Search Results (13)

With the rapid development of applications such as smart cities and the industrial internet, the computation-intensive tasks generated by massive sensing devices pose significant challenges to traditional cloud computing paradigms. Unmanned aerial vehicle (UAV)-assisted edge computing systems, leveraging their high mobility and wide-area coverage capabilities, offer an innovative architecture for low-latency and highly reliable edge services. However, the practical deployment of such systems faces a highly complex multi-objective optimization problem featured by the tight coupling of task offloading decisions, UAV trajectory planning, and edge server resource allocation. Conventional optimization methods are difficult to adapt to the dynamic and high-dimensional characteristics of this problem, leading to suboptimal system performance. To address this critical challenge, this paper constructs an intelligent collaborative optimization framework for UAV-assisted edge computing systems and formulates the system quality of service (QoS) optimization problem as a mixed-integer non-convex programming problem with the dual objectives of minimizing task processing latency and reducing overall system energy consumption. A multi-agent joint task association and trajectory optimization (MA-JTATO) algorithm based on hybrid reinforcement learning is proposed to solve this intractable problem, which innovatively decouples the original coupled optimization problem into three interrelated subproblems and realizes their collaborative and efficient solution. Specifically, the Advantage Actor-Critic (A2C) algorithm is adopted to realize dynamic and optimal task association between UAVs and edge servers for discrete decision-making requirements; the multi-agent deep deterministic policy gradient (MADDPG) method is employed to achieve cooperative and energy-efficient trajectory planning for multiple UAVs to meet the needs of continuous control in dynamic environments; and convex optimization theory is applied to obtain a closed-form optimal solution for the efficient allocation of computational resources on edge servers. Simulation results demonstrate that the proposed MA-JTATO algorithm significantly outperforms traditional baseline algorithms in enhancing overall QoS, effectively validating the framework’s superior performance and robustness in dynamic and complex scenarios. Full article

Unmanned vehicles are becoming increasingly essential in executing high-risk missions in unknown environments such as search and rescue. As the complexity of operational environments escalates, carrying out unmanned tasks becomes cumbersome or even infeasible for a single vehicle, hampered by limited perception and operational constraints. Aiming at enhancing the flexibility of unmanned operations under complicated scenarios, this study introduces AGC-NeRF, an innovative air–ground collaborative exploration framework that harnesses the functional complementarity of UAVs and UGVs—enabling a UGV to navigate through a complex scenario with the assistance of a UAV via referencing a neural radiance map. First, a UAV is employed to collect aerial images for reconstructing the environment to be explored by a UGV, leveraging its aerial perspective to achieve wide-area coverage and global environmental perception that is unattainable for a single UGV. Concurrently, an innovative image saliency evaluation approach is introduced to meticulously select landmarks that are contributive to the UGV’s navigation system, yielding a pre-trained NeRF model of the operation scene. Then, a landmark-aware 6-DOF ego-motion estimator and collision-free trajectory optimizer are designed for the UGV based on the NeRF map. Finally, an online replanning architecture is established which relies on a ground station for NeRF training and state optimization by synergizing the trajectory planner and the state estimator, which forms a dual-agent vision-only navigation pipeline. Simulations and experiments validate that AGC-NeRF enables reliable UGV trajectory planning and state estimation in unknown environments, demonstrating superior efficacy and robustness of the air–ground collaborative paradigm. Full article

To address the collaborative optimization of efficiency, stability, and energy consumption in container part-handling operations of material-handling robots, this paper proposes a multi-objective trajectory-planning method. First, the kinematic and dynamic models of the robot are established based on the improved D-H parameter method and Lagrange method, with the coordinates of key interpolation points and joint angles in handling operations clarified. Subsequently, the 3-5-3 hybrid polynomial interpolation method is adopted to generate the trajectory. Optimizing the objectives of minimum time, minimum jerk, and minimum energy consumption, an improved particle swarm optimization (IPSO) algorithm dynamically adjusts the inertia weight and learning factor for trajectory optimization. The results show that the convergence speed of the IPSO algorithm increases by 39.6% on average, and the fitness value reduces by 12.7% on average. Experimental validation of joint trajectory optimization demonstrated maximum positional errors of approximately 0.0049 rad, 0.0005 rad, 0.005 rad, and 0.0049 rad for the four joints, with the experimental trajectory closely matching the planned trajectory. Finally, the effectiveness of the scheme is verified by MATLAB 2019 and Adams simulation. Under the time–jerk–energy optimization strategy, the joint trajectory is continuous and smooth, with the peak jerk reduced by 30–40% and the peak torque reduced by 5–10%. The comprehensive performance is superior to the single-objective and dual-objective optimization strategies. This research provides technical support for the efficient and stable operation of the handling robot and provides a reference for the trajectory planning of similar robots. Full article

