Search Results (138)

To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article

The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article

This paper proposes a cluster-based flight path construction method for automated drone-assisted pear pollination systems in orchard environments. The approach uses RGB-D (Red-Green-Blue-Depth) sensing through an observation drone equipped with RGB and depth cameras to detect blooming pear flowers. Flower detection is performed using a YOLO (You Only Look Once)-based object detection algorithm, and three-dimensional flower positions are estimated by integrating depth information with the drone’s positional and orientation data in the east-north-up coordinate system. To enhance pollination efficiency, the method applies the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm to group detected flowers based on spatial proximity that correspond to branch-level distributions. The cluster centroids then construct a collision-free flight path, with offset vectors ensuring safe navigation and appropriate nozzle orientation for effective pollen spraying. Field experiments conducted using RTK-GNSS-based flight control confirmed the accuracy and stability of generated flight trajectories. The drone hovered in front of each flower cluster and performed uniform spraying along the planned path. The method achieved a fruit set rate of 62.1%, exceeding natural pollination at 53.6% and compared to the 61.9% of manual pollination. These results demonstrate the effectiveness and practicability of the method for real-world deployment in pear orchards. Full article

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a large payload in underwater scenarios. More precisely, by using the advantages of multi-UUV formation cooperation, based on rigidity graph theory and backstepping technology, the distance between each UUV, as well as the UUV and the transport payload, is controlled to form a three-dimensional rigid structure so that the load remains balanced and stable, to coordinate the transport of objects within the feasible area of the workspace. Moreover, a neural network (NN) is utilized to maintain system stability despite unknown nonlinearities and disturbances in the system dynamics. In addition, based on the interfered fluid flow algorithm, a collision-free motion trajectory was planned for formation systems. The control scheme also performs real-time formation reconfiguration according to the size and position of obstacles in space, thereby enhancing the flexibility of cooperative handling. The uniform ultimate boundedness of the formation distance errors is comprehensively demonstrated by utilizing the Lyapunov stability theory. Finally, the simulation results show that the UUVs can quickly form and maintain the desired formation, transport the payload along the planned trajectory to shuttle in multi-obstacle environments, verify the feasibility of the method proposed in this paper, and achieve the purpose of the collaborative transportation of large underwater payload by multiple UUVs and their targeted delivery. Full article

Trajectory tracking is a crucial task for autonomous mobile robots, requiring smooth and safe execution in dynamic environments. This study uses a nonlinear model predictive controller (MPC) to ensure accurate trajectory tracking of a four-wheel drive, four-wheel steer (4WD-4WS) mobile robot. However, the MPC’s performance depends on the optimal tuning of its key parameters, a challenge addressed using the Multivariate Gaussian Mixture Model Continuous Ant Colony Optimization (MGMM-${\mathrm{ACO}}_R$) algorithm. This method improves on the classic ${\mathrm{ACO}}_R$ algorithm by overcoming two major limitations: the lack of consideration for interdependencies between optimized variables, and an inadequate balance between global exploration and local exploitation. The proposed approach is validated by a two-phase evaluation. Firstly, benchmark function evaluations demonstrate its superiority over established optimization algorithms, including ACO, ${\mathrm{ACO}}_R$, and PSO and its variants, in terms of convergence speed and solution accuracy. Secondly, MGMM-${\mathrm{ACO}}_R$ is integrated into the MPC framework and tested in various scenarios, including trajectory tracking with circular and eight trajectories and dynamic obstacle avoidance during trajectory tracking. The results, evaluated based on trajectory error, control effort, and computational latency, confirm the robustness of the proposed method. In particular, the explicit modeling of correlations between variables in MGMM-${\mathrm{ACO}}_R$ guarantees stable, collision-free trajectory tracking, outperforming conventional ${\mathrm{ACO}}_R$-based approaches that optimize variables independently. Full article

This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, a mobile anchor trilateration and environmental modeling method is developed using a multi-UAV system by integrating the visual sensing capabilities of aerial surveillance UAVs with ultra-wideband technology. It constructs a real-time global 3D environmental model and provides precise positioning information, supporting autonomous planning and target guidance for near-ground UAV navigation. Secondly, based on real-time environmental perception, an improved D* Lite algorithm is employed to plan rapid and collision-free flight trajectories for near-ground navigation. This allows the UAV to autonomously execute collision-free movement from the initial position to the target position in complex environments. The results of real-world flight experiments demonstrate that the system can efficiently construct a global 3D environmental model in real time. It also provides accurate flight trajectories for the near-ground navigation of UAVs while delivering real-time positional updates during flight. The system enables UAVs to autonomously navigate in GPS-denied and unknown environments, and this work verifies the practicality and effectiveness of the proposed air–ground cooperative perception navigation system, as well as the mobile anchor trilateration and environmental modeling method. Full article

