Smooth Path Research Articles

A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints.

Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.

Apollo: Adaptive Polar Lattice-Based Local Obstacle Avoidance and Motion Planning for Automated Vehicles.

The motion planning module is the core module of the automated vehicle software system, which plays a key role in connecting its preceding element, i.e., the sensing module, and its following element, i.e., the control module. The design of an adaptive polar lattice-based local obstacle avoidance (APOLLO) algorithm proposed in this paper takes full account of the characteristics of the vehicle's sensing and control systems. The core of our approach mainly consists of three phases, i.e., the adaptive polar lattice-based local search space design, the collision-free path generation and the path smoothing. By adjusting a few parameters, the algorithm can be adapted to different driving environments and different kinds of vehicle chassis. Simulations show that the proposed method owns strong environmental adaptability and low computation complexity.

MDHO: Mayfly Deer Hunting Optimization Algorithm for Optimal Obstacle Avoidance Based Path Planning Using Mobile Robots

Mobile robot becomes more significant in human life and industry, whereas navigation of robot in the dynamic environment results a challenging problem and it need to be solved in an efficient way. Path planning gained more attention in recent decades and puts its practical usage in different industries. Path planning for the mobile robot is to determine feasible path to reach target location in workspace. A more challenging problem with mobile robot is solving path planning issue by avoiding obstacles in an optimize way. Various methods are designed to perform path planning mechanism, but it faced complexity in finding the solution to reach the target. Hence, an efficient Mayfly Deer Hunting Optimization (MDHO) algorithm is designed in this research to move the mobile robots to reach target location in the environment using multi-objective function. However, multi-objective function is designed by considering the factors, like path length, path smoothness, and the obstacle avoidance. The path that satisfies the objective constraints is selected as optimal path to reach the target of mobile robot. The proposed model attains minimum path length, maximal path smoothness, and maximum fitness as 1159.0 m, 0.913, and 3.5418 by considering fixed obstacles and multiple targets.

Smoothing and compressing algorithm of toolpath with complex contour in NC machining

The continuous short line segment processing method in CNC machining has the defect of frequent fluctuation of feedrate, which affects the quality and efficiency. For this problem, the local and global methods are common solutions. The local method is to insert transition curves at the corners to obtain a smooth toolpath, but this method does not essentially reduce the number of accelerations and decelerations, and it is difficult to insert transition curves between tiny straight sections. Although the global method can obtain smooth tool trajectories, the parametric interpolation method is not suitable for middle- and low-end CNC machine tools. The article proposes an adaptive tool path generation method with the original part contour as the research object and discretizes the NURBS curves into smooth trajectories consisting of large segments of straight lines and circular arcs. The article mimics the crawling characteristics of snakes and designs a double-headed snake algorithm based on the least squares method to implement this process. First, given the start and end points of the curve, two snakeheads search for the maximum linear and circular segments that satisfy the error constraint in the curve direction. The winner will then be selected as the candidate track segment through a competition mechanism. Finally, all trajectory segments are connected to obtain a smooth tool path. Experiments show that the toolpath data of the method in this paper are reduced by 75.56% compared with commercial CAM software when the contour error threshold is the same, and the contour error is reduced by more than one order of magnitude when the number of toolpath segments is equal. In addition, the method in this paper can obtain a smoother machining surface and more stable cutting force, thus achieving a win‒win situation of quality and efficiency.

Spatial Path Smoothing for Car-like Robots Using Corridor-Based Quadratic Optimization

The global planning module of car-like robots usually plans a coarse spatial path, which may be non-smooth and kinematically infeasible for car-like robots. This study proposes an efficient spatial path smoothing approach, which is capable of optimizing a rough spatial path to be a high quality one. Two novel designs contribute to the proposed approach. One is a direct corridor construction method that provides an optimization region for path optimization. Based on a redefined path representation in the generated corridor, the second one is a core spatial path optimization method, where the optimization problem is formulated as a multi-objective quadratic programming (QP) with corridor and maximum-curvature constraints. Meanwhile, integrating a two-steps strategy into an optimization process yields a good trade-off between efficiency and quality. Experiment results validate that the proposed approach has the ability to online generate a high quality path.

