Search Results (39)

The A* algorithm is widely used in mobile robot path planning; however, it faces challenges such as unsmooth planned paths, redundant nodes, and extensive search areas. This paper proposes a hybrid algorithm combining an improved A* algorithm with the Dynamic Window Approach. By quantifying grid obstacle data to extract environmental information and employing a grid-based environmental modeling method, the proposed approach enhances path smoothness at turns using second-order Bezier curve smoothing. It improves the heuristic function and child node selection process, applying these advancements in experimental path planning scenarios. A simulated 2D map was constructed using point cloud scanning in RViz to validate the hybrid algorithm through simulations and real-world outdoor tests. Experimental results demonstrate that, compared to the A* and DWA algorithms, the improved hybrid algorithm enhances search efficiency by 10.93%, reduces search node count by 32.26%, decreases the number of turning points by 36.36% and the value of turning angle by 34.83%, shortens the total path length by 22.05%, and improves overall path smoothness. Simulations and field tests confirm that the proposed hybrid algorithm is more stable, significantly reduces collision probability, and demonstrates its applicability for mobile robot localization and navigation in real-world environments. Full article

Safe path planning is essential for the autonomous operation of robotic roadheader in narrow underground tunnels, where limited perception and the robot’s geometric constraints present significant challenges. Traditional path planning methods often fail to address these issues. This paper proposes a collision prediction-integrated path planning method tailored for robotic roadheader in confined environments. The method comprises two components: collision prediction and path planning. A collision prediction model based on artificial potential fields is developed, considering the non-convex shape of the roadheader and enhancing scalability. By utilizing tunnel design information, a composite potential field model is created for both obstacles and the roadheader, enabling real-time collision forecasting. The A* algorithm is modified to incorporate the robot’s motion constraints, using a segmented weighted heuristic function based on collision predictions. Path smoothness is achieved through Bézier curve smoothing. Experimental results in both obstacle-free and obstacle-laden scenarios show that the proposed method outperforms traditional approaches in terms of computational efficiency, path length, and smoothness, ensuring safe, efficient navigation in narrow tunnels. Full article

With the increase in car ownership, traffic congestion, and frequent accidents, autonomous driving technology, especially for dynamic driving scenarios in the whole domain, has become a technological challenge for today’s researchers. Trajectory planning, as a crucial component of the autonomous driving technology framework, is gradually becoming a hot topic in intelligent research. In response to the challenges of planning lane-changing trajectories in complex dynamic driving scenarios under emergency evasive maneuvers, where it is difficult to consider surrounding vehicles and achieve dynamic adaptability, this paper proposes a dynamic adaptive trajectory planning method based on Bézier curves. Firstly, a mathematical model of Bézier curves is established and its curve characteristics are analyzed, which facilitates the correlation between the trajectory control points and the vehicle and the surrounding obstacles. Secondly, a mathematical function representing the Bézier curve is formulated, where the control points serve as the input and the lane-changing control curve as the output. Finally, the proposed method is validated through simulations on a jointly established simulation platform. The results indicate that the proposed method can plan lane-changing trajectories that are both safe and efficient under emergency evasive maneuvers, considering both static and complex dynamic conditions. This provides a novel solution for lane-changing trajectory planning in emergency evasive maneuvers for autonomous vehicles and holds significant theoretical research value. Full article