Recent advances in multiple autonomous underwater vehicles (AUVs) have highlighted formation control as a critical challenge for underwater collaborative operations. To address the inherent coupling between formation coordination and individual control in conventional approaches, this paper proposes a novel hierarchical framework of adaptive fixed-time formation control for multiple underactuated AUVs. This framework decouples AUVs’ formation requirements and individual control challenges into two distinct layers: the Collision-free Formation Trajectories Generation (CFTG) Layer and the Adaptive Trajectories Tracking (ATT) Layer. In the CFTG Layer, a consensus-based controller is developed to generate the desired trajectories for the AUVs to meet the requirements of complex formation tasks. And an improved artificial potential field method is proposed to ensure AUVs can reach the target point when the target is close to obstacles. In the ATT Layer, an auxiliary compensation system is designed to address the issue of input saturation. Furthermore, the adaptive fixed-time controllers are proposed to handle the uncertain parameters in the model, enabling underactuated AUVs to track the desired trajectory precisely. Both layers guarantee fixed-time convergence to increase the convergence speed. Simulations are conducted to demonstrate the effectiveness and better performance of the proposed method. Full article

This study develops an applied optimization method to address practical challenges in Laser Radar station planning for automotive Body-In-White (BIW) manufacturing inspection. Focusing on the spatially constrained industrial environments and complex measurement specifications, the work reformulates Laser Radar inspection planning as a multi-constrained optimization problem challenge. Firstly, a parametric geometric modeling approach is developed to define measurement spaces for individual features, accompanied by an innovative maximal complete subgraph mining algorithm to intelligently identify shared feasible measurement regions among multiple features. Secondly, kinematic equations are formulated using Denavit–Hartenberg (D-H) parameters, while a hierarchical bounding volume collision detection mechanism is integrated to establish a comprehensive constraint. Therefore, unified optimization method synergizing measurement coverage, robotic manipulator reachability, and operational safety requirements are proposed. Through experimental validations utilizing BIW (BIW) component inspection, the research has demonstrated its industrial applicability and has achieved a 92% measurement coverage with robot trajectories free of collisions. Compared with traditional manual planning methods, the proposed approach reduces the number of required inspection stations by 35% and improves the computational efficiency to meet industrial real-time deployment requirements. Experimental validation demonstrates the method’s effectiveness in measurement accuracy, operational safety, and equipment utilization for advanced manufacturing quality control systems. Full article

Deep Reinforcement Learning (DRL)-based algorithms have demonstrated substantial effectiveness in tackling complex control problems for autonomous underwater vehicles (AUVs). This paper attempts to evaluate reinforcement learning (RL)-based methods for AUV trajectory planning by incorporating a model of a vehicle’s full motion. In this study, the agent (AUV) is assumed to have no prior knowledge of the environment in which it navigates. Instead, it only receives inputs from navigation sensors and a simulated sonar. Additionally, in the article, a reward function is proposed and described, along with its optimization process, to elicit the desired behaviors in the underwater vehicle. The models are trained and tested on simplified electronic navigation chart (ENC) maps, followed by a comparative analysis against five effective classical methods for trajectory planning. The proposed solution enables efficient, collision-free route planning for the autonomous underwater vehicle, taking its motion dynamics into account to reach the designated target successfully. Full article

Aiming at the challenge of trajectory planning for a continuum manipulator in the confined spaces of gas-insulated switchgear (GIS) chambers during intelligent operation and maintenance of power equipment, this paper proposes a configuration space (C-space) obstacle-avoidance planning method based on an improved RRT-Connect algorithm. By constructing a virtual joint-space obstacle map, the collision-avoidance problem in Cartesian space is transformed into a joint-space path search problem, significantly reducing the computational burden of frequent inverse kinematics solutions inherent in traditional methods. Compared to the RRT-Connect algorithm, improvements in node expansion strategies and greedy optimization mechanisms effectively minimize redundant nodes and enhance path generation efficiency: the number of iterations is reduced by 68% and convergence speed is improved by 35%. Combined with polynomial-driven trajectory planning, the method successfully resolves and smoothens driving cable length variations, achieving efficient and stable control for both the end-effector and arm configuration of a dual-segment continuum manipulator. Simulation and experimental results demonstrate that the proposed algorithm rapidly generates collision-free arm configuration trajectories with high trajectory coincidence in typical GIS chamber scenarios, verifying its comprehensive advantages in real-time performance, safety, and motion smoothness. This work provides theoretical support for the application of continuum manipulator in precision operation and maintenance of power equipment. Full article