Event-triggered robust adaptive control for unmanned surface vehicle in presence of deception attacks

This article focuses on a double layer virtual ship guidance–based adaptive event–triggered path-following control for unmanned surface vehicle under the unknown actuator gain and deception attacks. In the guidance unit, the adaptive virtual ship is developed to derive the smooth reference paths for the controlled objective. That can effectively remove the abrupt phenomenon of actuators. Associated with the double layer virtual ship guidance strategy, a robust adaptive control algorithm is proposed for the unmanned surface vehicle by fusing the event-triggered rule and the adaptive compensated technique. In the algorithm, the deception attacks caused by the communication channel were compensated by constructing the attack adaptive parameters. That is critical to improve the accuracy and stabilize of the closed-loop control system. Besides, the transmission burden from the controller to the actuator was reduced for merits of the input-based event-triggered mechanism. Finally, the theoretical stability was proofed by the Lyapunov theorem, and the well performance was obtained through numerical simulation in presence of the time-varying disturbances.

Detection of underway gaps in rail systems by simulative and experimental methods

In this study, simulation and experimental studies have been carried out to determine the gaps formed under the rail line in rail transportation. In the simulations, where the analyzes are carried out with the finite element method, two different rail line conditions are designed for the normal and the defective lines. The displacement amounts and acceleration data in the vertical axis are examined for the normal and defective line conditions of the obtained vibration effect. The experimental conditions were designed in exact harmony with the simulative environment. A vehicle was moved at a constant speed of 0.112 m/s that has 1666 kg mass during the experiments. Vibration effects on the ground were monitored instantly with the acceleration sensor placed on the vehicle. In total, 4.425×10−3 m2 defected area is created under the certain points of rails. After the smooth and imperfect paths were examined both experimentally and simulatively, the results were presented comparatively in graphs. Although the gap is small, the difference between the normal and defective line reaches 61.8% for the displacement data in the simulation. In addition, 108.7% and 12.5% changes occurred, respectively when the simulation and experimental acceleration data were examined between faulty and the normal line. Eventually, the proposed method became a candidate to offer solutions for detecting underway gaps along the line.

Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography.

Myocardial ischemia/infarction causes wall-motion abnormalities in the left ventricle. Therefore, reliable motion estimation and strain analysis using 3D+time echocardiography for localization and characterization of myocardial injury is valuable for early detection and targeted interventions. Previous unsupervised cardiac motion tracking methods rely on heavily-weighted regularization functions to smooth out the noisy displacement fields in echocardiography. In this work, we present a Co-Attention Spatial Transformer Network (STN) for improved motion tracking and strain analysis in 3D echocardiography. Co-Attention STN aims to extract inter-frame dependent features between frames to improve the motion tracking in otherwise noisy 3D echocardiography images. We also propose a novel temporal constraint to further regularize the motion field to produce smooth and realistic cardiac displacement paths over time without prior assumptions on cardiac motion. Our experimental results on both synthetic and in vivo 3D echocardiography datasets demonstrate that our Co-Attention STN provides superior performance compared to existing methods. Strain analysis from Co-Attention STNs also correspond well with the matched SPECT perfusion maps, demonstrating the clinical utility for using 3D echocardiography for infarct localization.

Analysis of motor characteristics ofreaching movements in children with cerebral palsy

Studies confirm that children with cerebral palsy (CwCP) have difficulty with simple, everyday movements like reaching for objects. Accurate reaching requires that shoulder and elbow joints are coordinated to move the hand along a smooth path to the desired target location. Here we examined multijoint coordination by comparing reaching performance in the affected and unaffected limbs of CwCP (nine children, six girls and three boys, aged 8–10 years) to reaching performance in the non-dominant and dominant limbs of typically-developing age- and gender-matched control (CTR) children. The hypothesis was that CwCP would show the effects of coordination deficits in both their affected and unaffected limbs. All children performed two sessions (one session with each arm) of speeded reaching movements to three targets arranged to manipulate the required pattern of shoulder and elbow coordination. The movements were tracked with a motion tracker allowing us to assess the following measures: movement distance, duration, and speed, hand-path deviation from linearity, final position accuracy and precision, and measures of shoulder and elbow excursion. We found that CwCP made reaches that covered a greater distance and took more time, that their shoulder and elbow rotations were larger, and that their movements showed greater deviation from linearity than the movements performed by CTR children. Children with CP were also more variable than CTR children on every measure except movement duration. The pattern of shoulder and elbow rotation observed in the CwCP group represents a coordination pattern that is significantly different from the pattern used by CTR children and may represent a greater reliance by CwCP on proximal muscular control systems. The discussion section considers the role that the cortical-spinal system may play in multijoint coordination.

