Path Planning Model Research Articles

Research on personalized learning path planning model based on knowledge network

Research on personalized learning path planning model based on knowledge network

Path planning for spot welding robots based on improved ant colony algorithm

AbstractA welding path can be planned effectively for spot welding robots using the ant colony algorithm, but the initial parameters of the ant colony algorithm are usually selected through human experience, resulting in an unreasonable planned path. This paper combines the ant colony algorithm with the particle swarm algorithm and uses the particle swarm algorithm to train the initial parameters of the ant colony algorithm to plan an optimal path. Firstly, a mathematical model for spot welding path planning is established using the ant colony algorithm. Then, the particle swarm algorithm is introduced into the ant colony algorithm to find the optimal combination of parameters by treating the initial parameters $\alpha$ and $\beta$ of the ant colony algorithm and as two-dimensional coordinates in the particle swarm algorithm. Finally, the simulation analysis was carried out using MATLAB to obtain the paths of the improved ant colony algorithm for six different sets of parameters with an average path length of 10,357.7509 mm, but the average path length obtained by conventional algorithm was 10,830.8394 mm. Convergence analysis of the improved ant colony algorithm showed that the average number of iterations was 17. Therefore, the improved ant colony algorithm has higher solution quality and converges faster.

Robust Optimization Design for Path Planning of Bionic Robotic Fish in the Presence of Ocean Currents

The bionic robotic fish is one of the special autonomous underwater vehicles (AUV), whose path planning is crucial for many applications including underwater environment detection, archaeology, pipeline leak detection, and so on. However, the uncertain ocean currents increase the difficulty of path planning for bionic robotic fish in practice. In this paper, the path energy consumption is selected as the objective function for path planning, path safety factor, and smoothness are considered as the constraint conditions. The kinematic model is established for bionic robotic fish and, considering the uncertainty of ocean currents, a “min-max” robust optimization problem is proposed in the light of the normal optimization model of path planning for bionic robotic fish. The co-evolutionary genetic algorithm is presented to solve the robust optimization problem with two populations; one population represents the solutions and the other represents the uncertain ocean currents. The objective of the proposed algorithm is to find a robust solution that has the best worst-case performance over a set of possible ocean currents. Multiple experiments indicate that the proposed algorithm is very effective for path planning for bionic robotic fish with ocean currents.

Ship path planning based on safety potential field in inland rivers

With the growth of the shipping trade, inland water traffic is getting more prosperous than ever. Meanwhile, the shipping risk in inland rivers increases gradually. In this paper, a path planning model based on the safety potential field theory is proposed for inland ships from the perspective of navigation risk. Firstly, different navigation risks for inland ships are defined based on the safety potential field theory for three types of obstacles including static obstacles, dynamic obstacles and waterway bank walls. Secondly, different potential fields are used to quantify the different navigation risks. Then, the superposition field is formulated by summing the above three potential fields, and the spatial distribution of the safety potential field is obtained. Finally, the minimum navigation risk points on each cross section, which is perpendicular to the navigation direction, are selected to formulate the optimal path. In addition, a case study of overtaking scenario is conducted using the proposed model. The simulation results show that the model can effectively describe the navigation risk, which could be used to generate the optimal path for ships in inland rivers. This research is of great importance for ship navigation in inland rivers.

Temporal and spatial heat input regulation strategy for high-throughput micro-drilling based on multi-beam ultrafast laser

High-throughput laser drilling is crucial for the processing of large-area micro-hole arrays, but the local heat accumulation effect in the processing significantly limits the fabrication quality, especially for multi-beam laser micro-drilling. Although cooling methods such as water or airflow is applied to enhance heat dissipation, the heat input regulation is a convenient and effective way to improve the processing quality. In this work, a novel temporal and spatial heat input regulation strategy is proposed for multi-beam laser micro-drilling to effectively suppress the local heat accumulation during the processing. The strategy consists of a scanning path planning model and an optimization method of key drilling parameters. The scanning path planning model is established based on the temperature field of the workpiece, which is dynamically calculated using a transient finite-difference method (FDM). In addition, an optimization method is proposed to match the drilling parameters with the designated scanning path. Via this heat input regulation strategy, a more homogeneous temperature distribution can be obtained in the multi-beam laser drilling of micro-hole arrays. The maximum temperature and duration of overheating that causes material oxidation are decreased by 10%, and 50%, respectively. With the realization of high-quality (recast-free) and high-throughput (2430 micro-holes in 210 s, or 12 micro-holes/s), the proposed strategy showsa promising prospect in the multi-beam drilling of micro-hole arrays and other laser processing with possible heat accumulation, such as large-area surface texture and precise engraving of micro-nano structures.

Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Discrete Global Trajectory

Automatic lane-changing is a complex and critical task for autonomous vehicle control. Existing researches on autonomous vehicle technology mainly focus on avoiding obstacles; however, few studies have accounted for dynamic lane changing based on some certain assumptions, such as the lane-changing speed is constant or the terminal state is known in advance. In this study, a typical lane-changing scenario is developed with the consideration of preceding and lagging vehicles on the road. Based on the local trajectory generated by the global positioning system, a path planning model and a speed planning model are respectively established through the cubic polynomial interpolation. To guarantee the driving safety, passenger comfort and vehicle efficiency, a comprehensive trajectory optimization function is proposed according to the path planning model and speed planning model. In addition, a dynamic decoupling model is established to solve the problems of real-time application to provide viable solutions. The simulations and real vehicle validations are conducted, and the results highlight that the proposed method can generate a satisfactory lane-changing trajectory for automatic lane-changing actions.

Active obstacle avoidance method of autonomous vehicle based on improved artificial potential field

Aiming at the local minimum point problem in an artificial potential field based on a safe distance model, this article proposes an algorithm for active obstacle avoidance path planning and tracking of autonomous vehicles using an improved artificial potential field. First, a possible road operating condition in which the artificial potential field based on the safety-distance model falls into a local minimum point is studied. Subsequently, an improved artificial potential field method is proposed by introducing the second virtual target attraction potential field, which successfully overcomes the local minimum point problem. Second, a model for autonomous vehicle active obstacle avoidance path planning and tracking based on the improved artificial potential field is established. Finally, MATLAB/CarSim co-simulations were performed under the road conditions of constant- and variable-velocity obstacle vehicles. The simulation results demonstrate that the improved artificial potential field method can effectively solve the local minimum point problem of the artificial potential field based on the safe distance model. Additionally, the safety and stability of autonomous vehicle active obstacle avoidance are improved.

Modeling analysis of automatic parking path planning for wheeled AGV

Path planning is an important part of vehicle automatic parking. In this paper, the vehicle kinematics model is built based on the Ackerman steering principle, and the automatic parking path planning is explored. The starting conditions of one-step vertical parking are determined, the kinematic constraints are summarized, the parking tracking scheme is designed, and the parking path planning model is established. The parking path is fitted by B-spline curve to obtain the path optimization function. The parking space coordinate system is established according to the vertical parking space, and the parking path data obtained by simulation are input into the vehicle controller for one-step vertical parking test. The test results show that the error between the real vehicle test parking trajectory and the set trajectory is within the allowable range by using the parking path planning algorithm, indicating that the parking path planning strategy is effective.

Needle path planning in semiautonomous and teleoperated robot-assisted epidural anaesthesia procedure: A proof of concept.

Epidural anaesthesia is a Percutaneous Procedure (PP) which plays a crucial role in surgical procedures, where accurate needle insertion is still challenging. The objective of this work is to present a Tuohy needle path planning, which allows an anaesthesiologist to drive semiautonomously, with the assistance of a teleoperated robot, the tip of the needle during this PP. We capture, analysed and modelled the anaesthetist hands' motion during the execution of this procedure, by synthetising, programing and simulating a parametrised and normalised kinematic constrains dependent on an insertion variable in a virtual robot. Two preoperative path planning models were obtained, which provide a teleoperated robot with kinematic constraints to semiautonomously drive a Tuohy needle in the epidural anaesthesia procedure. A semiautonomous robot can assist in the execution of this PP using the kinematic constraints obtained from the study of the movement of a specialist's hands.

An Enhanced Artificial Electric Field Algorithm with Sine Cosine Mechanism for Logistics Distribution Vehicle Routing

Aiming at the scheduling problem of logistics distribution vehicles, an enhanced artificial electric field algorithm (SC-AEFA) based on the sine cosine mechanism is proposed. The development of the SC-AEFA was as follows. First, a map grid model for enterprise logistics distribution vehicle path planning was established. Then, an enhanced artificial electric field algorithm with the sine cosine mechanism was developed to simulate the logistics distribution vehicle scheduling, establish the logistics distribution vehicle movement law model, and plan the logistics distribution vehicle scheduling path. Finally, a distribution business named fresh enterprise A in the Fuzhou Strait Agricultural and Sideline Products Trading Market was selected to test the effectiveness of the method proposed. The theoretical proof and simulation test results show that the SC-AEFA has a good optimization ability and a strong path planning ability for enterprise logistics vehicle scheduling, which can improve the scheduling ability and efficiency of logistics distribution vehicles and save transportation costs.