In the context of evolving Smart Cities, the integration of drone technology and distributed computing paradigms presents significant potential for enhancing urban infrastructure and services. This paper proposes a comprehensive approach to optimizing urban delivery logistics through a cloud-based model that employs Ant Colony Optimization (ACO) for planning and Model Predictive Control (MPC) for trajectory tracking within a broader Computing Continuum framework. The proposed system addresses the Capacitated Vehicle Routing Problem (CVRP) by considering both drone capacity constraints and autonomy, using the ACO-based algorithm to efficiently assign delivery destinations while minimizing travel distances. Collision-free paths are computed by using a Visibility Graph (VG) based approach, and MPC controllers enable drones to adapt to dynamic obstacles in real time. Additionally, this work explores how clusters of drones can be deployed as edge devices within the Computing Continuum, seamlessly integrating with IoT sensors and fog computing infrastructure to support various urban applications, such as traffic management, crowd monitoring, and infrastructure inspections. This dual-architecture approach, combining the optimization capabilities of ACO with the flexible, distributed nature of the Computing Continuum, allows for scalable and efficient urban drone deployment. Simulation results validate the effectiveness of the proposed model in enhancing delivery efficiency and collision avoidance while demonstrating the potential of integrating drone technology into Smart City environments for improved data collection and real-time response. Full article

Recent industrial production paradigms have seen the promotion of the outsourcing of low-value-added operations to robotic cells as a service, particularly end-of-line packaging. As a result, various types of research have emerged, offering different approaches to the trajectory design optimization of robotic manipulators and their applications. Over time, numerous improvements and updates have been made to the proposed methodologies, addressing the limitations and restrictions of earlier work. This survey-type article compiles research articles published in recent years that focus on the main algorithms proposed for addressing placement and minimum-time path planning for a manipulator responsible for performing pick-and-place tasks. Specifically, the research examines the construction of an automated robotic cell for the palletizing of regular heterogeneous boxes on a collision-free mixed pallet. By reviewing and synthesizing the most recent research, this article sheds light on the state-of-the-art manipulator planning algorithms for pick-and-place tasks in palletizing applications. Full article

In modern manufacturing, achieving high-speed laser cutting requires advanced robotic trajectory planning for smoothness and collision avoidance. Poorly optimized motion can cause frequent velocity changes, leading to mechanical vibrations that shorten machine service life. This study presents an innovative trajectory optimization approach for laser cutting machines equipped with a redundant standoff axis. A B-spline-based analytical model formulates rotational axes trajectories as quadratic programming problems to minimize jerk (the rate of acceleration change) under machining accuracy and kinematic constraints. Additionally, an M path, represented by the wrist center’s trajectory, refines translational axes by adjusting the standoff axis through a similar optimization model, thereby reducing mechanical stress. Collision avoidance is ensured through a concurrent iterative optimization process, considering the feasible domains of representative 3D geometric tool orientations. Simulation experiments on a complex B-pillar workpiece demonstrate the framework’s effectiveness, clearly indicating significant reductions in jerk and improved trajectory smoothness for both rotational and translational axes compared with conventional methods and a prior approach. This work advances high-speed machining capabilities by offering a novel, robust solution that leverages redundant structures to further improve trajectory smoothness and reliability in demanding industrial applications. Full article

This paper presents a complete system for autonomous navigation in GPS-denied environments using a minimal sensor suite that operates onboard a robotic vehicle. Our system utilizes a single camera and, given a target destination without prior knowledge of the environment, replans in real time to generate a collision-free trajectory that avoids static and dynamic obstacles. To achieve this, we introduce, for the first time, a local Euclidean Signed Distance Field (ESDF) map with variable size and resolution, which scales as a function of the vehicle’s velocity. The map is updated at a high rate, requiring minimal computational power. Additionally, a short-term vicinity-based memory is maintained for previously observed areas to facilitate smooth trajectory generation, addressing the limited field-of-view provided by the RGB-D camera. System validation is carried out by deploying our algorithm on a differential drive vehicle in both simulation and real-world experiments involving static and dynamic obstacles. We benchmark our robotic system against state-of-the-art autonomous navigation frameworks, successfully navigating to designated target locations while avoiding obstacles in both static and dynamic scenarios, all without introducing additional computational overhead. Our approach consistently achieves the target goals even in complex settings where current state-of-the-art methods may fall short. Full article

Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and complicating the generation of optimal flight trajectories. Although the slime mold algorithm (SMA) has proven effective in optimization missions, it still suffers from limitations such as inadequate exploration capacity, premature convergence, and a propensity to become stuck in local optima. These drawbacks degrade its performance in intricate multi-UAV scenarios. This study proposes a self-adaptive improved slime mold algorithm called AI-SMA to address these issues. Firstly, AI-SMA incorporates a novel search mechanism to balance exploration and exploitation by integrating ranking-based differential evolution (rank-DE). Then, a self-adaptive switch operator is introduced to increase population diversity in later iterations and avoid premature convergence. Finally, a self-adaptive perturbation strategy is implemented to provide an effective escape mechanism, facilitating faster convergence. Extensive experiments were conducted on the CEC 2017 benchmark test suite and multi-UAV path-planning scenarios. The results show that AI-SMA improves the quality of optimal fitness by approximately $7.83\%$ over the original SMA while demonstrating superior robustness and effectiveness in generating collision-free trajectories. Full article