Optimal Path Generation Research Articles

Robotic navigation with deep reinforcement learning in transthoracic echocardiography

Abstract Purpose The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart components. Method The proposed method introduces (i) an optimized search behavior generation algorithm that avoids multiple local solutions and searches for the optimal solution and (ii) an optimized path generation algorithm that minimizes the search path, thereby realizing short search times. Results The mitral valve search with the proposed method reaches the optimal solution with a probability of 74.4%, the mitral valve confidence loss rate when the local solution stops is 16.3% on average, and the inspection time with the generated path is 48.6 s on average, which is 56.6% of the time cost of the conventional method. Conclusion The results indicate that the proposed method improves the search efficiency, and the optimal location can be searched in many cases with the proposed method, and the loss rate of the confidence in the mitral valve was low even when a local solution rather than the optimal solution was reached. It is suggested that the proposed method enables accurate and quick robotic navigation to find heart components.

Intelligent switching gain based sliding mode control for optimization of power consumption in cooperative manipulators

AbstractCooperative manipulators, vital for intricate tasks, are gaining widespread attention across industries. Recognizing their impact on power consumption, costs, and task outcomes, this paper emphasizes the critical study of control methods, actuator power consumption, and manipulator accuracy. Addressing these challenges, we propose a neural network‐sliding mode controller (NN‐SMC) to optimize actuator power consumption and minimize errors in cooperative manipulators. Operating in trajectory and point‐to‐point modes, the NN‐SMC dynamically generates real‐time switching mode controller (SMC) gains (L and K) for precise control. Stability is ensured by maintaining gains within permissible ranges. In point‐to‐point mode, the NN orchestrates an optimal path generation, along with tailored gains. To evaluate performance accurately, a novel control performance index is introduced. Experimental results on 3‐DOF cooperative manipulators demonstrate a remarkable 28% increase in the control performance index for the trajectory mode and a substantial reduction in computational complexity for both modes. This work not only addresses inherent challenges in cooperative manipulators but also signifies a methodological advancement through the integration of neural network‐based control, promising enhanced efficiency and stability.

New Design of an Electrical Excavator and Its Path Generation for Energy Saving and Obstacle Avoidance

This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save energy by reducing energy consumption, improve the digging accuracy by minimizing the deviation between the desired and dug surfaces of the ground, and prevent colliding with subsurface objects. For this purpose, computer vision was employed to improve monitoring and verification. Five types of experiments were carried out in this investigation. The first two and the other three examined the impact of energy conservation in PSO- and PFM-based path generation, respectively. Finally, the results from these experiments were compared to identify and show the effect of optimal path generation.

Learning and generalization of task-parameterized skills through few human demonstrations

In the field of robotics, the demand for adaptable skills capable of effectively handling diverse situations has surpassed the reliance on repetitive tasks. To enhance the generalization of motion policies, task-parameterized learning has emerged as a valuable approach, as it encodes pertinent contextual information in task parameters, facilitating the flexible execution of tasks. This process requires the collection of multiple demonstrations in various situations. Generate a set of diverse situations covering all possible cases is a complex task. Due to this, the search for training with fewer demonstrations becomes highly desirable. In this article, we present a novel algorithm focused on generating synthetic information to facilitate the generalization of parameterized tasks. This algorithm makes use of Kinesthetic Fast Marching Learning, a Learning from Demonstration (LfD) algorithm that allows obtaining optimal movement paths based on velocity fields. The algorithm enables autonomous data generation, producing demonstrations that are equal to or more optimal than those generated by users themselves. Evaluation is done through a metric based on Wasserstein distance that takes into account the probabilistic data of the generated paths. The algorithm has been evaluated through tests in simulated environments, comparing its efficiency against two widely used algorithms (where it has shown greater efficiency in generalization and the generation of more optimal paths) and with real-world tests in a task-oriented environment (sweeping) carried out by the ADAM robot.