Complete coverage path planning and performance factor analysis for autonomous bulldozer

AbstractWith the development of intelligent machinery, path planning of autonomous bulldozers plays an important role to solve the problems of efficiency in future construction sites. How to plan the path that meets the construction requirements is the key problem. This study investigates the standardized construction workmanship and moving rules of the bulldozer to add rules and path selection strategies to the algorithm. This study establishes the path planning model of bulldozer's leveling, considering the limitations of the traditional bioinspired neural network algorithm, an improved complete coverage path planning method is proposed. An integrated framework is proposed for an automatic bulldozer with a sensor system for a field experiment to verify the feasibility of the method. According to the comparative statistical simulation results, the effectiveness of the method is verified, and the effects of different factors on the path planning results are analyzed.

Autonomous, Digital-Twin Free Path Planning and Deployment for Robotic NDT: Introducing LPAS: Locate, Plan, Approach, Scan Using Low Cost Vision Sensors

Robotised Non Destructive Testing (NDT) presents multifaceted advantages, saving time and reducing repetitive manual workloads for highly skilled Ultrasonic Testing (UT) operators. Due to the requisite accuracy and reliability of the field, robotic NDT has traditionally relied on digital twins for complex path planning procedures enabling precise deployment of NDT equipment. This paper presents a multi-scale and collision-free path planning and implementation methodology enabling rapid deployment of robotised NDT with commercially available sensors. Novel algorithms are developed to plan paths over noisy and incomplete point clouds from low-cost sensors without the need for surface primitives. Further novelty is introduced in online path corrections utilising laser and force feedback while applying a Conformable-Wedge probe UT sensor. Finally, a novel source of data beneficial to automated NDT is introduced by collecting frictional forces of the surface informing the operator of the surface preparation quality. The culmination of this work is a new path-planning free, single-shot automated process removing the need for complex operator-driven procedures with a known surface, visualising collected data for the operator as a three-dimensional C-scan model. The dynamic robotic control enables a move to the industry 4.0 model of adaptive online path planning. Experimental results indicate the flexible and streamlined pipeline for robotic deployment, and demonstrate intuitive data visualisation to aid highly skilled operators in a wide field of industries.

Research on human-like lane-changing control of intelligent vehicles based on extension goodness evaluation

In order to improve the trajectory tracking accuracy and passenger comfort for intelligent vehicle lane-changing at medium and high speed, this paper proposes a new control algorithm based on extension evaluation system for intelligent vehicle lane-changing. In this research, firstly, a vehicle-road dynamics model has been developed. In addition, the actual driving trajectory data of skilled drivers are collected by driving simulator, and the lane-changing path planning model is established based on generalized regression neural network (GRNN). Last, in order to improve the tracking ability to the planning path, the trajectory tracking controller is established. A two-layer structure of lateral trajectory tracking control system is designed, in which the upper layer includes two algorithms: (1) PID feedback control based on preview deviation; (2) PID feedforward-feedback control based on the curvature of the path planning by skilled drivers. While the extension goodness evaluation is introduced in the lower layer which is used to evaluate two controllers in upper layer. According to the real-time state of vehicle-road system, the controller output with higher goodness is selected. Finally, the simulation results verify that comparing with the conventional single PID feedback control, the extension goodness evaluation control can effectively improve the passenger comfort while ensuring tracking accuracy.

Efficient Path Planning for Mobile Robot Based on Deep Deterministic Policy Gradient.