HG-SMA: hierarchical guided slime mould algorithm for smooth path planning

HG-SMA: hierarchical guided slime mould algorithm for smooth path planning

Joining Forces with Veterans: Veterans’ and Researchers’ Perspectives on Veteran-Centered Engagement Practices

Community-engaged research must reflect the uniqueness of the specific community involved. To help researchers produce quality community-engaged research with the veteran community, the authors of this paper (a coalition of both veterans and academic researchers) have highlighted essential considerations when engaging the veteran community in the research enterprise. Research with veterans requires unique sensitivity based on the impacts of their military service, history, and experiences. Understanding the impacts of veteran culture and community on recruitment is an essential prerequisite for anyone engaging with this population. At a minimum, community-engaged researchers should have an understanding of veteran history in relation to research, how veterans have experienced “volunteering” while under the chain of command, the impacts of a veteran’s military experience, and veterans’ views of the research process. The element most crucial to a successful research project conducted with the veteran community is incorporating veterans as full research team members. Building a veteran-centered research team requires academic researchers to establish trust with veteran team members and the veteran community, to conduct the research project with respect, and to actively encourage veterans’ participation in project activities. All of these are facilitated by having veterans as full members of a research team. It is our hope that sharing the lessons we have learned through working with veteran communities, as well as our lived experiences as veteran research team members, will help pave a smoother path forward for others wishing to conduct impactful veteran-centered research.

Efficient Tuning of the Opto-Electronic Properties of Sol-Gel-Synthesized Al-Doped Titania Nanoparticles for Perovskite Solar Cells and Functional Textiles.

The efficient electron transport layer (ETL) plays a critical role in the performance of perovskites solar cells (PSCs). Ideally, an unobstructed network with smooth channels for electron flow is required, which is lacking in the pristine TiO2-based ETL. As a potential solution, here we tuned the structure of TiO2 via optimized heteroatom doping of Al. Different concentrations (1, 2, and 3 wt%) of Al were doped in TiO2 and were successfully applied as an ETL in PSC using spin coating. A significant difference in the structural, opto-electronic, chemical, and electrical characteristics was observed in Al-doped TiO2 structures. The opto-electronic properties revealed that Al doping shifted the absorption spectra toward the visible range. Pure titania possesses a bandgap of 3.38 eV; however, after 1, 2, and 3% Al doping, the bandgap was linearly reduced to 3.29, 3.25, and 3.18 eV, respectively. In addition, higher light transmission was observed for Al-doped TiO2, which was due to the scattering effects of the interconnected porous morphology of doped-TiO2. Al-doped titania shows higher thermal stability and a 28% lower weight loss and can be operated at higher temperatures compared to undoped titania (weight loss 30%) due to the formation of stable states after Al doping. In addition, Al-doped TiO2 showed significantly high conductivity, which provides smooth paths for electron transport. Thanks to the effective tuning of band structure and morphology of Al-doped TiO2, a significant improvement in current densities, fill factor, and efficiency was observed in PSCs. The combined effect of better Jsc and FF renders higher efficiencies in Al-doped TiO2, as 1, 2, and 3% Al-doped TiO2 showed 12.5, 14.1, and 13.6% efficiency, respectively. Compared to undoped TiO2 with an efficiency of 10.3%, the optimized 2% Al doping increased the efficiency up to 14.1%. In addition, Al-doped TiO2 also showed improvements in antibacterial effects, required for photoactive textiles.