Local Path Planning for Nonholonomic Mobile Robots Based on Planar G2 Pythagorean Hodograph Degree 7 Bezier Transition Curves

The paper proposes a method that joins two line segments using planar Pythagorean hodograph (PH) degree 7 Bezier transition curves. The objective is to make use of such curvature-continuous curves for the path planning of nonholonomic constrained vehicles moving in 2D space, which requires designing a smooth path while satisfying the constraint of minimum turning radius. We initially present a novel idea that employs PH degree 7 Bezier transition curves for dealing with curvature constraints, the result of which proves to be better than the traditional method based on the “forward simulation”. The properties of PH degree 7 Bezier transition curves are then fully analyzed showing that the curves can be determined by a minimum of three parameters, each of which has an intuitive geometric meaning. The paper also proposes a path planner with direct curvature constraints during the process of path generation in 2D space. The objective function of the optimal path generation can be adjusted to minimize the length of the whole path or to closely follow the original global nonsmooth sequences of line segments. Here, the optimization problem is tactfully converted to a linear programming problem that can be solved efficiently. Finally, we use a kinematic bicycle model for simulation and compare the results with those from Dubins path showing that a [Formula: see text] curve is easier to follow than a [Formula: see text] one.

Automatic Methods for Ultrasonic Scanning Paths Generation

This paper presents the last evolutions of a program developed in 2020 for the automated generation of optimal scanning paths for 0°LW ultrasonic inspections of complex parts. After summarizing the overall concepts of the path generation method, the paper presents two new algorithms (side following path and side/radius interpolating path) recently developed for specific geometries such as long and thin parts, or parts including nonparallel radii. Trajectories automatically generated by the software are then presented for a collection of various aerospace and industrial CFRP components. The results show that the software was able to generate optimal paths for all the tested geometries, using the part’s 3D surface CAD and the probe’s characteristics as only inputs. In particular, the addition of the new algorithms allows for the generation of satisfactory trajectories even for the parts that were previously problematic.

Development of Autonomous Driving and Motion Control System for a Patient Transfer Robot

In this study, an autonomous driving system of a patient-transfer robot is developed. The developed autonomous driving system has a path-planning module and a motion-control module. Since the developed autonomous driving system is applied to medical robots, such as patient-transfer robots, the main purpose of this study is to generate an optimal path for the robot’s movement and to ensure the patient on board moves comfortably in the PTR. In particular, for the patient’s comfortable movement, a lower controller is needed to minimize the sway angle of the patient. In this paper, we propose a hybrid path-planning algorithm that combines the A-STAR algorithm as a global path-planning method and the AHP (Analytic Hierarchy Process)-based path-planning algorithm as a local path-planning method. In addition, model-based controllers are designed to move patient-transport robots along planned paths. In particular, the LQR controller with the Kalman filter is designed to be robust to the uncertainty and disturbance of the model including the patient. The optimal path generation and patient shaking angle reduction performance of the proposed autonomous driving system have been demonstrated via a simulation on a map that mimics a hospital environment.

Generation of Optimal Load Paths for Corroded Reinforced Concrete Beams Part II: Multi-Angle Truss Model

Generation of Optimal Load Paths for Corroded Reinforced Concrete Beams Part II: Multi-Angle Truss Model

Generation of Optimal Load Paths for Corroded Reinforced Concrete Beams Part I: Automatic Stiffness Adjustment Technique

Generation of Optimal Load Paths for Corroded Reinforced Concrete Beams Part I: Automatic Stiffness Adjustment Technique

Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor

As autonomous tractors become more common crop harvesting applications, the need to optimize the global servicing path becomes crucial for maximizing efficiency and crop yield. In recent years, several methods of path generation have been researched, but very few have studied their applications on complex field shapes. In this study, a method of creating the optimal servicing path for simple and complex field shapes is proposed. The proposed algorithm creates subfields for a target land, optimizes the track direction for several subfields individually, merges subfields that result in overall increased efficiency, and finds the minimum non-operating paths to travel from subfield to subfield while selecting the respective optimal subfield starting locations. Additionally, it is required that this process must be done within 3 seconds to meet performance requirements. Results from 3 separate field shapes show that the field traversal efficiency can range from 68.0% to 94.4%, and the coverage ratio can range from 98.8% to 99.9% for several different conditions. In comparison with previous studies using the same field shape, the proposed methods demonstrate an increase of 5.5% in field traversal efficiency.