When a traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used in mobile robot path planning, due to the limited observable environment of mobile robots, the training efficiency of the path planning model is low, and the convergence speed is slow. In this paper, Long Short-Term Memory (LSTM) is introduced into the DDPG network, the former and current states of the mobile robot are combined to determine the actions of the robot, and a Batch Norm layer is added after each layer of the Actor network. At the same time, the reward function is optimized to guide the mobile robot to move faster towards the target point. In order to improve the learning efficiency, different normalization methods are used to normalize the distance and angle between the mobile robot and the target point, which are used as the input of the DDPG network model. When the model outputs the next action of the mobile robot, mixed noise composed of Gaussian noise and Ornstein–Uhlenbeck (OU) noise is added. Finally, the simulation environment built by a ROS system and a Gazebo platform is used for experiments. The results show that the proposed algorithm can accelerate the convergence speed of DDPG, improve the generalization ability of the path planning model and improve the efficiency and success rate of mobile robot path planning.

Optimization Model of Logistics Task Allocation Based on Genetic Algorithm

In order to improve the efficiency of logistics task allocation, the rationality and algorithm of the logistics cloud task scheduling model based on genetic algorithm are proposed in this paper. Firstly, the basic principle of genetic algorithm is introduced, the logistics cooperative distribution model is constructed, and the judgment mathematical model of the transfer point of the logistics distribution demand point is constructed. Genetic algorithm is used to solve the logistics distribution path planning model, and the model is simplified. The complex multiobjective optimization problem is transformed into a single-objective optimization problem through preference vector. The genetic algorithm and open-source algorithm on Python are used to simulate the model proposed in this paper. From the change curve of the objective function, after 100 generations of iteration, the value of objective function increases rapidly from 30 to 130 and slowly from generation 5 to generations 40 to 130. Subsequently, the 40th generation to 60th generation were rapidly upgraded to 160. Finally, the 60th to 100th generations are basically stable at about 170. The cost in the scheduling process decreases gradually with the increase of the number of iterations of the algorithm, from the initial unit cost of nearly 200 to 120. Then it gradually decreases to about 80. Genetic algorithm shows the ability of efficient and accurate solution in this 100-generation iteration. The genetic algorithm is used to solve the problem. The algorithm parameters are as follows: population size pop size = 300, maximum number of iterations max gen = 200, crossover probability PC = 0.8, and mutation probability PM = 0.1. Using the data in this paper and substituting it into the model established in this paper, the following distribution scheme is obtained: p the minimum distribution cost is 601.58 yuan, the distribution vehicle is 5, and the total mileage is 477.41. After using the algorithm to optimize the path, the path interleaving is greatly reduced, and the vehicles do not take the repeated route, which can greatly save the cost. After calculation, the total mileage after optimization is 74.8% lower than that before optimization, and the cost is significantly reduced by 72.8%. To sum up, the last kilometer distribution algorithm proposed in this paper can greatly reduce the cost of logistics resource scheduling, which has obvious research significance.

Path Planning Algorithm for Unmanned Surface Vehicle Based on Optimized Ant Colony Algorithm

In order to construct an efficient and reliable global path for unmanned surface vehicles (USVs), this paper proposes a USV global path planning algorithm based on an optimized ant colony algorithm (OACA). The algorithm constructs a USV global path planning model by comprehensively considering energy consumption costs and turning control costs. In order to ensure the adaptability of the model to the task environment, the USV's motion characteristics are analyzed with full consideration of external interference, and on this basis, the characterization of energy consumption costs and turning control costs is completed. In order to obtain the global optimal solution of the model and accelerate the convergence speed of the model, in the initialization process of the ant colony algorithm, the initial pheromone is distributed unevenly by introducing the distance relationship among the intermediate node, the starting point, and ending point to improve the initial search efficiency. In the iterative process of searching for the optimal solution, enhancing the guidance effect of the current optimal solution on the offspring ants by introducing a weight factor to improve the update rule of pheromone, which accelerates the convergence speed of the algorithm. In the last two steps of the deadlock path, the number of lost ants is reduced by introducing a penalty factor to punish the pheromone which can guarantee the diversity of ants, and the algorithm's premature convergence is overcome. The simulation results prove the effectiveness of this algorithm. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Path Optimization Algorithm for Multiple Unmanned Tractors in Peach Orchard Management