The static aeroelastic characteristics of the distributed propulsion wing (DPW) were studied using the CFD/CSD loose coupling method in this study. The momentum source method of the Reynolds-averaged Navier–Stokes equation based on the k-ω SST turbulence model solution was used as the CFD solution module. The upper and lower surfaces of the DPW were established using the cubic B-spline basis function method, and the surfaces of the inlet and outlet were established using the fourth-order Bezier curve. Finally, a three-dimensional parametric model of the DPW was established. A structural finite-element model of the DPW was established, a multipoint array method program based on the three-dimensional radial basis function (RBF) was written as a data exchange module to realize the aerodynamic and structural data exchange of the DPW’s static aeroelastic analysis process, and, finally, an aeroelastic analysis of the DPW was achieved. The results show that the convergence rate of the CFD/CSD loosely coupled method is fast, and the structural static aeroelastic deformation is mainly manifested as bending deformation and positive torsion deformation, which are typical static aeroelastic phenomena of the straight wing. Under the influence of static aeroelastic deformation, the increase in the lift characteristics of the DPW is mainly caused by the slipstream region of the lower surface and the non-slipstream region of the upper and lower surface. Meanwhile, the increase in its nose-up moment and the increase in the longitudinal static stability margin may have an impact on the longitudinal stability of the UAV. To meet the requirements of engineering applications, a rapid simulation method of equivalent airfoil, which can be applied to commercial software for analysis, was developed, and the effectiveness of the method was verified via comparison with the CFD/CSD loose coupling method. On this basis, the static aeroelastic characteristics of the UAV with DPWs were studied. The research results reveal the static aeroelastic characteristics of the DPW, which hold some significance for engineering guidance for this kind of aircraft. Full article

Nowadays, metaheuristic algorithms have been applied to optimize last lower-surface models. Also, the last lower-surface model has been adjusted through the computational algorithms to perform custom shoe lasts. Therefore, it is necessary to implement nature-inspired metaheuristic algorithms to perform the adjustment of last lower-surface model to the footprint topography. In this study, a metaheuristic genetic algorithm is implemented to adjust the last lower surface model to the footprint topography. The genetic algorithm is constructed through an objective function, which is defined through the last lower Bezier model and footprint topography, where a mean error function moves the last lower surface toward the footprint topography through the initial population. Also, the search space is deduced from the last lower surface and footprint topography. In this way, the genetic algorithm performs explorations and exploitations to optimize a Bezier surface model, which generates the adjusted last lower surface, where the surface is recovered via laser line scanning. Thus, the metaheuristic algorithm enhances the last lower-surface adjustment to improve the custom last manufacture. This contribution is elucidated by a discussion based on the proposed metaheuristic algorithm for surface model adjustment and the optimization methods implemented in recent years. Full article

Aiming at the problem that it is difficult for an orbital photovoltaic panel cleaning robot to span a large distance between photovoltaic panels, a method of designing and optimizing a non-coplanar orbit based on Bezier curves is proposed. Firstly, the robot’s motion law is analyzed to obtain trajectory data for a single work cycle. Then, Bezier curves are utilized for trajectory design to ensure a smooth transition during the spanning motion phase. Thirdly, with the average value of the minimum distance between the Bezier curve and the point set data of the spanning motion phase as the optimization objective function, the nonlinear planning based on the SQP algorithm was adopted for the optimization of the upper and lower trajectories. Finally, the results of the case calculations indicate that the standard deviation of the optimized upper and lower trajectories was reduced by 35.63% and 40.57%, respectively. Additionally, the ADAMS simulation validation demonstrates that the trajectory errors of the four wheels decreased by a maximum of 8.79 mm, 23.78 mm, 10.11 mm, and 14.97 mm, respectively, thereby confirming the effectiveness of the trajectory optimization. Full article

In this study, a new kind of modified $\lambda$-Bernstein-Stancu operators is constructed. Compared with the original $\lambda$-Bézier basis function, the newly operator basis function is more concise in form and has certain symmetry beauty. The moments and central moments are computed. A Korovkin-type approximation theorem is presented, and the degree of convergence is estimated with respect to the modulus of continuity, Peetre’s K-functional, and functions of the Lipschitz-type class. Moreover, the Voronovskaja type approximation theorem is examined. Finally, some numerical examples and graphics to show convergence are presented. Full article