To address the failure of unmanned aerial vehicle (UAV) target tracking caused by occlusion and limited field of view in dense low-altitude obstacle environments, this paper proposes a novel framework integrating occlusion-aware modeling and multi-UAV collaboration. A lightweight tracking model based on the Mamba backbone is developed, incorporating a Dilated Wavelet Receptive Field Enhancement Module (DWRFEM) to fuse multi-scale contextual features, significantly mitigating contour fragmentation and feature degradation under severe occlusion. A dual-branch feature optimization architecture is designed, combining the Distilled Tanh Activation with Context (DiTAC) activation function and Kolmogorov–Arnold Network (KAN) bottleneck layers to enhance discriminative feature representation. To overcome the limitations of single-UAV perception, a multi-UAV cooperative system is established. Ray intersection is employed to reduce localization uncertainty, while spherical sampling viewpoints are dynamically generated based on obstacle density. Safe trajectory planning is achieved using a Crested Porcupine Optimizer (CPO). Experiments on the Multi-Drone Multi-Target Tracking (MDMT) dataset demonstrate that the model achieves 84.1% average precision (AP) at 95 Frames Per Second (FPS), striking a favorable balance between speed and accuracy, making it suitable for edge deployment. Field tests with three collaborative UAVs show sustained target coverage in complex environments, outperforming traditional single-UAV approaches. This study provides a systematic solution for robust tracking in challenging low-altitude scenarios. Full article

Trajectory planning for logistics UAVs in complex environments faces a key challenge: balancing global search breadth with fine constraint accuracy. Traditional algorithms struggle to simultaneously manage large-scale exploration and complex constraints, and lack sufficient modeling capabilities for multi-UAV systems, limiting cluster logistics efficiency. To address these issues, we propose a GWOP algorithm based on dual-layer fusion of GWO and GRPO and incorporate a graph attention network (GAT). First, CEC2017 benchmark functions evaluate GWOP convergence accuracy and balanced exploration in multi-peak, high-dimensional environments. A hierarchical collaborative architecture, “GWO global coarse-grained search + GRPO local fine-tuning”, is used to overcome the limitations of single-algorithm frameworks. The GAT model constructs a dynamic “environment–UAV–task” association network, enabling environmental feature quantification and multi-constraint adaptation. A multi-factor objective function and constraints are integrated with multi-task cascading decoupling optimization to form a closed-loop collaborative optimization framework. Experimental results show that in single UAV scenarios, GWOP reduces flight cost (FV) by over 15.85% on average. In multi-UAV collaborative scenarios, average path length (APL), optimal path length (OPL), and FV are reduced by 4.08%, 14.08%, and 24.73%, respectively. In conclusion, the proposed method outperforms traditional approaches in path length, obstacle avoidance, and trajectory smoothness, offering a more efficient planning solution for smart logistics. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

To address energy management challenges for intelligent connected automated range-extended electric vehicles under vehicle-road cooperative environments, a hierarchical energy management strategy (EMS) based on speed prediction is proposed from the perspective of multi-objective optimization (MOO), with comprehensive system performance being significantly enhanced. Focusing on connected car-following scenarios, acceleration sequence prediction is performed based on Kalman filtering and preceding vehicle acceleration. A dual-layer optimization strategy is subsequently developed: in the upper layer, optimal speed curves are planned based on road network topology and preceding vehicle trajectories, while in the lower layer, coordinated multi-power source allocation is achieved through EMS_MPC-P, a Bayesian-optimized model predictive EMS based on Pontryagin’ s minimum principle (PMP). A MOO model is ultimately formulated to enhance comprehensive system performance. Simulation and bench test results demonstrate that with SoC₀ = 0.4, 7.69% and 5.13% improvement in fuel economy is achieved by EMS_MPC-P compared to the charge depleting-charge sustaining (CD-CS) method and the charge depleting-blend (CD-Blend) method. Travel time reductions of 62.2% and 58.7% are observed versus CD-CS and CD-Blend. Battery lifespan degradation is mitigated by 16.18% and 5.89% relative to CD-CS and CD-Blend, demonstrating the method’s marked advantages in improving traffic efficiency, safety, battery life maintenance, and fuel economy. This study not only establishes a technical paradigm with theoretical depth and engineering applicability for EMS, but also quantitatively reveals intrinsic mechanisms underlying long-term prediction accuracy enhancement through data analysis, providing critical guidance for future vehicle–road–cloud collaborative system development. Full article