In order to improve the management efficiency of peach orchards, this paper considers the cooperative operation scheme of multiple unmanned tractors. According to the actual situation, this paper constructs the path planning model of multiple unmanned tractors in a standard peach orchard, designs the objective function to optimize the total turning time and total operating time according to the tractor driving parameters, and solves it by improving the differential evolution algorithm. Aiming at the premature convergence problem, the permutation matrix is introduced to represent the driving paths of multiple unmanned tractors. Then, the dynamic parameters are adopted to make the parameters change with the number of iterations, and the elite selection strategy is used to eliminate the redundant feasible solutions. An Adaptive Elite Differential Evolution (AEDE) algorithm suitable for multi-tractor path optimization is proposed. The results show that, compared with the traditional Differential Evolution algorithm (Differential Evolution, DE), the total turning time and total operating time in the rectangular peach orchard optimized by AEDE are reduced by 3.34% and 0.87%, respectively. Compared with the block operation, the total turning time and total operating time of the AEDE-optimized rectangular peach orchard operation path were reduced by 37.37% and 9.47%, respectively. Experiments show that AEDE, which optimizes the operating path of multi-tractors in standard peach orchards, is able to improve the efficiency and reduce the operating time.

Airport AGV path optimization model based on ant colony algorithm to optimize Dijkstra algorithm in urban systems

Nowadays, with the rapid development of intelligent logistics and Automated Guided Vehicles (AGV), the application scenario areas of AGVs are becoming more and more widely. This research takes the application of AGV in airport baggage loading scenarios as the research direction. Most of the airport AGV path planning does not capitalize on the time window of the early arrival of baggage, which leads to the prolonged running time of AGVs. To reduce the operating cost and path cost of AGV, this research proposed an airport AGV path optimization model based on the Dijkstra algorithm of ant colony optimization (ACO-DA). The model considered the environment with obstacles, first of all, the baggage pickup sequencing by ant colony optimization, and in the second place, the AGV path planning by integrating with Dijkstra’s algorithm. Last, the model was simulated and analyzed, and its proposed fusion algorithm performed better than the path planning models of the other three algorithms in airport baggage check-in, and its paths were respectively shortened by 2.3%, 2.64%, and 6.06%.

Vehicle-Assisted UAV Delivery Scheme Considering Energy Consumption for Instant Delivery.

Unmanned aerial vehicles (UAVs) are increasingly used in instant delivery scenarios. The combined delivery of vehicles and UAVs has many advantages compared to their respective separate delivery, which can greatly improve delivery efficiency. Although a few studies in the literature have explored the issue of vehicle-assisted UAV delivery, we did not find any studies on the scenario of an UAV serving several customers. This study aims to design a new vehicle-assisted UAV delivery solution that allows UAVs to serve multiple customers in a single take-off and takes energy consumption into account. A multi-UAV task allocation model and a vehicle path planning model were established to determine the task allocation of the UAVs as well as the path of UAVs and the vehicle, respectively. The model also considered the impact of changing the payload of the UAV on energy consumption, bringing the results closer to reality. Finally, a hybrid heuristic algorithm based on an improved K-means algorithm and ant colony optimization (ACO) was proposed to solve the problem, and the effectiveness of the scheme was proven by multi-scale experimental instances and comparative experiments.

Optimization of triage time and sample delivery path in health infrastructure to combat COVID-19

PurposeThis study presented the experience of improving the nucleic acid sample collection and transportation service in response to the epidemic. The main purpose is that through intelligent path planning, combined with the time scheduling of sample points, the process of obtaining results to determine the state of COVID-19 patients could be speeding up.Design/methodology/approachThe research optimized the process, including finding an optimal path to traverse all sample points in the hospital area via intelligent path planning method and standardizing the operation through the time sequence scheduling of each round of support staff to collect and send samples in the hospital area, so as to ensure the shortest time in each round. And the study examines these real-time experiments through retrospective examination.FindingsThe real-time experiments' data showed that the proposed path planning and scheduling model could provide a reliable reference for improving the efficiency of hospital logistics. Testing is a very important part of diagnosis and prompt results are essential. It shows the possibility of applying the shortest-path algorithms to optimize sample collection processes in the hospital and presents the case study that gives the expected outcomes of such a process.Originality/valueThe value of the study lies in the abstraction of a very practical and urgent problem into a TSP. Combining the ant colony algorithm with the genetic algorithm (ACAGA), the performance of path planning is improved. Under the intervention and guidance, the efficiency of hospital regional logistics planning was greatly improved, which may be of greater benefit to critical patients who must go through fever clinic during the epidemic. By detailing how to more rapidly obtain results through engineering method, the paper contributes ideas and plans for practitioners to use. The experience and lessons learned from Tongji Hospital are expected to provide guidance for supporting service measures in national public health infrastructure management and valuable reference for the development of hospitals in other countries or regions.