development levels of the two systems, the characteristics of their coupling coordination stages, and the main constraining factors from 1995 to 2025. The results show: (1) Both systems have shown continuous growth, with underground public space accelerating its development after 2010, and by 2015, it had nearly caught up with the aboveground system in the time-series projection results; (2) The D value of coupling coordination has increased from 0.2431 to 0.9532, experiencing three stages of low coupling coordination, general coupling coordination, and high coupling coordination; (3) The obstacle factors have shown a dynamic evolution path from scale shortage to morphological complexity, and then to the synergy of the aboveground and underground morphologies. In the higher coupling coordination stage, the length of the aboveground bus lines and the landscape shape index of the underground became the dominant obstacles. This study provides a quantitative basis for coordinated planning and decision-making in the renewal of old urban areas. Full article