With the rapid development of new-generation artificial intelligence and Internet of Things technology, mobile robot technology has been widely used in various fields. Among them, the autonomous path-planning technology of mobile robots is one of the cores for realizing their autonomous driving and obstacle avoidance. This study conducts an in-depth discussion on the real-time and dynamic obstacle avoidance capabilities of mobile robot path planning. First, we proposed a preprocessing method for obstacles in the grid map, focusing on the closed processing of the internal space of concave obstacles to ensure the feasibility of the path while effectively reducing the number of grid nodes searched by the A* algorithm, thereby improving path search efficiency. Secondly, in order to achieve static global path planning, this study adopts the A algorithm. However, in practice, algorithm A has problems such as a large number of node traversals, low search efficiency, redundant path nodes, and uneven turning angles. To solve these problems, we optimized the A* algorithm, focusing on optimizing the heuristic function and weight coefficient to reduce the number of node traversals and improve search efficiency. In addition, we use the Bezier curve method to smooth the path and remove redundant nodes, thereby reducing the turning angle. Then, in order to achieve dynamic local path planning, this study adopts the artificial potential field method. However, the artificial potential field method has the problems of unreachable target points and local minima. In order to solve these problems, we optimized the repulsion field so that the target point is at the lowest point of the global energy of the gravitational field and the repulsive field and eliminated the local optimal point. Finally, for the path-planning problem of mobile robots in dynamic environments, this study proposes a hybrid path-planning method based on a combination of the improved A* algorithm and the artificial potential field method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. By introducing symmetry, we can more intuitively judge whether the path is close to the optimal state. Symmetry is an important criterion for us to evaluate the performance of the final path. Full article

Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm utilizes a search window approach for population initialization, which improves the quality of the initial population. An innovative fitness function is designed as the objective function for optimization iterations. A probability-based selection strategy is employed for population selection and optimization, enhancing the algorithm’s ability to escape local minima and preventing premature convergence to suboptimal solutions. Furthermore, a path smoothing algorithm is developed by incorporating Bézier curves. By connecting multiple segments of Bézier curves, the problem of the high computational complexity associated with high-degree Bézier curves is addressed, while simultaneously achieving smooth paths. Simulation results demonstrated that the proposed path planning algorithm exhibited fewer iterations, superior path quality, and path smoothness. Compared to other methods, the proposed approach demonstrated better overall performance and practical applicability. Full article

In order to solve the problem of the low pickup efficiency of the robotic arm when harvesting safflower filaments, we established a pickup trajectory cycle and an improved velocity profile model for the harvest of safflower filaments according to the growth characteristics of safflower. Bezier curves were utilized to optimize the picking trajectory, mitigating the abrupt changes produced by the delta mechanism during operation. Furthermore, to overcome the slow convergence speed and the tendency of the ant colony algorithm to fall into local optima, a safflower harvesting trajectory planning method based on an ant colony genetic algorithm is proposed. This method includes enhancements through an adaptive adjustment mechanism, pheromone limitation, and the integration of optimized parameters from genetic algorithms. An optimization model with working time as the objective function was established in the MATLAB environment, and simulation experiments were conducted to optimize the trajectory using the designed ant colony genetic algorithm. The simulation results show that, compared to the basic ant colony algorithm, the path length with the ant colony genetic algorithm is reduced by 1.33% to 7.85%, and its convergence stability significantly surpasses that of the basic ant colony algorithm. Field tests demonstrate that, while maintaining an S-curve velocity, the ant colony genetic algorithm reduces the harvesting time by 28.25% to 35.18% compared to random harvesting and by 6.34% to 6.81% compared to the basic ant colony algorithm, significantly enhancing the picking efficiency of the safflower-harvesting robotic arm. Full article

Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents. Full article

This article introduces a finite element method based on the C-Bézier basis function for second-order elliptic equations. The trial function of the finite element method is set up using a combination of C-Bézier tensor product bases. One advantage of the C-Bézier basis is that it has a free shape parameter, which makes geometric modeling more convenience and flexible. The performance of the C-Bézier basis is searched for by studying three test examples. The numerical results demonstrate that this method is able to provide more accurate numerical approximations than the classical Lagrange basis. Full article