Experimental Study of Robotic Polishing Process for Complex Violin Surface

This paper presents a robotic polishing process for complex violin surfaces to increase efficiency and minimize the cost and consumed time caused by using labor and traditional polishing machines. The polishing process is implemented based on modeling a smooth path, controlled contact force embedded with gravity compensation and material removal depth. A cubic Non-Uniform Rational Bases-Spline (NURBS) interpolation curve combined with an S-curve trajectory model is used to generate a smooth polishing path on a complex violin surface to achieve stable motion during the polishing process. An online admittance controller added to the fast gravity compensation algorithm maintains an accurate polishing force for equal removal depth on all polished surface areas. Then, based on Pythagorean theory, the removal depth model is calculated for the violin’s complex surface before and after polishing to estimate the accuracy of the polishing process. Experimental studies were conducted by polishing a wooden surface using the 6DOF robot manipulator to validate this methodology. The experimental results demonstrated that the robot had accurate polishing force based on the online admittance controller with gravity compensation. It also showed a precise proportional uniformity of removal depths at the different normal forces of 10, 15, and 20 N. The final results indicated that the proposed experimental polishing approach is accurate and polishes complex surfaces effectively.

Path Planning and Smoothing for 4WDs Hydraulic Heavy-Duty Field Robots

This paper discusses the path planning and path-following control for a four wheel drive (4WD), steer-articulated boom lift driven by hydraulic actuators. The environment is assumed to be both static and known. The path planning will be done in two phases, where the first one finds a crude, collision-free path accounting for the vehicle dimensions, and this path will be smoothed with a path smoothing algorithm to satisfy the kinematic and dynamic constraints imposed by the vehicle and its actuators. The path smoothing algorithm will be chosen from several candidates by using a simulated test scenario. Then, the simulation results will be used to verify the path planners feasibility in heavy-duty, four-wheel-steered field robots having hydraulic actuators and high inertia.

A modified cuckoo search algorithm implemented with SCA and PSO for multi-robot cooperation and path planning

The paper puts forward an intelligent approach that deals with the computation of an optimal path with collision avoidance for the stick-carrying twin moving from a pre-assumed start position to a predefined goal position. It has been solved through the efficient implementation of modified cuckoo search, sine cosine algorithm, and particle swarm optimization to design a hybrid algorithm aimed at using the communal advantages of the search and position update ability of these algorithms. The benefits are realized by integrating the egg-laying behavior of the cuckoo species to achieve an efficient global search strategy with modified parameters, local search strategy of particle swarm optimization, and greedy approach of sine cosine algorithm. The proposed algorithm is validated using 10 standard benchmark functions, computer simulation using C language, and real robot platform using Epuck robot to illustrate minimal time, shortest distance, collision avoidance, path smoothness, synchronized action, and reduced energy usage in terms of the path traveled, execution time, the number of steps, and the number of turns in the static as well as the dynamic environment.

An Improved Bald Eagle Search Algorithm for Global Path Planning of Unmanned Vessel in Complicated Waterways

The path planning of unmanned ships in complex waters using heuristics usually suffers from problems such as being prone to fall into the local optimum, slow convergence, and instability in global path planning. Given this, this paper proposes a Self-Adaptive Hybrid Bald Eagle Search (SAHBES) Algorithm by incorporating adaptive factors into the traditional BES in order to enhance the early global searching ability of the BES algorithm. Moreover, Pigeon-Inspired Optimization (PIO) is introduced to overcome the disadvantage of traditional BES algorithms: that it is easy for them to fall into local optimization. This study improves the fitness function by adding a distance between the ships’ path corners. The obstacle is based on the calculation of the path length. The curve optimization module is applied to smooth the obtained path to generate more rational path planning results, which means the path is the shortest and avoids collision successfully. A simulation test of the SAHBES algorithm on the path planning under different obstacle scenarios is conducted by using the MATLAB platform. The results show that SAHBES can generate the shortest safe, smooth path in different complex water environments, considering the limitations of fundamental ship maneuvering operations compared to other algorithms, thus verifying the feasibility and efficiency of the proposed SAHBES algorithm.