In robot grinding tasks, dual manipulators possess improved flexibility, which can cooperate to complete different tasks with higher efficiency and satisfactory effect. In collaborative robot grinding tasks, the critical issues lie in the motion trajectory planning of the two manipulators and trajectory tracking with satisfactory accuracy under the condition that the two manipulator ends apply force on each other. In order to accomplish the goals in a more concise and feasible way, a complete scheme for dual-arm robot grinding tasks is essential. To address this issue, taking the motion trajectory planning and impedance control into consideration, a novel scheme for dual manipulators to complete collaborative grinding tasks is presented in this paper. To this end, a dual-arm grinding system is first constructed, and the kinematic constraints in the cooperative motion are analyzed, based on which the motion trajectories of the dual manipulators are planned according to the grinding task objectives. Then, an impedance controller is designed to achieve accurate tracking of the motion trajectory in the grinding process. Finally, dual-arm collaborative simulations and grinding experiments are carried out, and the results show that the proposed method can achieve good motion results and better flexibility compared to the single-arm motion, which demonstrates the effectiveness of the proposed method. Full article

The design and industrial innovation of intelligent agricultural machinery and equipment for saline alkali land are important means for comprehensive management and capacity improvement of saline alkali land. The autonomous and unmanned agricultural tractor is the inevitable trend of the development of intelligent machinery and equipment in saline alkali land. As an underactuated system with non-holonomic constraints, the independent trajectory planning and lateral stability control of the tractor-trailer system (TTS) face challenges in saline alkali land. In this study, based on the nonlinear underactuation characteristics of the TTS and the law of passive trailer steering, a dual-trajectory collaborative control model was designed. By solving the TTS kinematic/dynamic state space, a nonlinear leading system that can generate the reference pose of a tractor-trailer was constructed. Based on the intrinsic property of the lateral deviation of the TTS, a collaborative trajectory prediction algorithm that satisfies the time domain and system constraints is proposed. Combining the dual-trajectory independent offset and lateral stability parameter of the TTS, an energy function optimization control parameter was constructed to balance the system trajectory tracking performance and lateral control stability. The experimental results showed good agreement between the predicted trailer trajectory and the collaborative control trajectory, with an average lateral error not exceeding 0.1 m and an average course angle error not exceeding 0.054 rad. This ensures that the dynamic controller designed around the tractor-trailer underactuation system can guarantee the smoothness of the trailer trajectory and the controlling stability of the tractor in saline alkali land. Full article

As an essential application of unmanned aerial vehicle (UAV) systems, payload transportation has garnered significant attention in recent years. Collaborative payload transportation utilizing multiple UAVs can effectively increase the payload capacity of the transportation system. Nevertheless, the incorporation of multiple UAVs makes the dynamic model of the transportation system more complex due to the coupled UAV and payload states. In the immediate disaster relief response, the collaborative system is often required to promptly deliver supplies to the target site while avoiding obstacles to ensure the system’s safety. Consequently, devising fast delivery trajectories that avoid collisions for such complicated systems poses a considerable challenge. To this end, a novel trajectory planning method is presented for collaborative transportation systems. Specifically, the dynamic model of the collaborative transportation system is derived by utilizing the Euler–Lagrange method. Then, the trajectory planning problem is formulated as an optimization problem with considerations of dynamics, actuation, safety, and formation constraints. To expedite the optimization process, the collision avoidance safety constraint is constructed using dual variables. The efficacy of this trajectory planning approach is confirmed through multiple real-world flight experiments involving collaborative aerial transportation systems of two and three UAVs. Full article