In order to improve the efficiency of agricultural machinery operations and reduce production costs, this article proposes a path planning algorithm based on the improved A* algorithm (IA*) and a tracking controller based on fuzzy sliding mode variable structure control (F-SMC) to meet the operation requirements of tracked agricultural machinery. Firstly, we introduce a heuristic function with variable weights, a penalty, and a fifth-order Bezier curve to make the generated path smoother. On this basis, the ant colony algorithm is introduced to further optimize the obtained path. Subsequently, based on fuzzy control theory and sliding mode variable structure control theory, we established a kinematic model for tracked agricultural machinery as the control object, designed a fuzzy sliding mode approaching law, and preprocessed it to reduce the time required for sliding mode control to reach the chosen stage. The simulation experiment of path planning shows that compared with A*, the average reduction rate of the path length for IA* is 5.51%, and the average reduction rate of the number of turning points is 39.01%. The path tracking simulation experiment shows that when the driving speed is set to 0.2 m/s, the adjustment time of the F-SMC controller is reduced by 0.99 s and 1.42 s compared to the FUZZY controller and PID controller, respectively. The variance analysis of the adjustment angle shows that the minimum variance of the F-SMC controller is 0.086, and the error converges to 0, proving that the vehicle trajectory is smoother and ultimately achieves path tracking. The field test results indicate that the path generated by the IA* algorithm can be tracked by the F-SMC controller in the actual environment. Compared to the A* algorithm and FUZZY controller, the path tracking time reduction rate of IA* and F-SMC is 29.34%, and the fuel consumption rate is reduced by 2.75%. This study is aimed at providing a feasible approach for improving the efficiency of tracked agricultural machinery operations, reducing emissions and operating costs. Full article

NURBS curve parameter interpolation is extensively employed in precision trajectory tasks for industrial robots due to its smoother performance compared to traditional linear or circular interpolation methods. The trajectory planning systems for industrial robots necessitate four essential functional modules: first, the spline curve discretization technique ensuring chord error compliance; second, the contour scanning technique for determining the maximum feasible feed rate for multi-constraint and multi-segment paths; third, the technique for achieving a smooth feed rate profile; and fourth, the continuous curve parameter interpolation technique. Therefore, this paper proposes a high-precision planar NURBS interpolation system for industrial robots. Firstly, a segmentation method for NURBS curves based on a closed-loop chord error constraint is proposed, which segments the original global NURBS curve into a collection of Bezier curves that strictly meet the chord error constraint. Secondly, a bidirectional scanning technique is presented to meet the joint space constraint, establishing an analytical mapping between the tool tip kinematic constraint and the joint kinematic constraint. Then, based on the traditional S-shaped feed rate profile, an adaptive algorithm with a displacement constraint is introduced, considering the real-time speed adjustment requirements of robots. Finally, a compensation interpolation strategy based on arc length parameterization is adopted to solve the accumulated error problem in parameter interpolation. The effectiveness of and potential for enhancing the quality of planar machining of the proposed planar NURBS interpolation system for industrial robots are validated through simulations and experiments. The results demonstrate the system’s applicability and accuracy, and its ability to improve planar machining quality. Full article

Electroaerodynamic unmanned aerial vehicles (EAD-UAVs) are innovative UAVs that use high-voltage asymmetric electrodes to ionize air molecules and Coulomb force to push these ions to produce thrust. Unlike fixed-wing and rotor UAVs, EAD-UAVs contain no moving surfaces and have the advantages of very low noise, low mechanical fatigue, and no carbon emissions. This paper proposes an EAD-UAV configuration with an orthogonal arrangement of multiple EAD thrusters to adjust the EAD-UAV attitude and flight trajectory through voltage distribution control alone. Based on a one-dimensional dynamic model of an EAD thruster, the attitude–path coupling dynamics of the EAD-UAV were derived. To achieve EAD-UAV flight control for a specified target, the Bezier shaping approach (BSA) was implemented to realize rapid trajectory optimization considering the coupling dynamic constraints. The numerical simulation results indicate that the BSA can quickly procure an optimized flight trajectory that satisfies the dynamic and boundary constraints. Compared with the Gaussian pseudospectral method (GPM), the BSA changes the optimization index of the objective function by nearly 1.14% but demands only nearly 1.95% of the computational time on average. Hence, the improved integrative Bezier shaping approach (IBSA) can overcome the poor convergence issue of the BSA under the continuous acceleration constraint of multi-target flight trajectories. Full article