Attitude path design and adaptive robust tracking control of a remote sensing satellite in various imaging modes

This paper addresses the attitude path design of a remote sensing satellite in various imaging modes. The novel path design algorithm is based on local polynomial regression, which produces a smooth attitude path by receiving the size and timing of maneuvers in each axis according to the desired imaging mode. This algorithm is described for stereo and snapshot modes in an imaging operation. The adaptive robust tracking control (ARTC) law is designed using quaternion algebra to perform the required maneuvers in an attitude path. The ARTC structure includes a sliding mode strategy, a projection-based adaptive model compensation, and a linear feedback term. Suitable conditions for imaging in each mode and the time of taking the image are determined by defining and evaluating the attitude control indices. These indices are defined by the half-cone error along the payload line of sight and relative performance error as a jitter with a 3-sigma confidence level. Despite the challenges in the path design, such as smoothness, system agility, and finite time realization of indices, in a conventional stereo imaging mode, taking an image in the nadir-pointing attitude is neglected. As a result, our work provides suitable conditions for taking the image in this attitude with an interval of 1.2 s. Finally, numerical simulations and verification are performed using multidisciplinary simulation, indicating the effectiveness of the proposed algorithm and model-based ARTC law.

Smooth Path Planning for Redundant Large-Scale Robots using Measured Reference Points

Assistance systems generating paths for large-scale robots automatically ensure comfort for the operator and save time compared to operation by hand levers. For an industrial use case, a previously saved path with multiple measurement points needs to be replayed on demand in a time efficient fashion. For this purpose, an existing algorithm for smooth path planning is extended by modifications to allow its application to redundant large-scale robots. Due to low computational effort, it is possible to implement the proposed algorithm on the control unit of the system online. The resulting path fulfills the requirement of being continuously differentiable and all actuator limits are respected. A numerical simulation shows the effectiveness of the proposed path planning algorithm. The smooth path of the tool center point position in world coordinates is still very close to the measurements to prevent collisions, but brings the opportunity to save time while reaching for the same target position.

An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle

Most of the existing studies developed and improved local path planning algorithms independently of global planning, i.e., ignoring the global optimal constrains. To meet the requirements of practical applications, this paper presented an energy-efficient hierarchical collision avoidance algorithm for unmanned surface vehicle operating in clustered dynamic environments. For the global level, genetic algorithm was modified by strategies of greedy-inspired population initialization, penalty-based multi-objective fitness function, and joint crossover. For the local level, velocity obstacle was combined with dynamic window approach to provide the kinematic constraints of the vehicle to its admissible velocities and simplified collision avoidance rules to guide the evasive maneuvers. Simulations showed that the proposed global algorithm was superior to three other algorithms in terms of path length, path smoothness, and convergence speed regardless of the environment size. The performance of the local algorithm was also verified for various encounter scenarios and speed ratios. In addition, the combination of the global and local planning can effectively solve the path optimization and dynamic obstacle avoidance in a designed offshore environment of fish cage culture.

Research on obstacle climbing gait structure design and gait control of hexapod wall climbing robot based on STM32F103 core controller

The hexapod wall climbing robots have the advantages of traversing complex wall surfaces. To traverse complex environments autonomously, it must possess the capability to select gait parameters and paths appropriate for the wall surface. Path planning and gait optimization is a fundamental issue in the aspect of stable, energy efficient robot navigation in complex environments with static and dynamic obstacles. Traditional statistical models have been developed to get the optimal path and gait parameters but the result obtained was very poor. Metaheuristic algorithms are gaining importance in robotic gait planning. In this paper, we proposed robust two stage gait planning approach for predicting collision-free, distance-minimal, smooth navigation path and ensuring stable, energy efficient gait patterns for robots using hybrid metaheuristic algorithms. In the first stage, optimal climbing path for robot is predicted using Tri-objective Grey Wolf Path Optimization (TGWPO) based on obstacle and target detection. In the second stage, the gait parameters adaptive to the constructed climbing path are optimized using Adaptive multi-objective Particle swarm optimization (AMPSO). The hexapod wall climbing robot is designed with STM32F103 as core controller modeled with optimal path planner (using TDWPO) and gait optimizer module (using AMPSO). STM32F103 controller commands and controls the robot to climb on wall with optimized gait parameters according to the optimal path. We analyzed the efficacy of the proposed two stage gait planning approach using TDWPO-AMPSO for hexapod wall climbing robots with existing gait planning approaches in terms of path length, climbing time, gait stability, obstacle avoidance, and energy efficiency. The result analysis showed that the suggested gait planning approach is efficient over conventional strategies for climbing robots.