Ensuring the safety of autonomous vehicles (AVs) in complex and high-risk traffic scenarios remains a critical unresolved challenge. Current AV planning methods exhibit limitations in generating robust driving trajectories that effectively avoid collisions, highlighting the urgent need for improved planning strategies to address these issues. This paper introduces a novel iterative optimization framework that incorporates safety-critical trajectory generation to enhance AV planning. The use of the HighD dataset, which is collected using the wide-area remote sensing capabilities of unmanned aerial vehicles (UAVs), is fundamental to the framework. Remote sensing enables large-scale real-time observation of traffic conditions, providing precise data on vehicle dynamics, road structures, and surrounding environments. To generate safety-critical trajectories, the decoder within the conditional variational auto-encoder (CVAE) is innovatively designed through a data-mechanism integration method, ensuring that these trajectories strictly adhere to vehicle kinematic constraints. Furthermore, two parallel CVAEs (Dual-CVAE) are trained collaboratively by a shared objective function to implicitly model the multi-vehicle interactions. Inspired by the concept of “learning to collide”, adversarial optimization is integrated into the Dual-CVAE (Adv. Dual-CVAE), facilitating efficient generation from normal to safety-critical trajectories. Building upon this, these generated trajectories are then incorporated into an iterative optimization framework, significantly enhancing the AV’s planning ability to avoid collisions. This framework decomposes the optimization process into stages, initially addressing normal trajectories and progressively tackling more safety-critical and collision trajectories. Finally, comparative case studies of enhancing AV planning are conducted and the simulation results demonstrate that the proposed method can efficiently enhance AV planning by generating safety-critical trajectories. Full article

Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established for the characteristic trajectories between the workpiece coordinate system and various other coordinate systems. Subsequently, the position and orientation curves of the working trajectory are discretized to facilitate the controller’s execution. Furthermore, an analysis is conducted of the closed-chain kinematics relationship between two arms of the robot and a pose-calibration method based on a reference coordinate system is introduced. Finally, constraints to the collaborative motion of the dual-arm robot are analyzed, leading to the establishment of a motion collaboration planning methodology. Simulations and experiments demonstrate that the proposed approach enables effective and collaborative task planning for dual-arm robots. Moreover, joint angle and angular velocity curves corresponding to the motion trajectory exhibit smoothness, reducing joint impacts. Full article

Intelligent manufacturing requires robots to adapt to increasingly complex tasks, and dual-arm cooperative operation can provide a more flexible and effective solution. Motion planning serves as a crucial foundation for dual-arm cooperative operation. The rapidly exploring random tree (RRT) algorithm based on random sampling has been widely used in high-dimensional manipulator path planning due to its probability completeness, handling of high-dimensional problems, scalability, and faster exploration speed compared with other planning methods. As a variant of RRT, the RRT*Smart algorithm introduces asymptotic optimality, improved sampling techniques, and better path optimization. However, existing research does not adequately address the cooperative motion planning requirements for dual manipulator arms in terms of sampling methods, path optimization, and dynamic adaptability. It also cannot handle dual-manipulator collaborative motion planning in dynamic scenarios. Therefore, in this paper, a novel motion planner named RRT*Smart-AD is proposed to ensure that the dual-arm robot satisfies obstacle avoidance constraints and dynamic characteristics in dynamic environments. This planner is capable of generating smooth motion trajectories that comply with differential constraints and physical collision constraints for a dual-arm robot. The proposed method includes several key components. First, a dynamic A* cost function sampling method, combined with an intelligent beacon sampling method, is introduced for sampling. A path-pruning strategy is employed to improve the computational efficiency. Strategies for dynamic region path repair and regrowth are also proposed to enhance adaptability in dynamic scenarios. Additionally, practical constraints such as maximum velocity, maximum acceleration, and collision constraints in robotic arm applications are analyzed. Particle swarm optimization (PSO) is utilized to optimize the motion trajectories by optimizing the parameters of quintic non-uniform rational B-splines (NURBSs). Static and dynamic simulation experiments verified that the RRT*Smart-AD algorithm for cooperative dynamic path planning of dual robotic arms outperformed biased RRT* and RRT*Smart. This method not only holds significant practical engineering significance for obstacle avoidance in dual-arm manipulators in intelligent factories but also provides a theoretical reference value for the path planning of other types of robots. Full article