Control and Path Planning of Mobile Swarm Robots Using Blockchain Technology with Particle Swarm Optimization

Technological advancement has made robots become rampant in industrial automation and globalization, as it’s fast and efficient in delivering tasks in industries with no supervision. This work shows the use of blockchain technology (smart contract) in controlling a swarm of robots combined with particle swarm optimization for solving the navigation path. The technique in this research modeled a new fitness function, that uses the optimal path generation and barrier avoidance for the mobile robot’s movement within the swarm, while the blockchain smart contract was integrated to control the robot’s speed. Simulated results validate path optimization and speed control with PSO and BT.

UAV path planning and collision avoidance in 3D environments based on POMPD and improved grey wolf optimizer

Due to the complexity and uncertain factors of the environment, a 3D path planning algorithm is urgently needed. This paper presents a 3D optimal feasible flight path generation and collision avoidance algorithms based on partially observable Markov decision process (POMDP) and improved grey wolf optimizer (GWO) for an unmanned aerial vehicle (UAV). Firstly, a novel algorithm based on the GWO is proposed to deal with constrained optimization problem (COP) and utilized to plan a flyable path. The designed variant is called improved GWO with level comparison (GWOLC), which combines the communication mechanism and the ε-level comparison method at the same time. Secondly, aircraft collision avoidance is modeled as a Partially Observable Markov Decision Process (POMDP) and the Monte-Carlo tree search (MCTS) algorithm is used to solve it. We introduce a novel algorithm, Information Particle Filter Tree (IPFT), to solve the problem of belief update in continuous domain. Thirdly, simulation experiments are conducted in 3D environment, and numerical results showed the proposed algorithm offers good performance as measured by effectiveness, robustness, convergence, and constraint handling capabilities.

A novel genetic algorithm for curriculum sequence optimization

A curriculum sequence represents a match between learners’ preferences, needs, and surroundings from one side, and the learning content characteristics and the pedagogical requirements from the other side. The curriculum sequence adaptation problem (CSA) is considered as an important issue in adaptive and personalized learning field. It concerns the dynamic generation of a personal optimal learning path for a specific learner. This problem has gained an increased research interest in the last decade, and heuristics and meta-heuristics are usually used to solve it. In this direction, this paper summarizes existing works and presents a novel GA-based approach modeled as an objective optimization problem to deal with this problem. The experimental results from simulations showed that the proposed GA could outperform particle swarm optimization (PSO) and a random search approach in many simulated datasets. Moreover, from a pedagogical perspective, positive learners’ feedback and high acceptance towards the proposed approach is indicated.

CARE: A Collision-Aware Mobile Robot Navigation in Grid Environment using Improved Breadth First Search

In recent years, there is an increasing interest in designing autonomous vehicles such as Mobile Robots. However, one of the fundamental needs of Mobile Robots is a collision-free navigation with an optimal path from the source to the destination. In this paper, a collision-free low-complexity Mobile Robot navigation scheme called Collision Aware Mobile Robot navigation in Grid-Environment is designed. The proposed scheme uses the Radio Frequency based Identification method for Mobile Robot localization, the hybrid approach for the path planning, and a predefined decision table for the navigation. The algorithms are implemented in two stages, construction of virtual world and generation of optimal shortest path. The algorithms’ performance is analyzed in grid-based environment of size 5×5,20×20,35×35, and 50 × 50 with four different cases. It is observed that for the environment with no obstacles, the robot explores fewer cells in finding the shortest path. However, the number of turning in the shortest path is always less in the environment with some obstacles than in the case of whole virtual world exploration. The performance results show the effectiveness of the proposed scheme for Mobile Robot navigation in the grid environment.

IADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

Generation of Optimal Coverage Paths for Mobile Robots Using Hybrid Genetic Algorithm

This paper presents new optimal offline approaches to solve the coverage path planning problem. A novel hybrid genetic algorithm (HGA), which uses, the turn-away starting point and backtracking spiral algorithms for performing local search, is proposed for grid-based environmental representations. The HGA algorithm is validated using the following three different fitness functions: the number of cell visits, traveling time, and a new energy fitness function based on experimentally acquired energy values of fundamental motions. Computational results show that compared to conventional methods, HGA improves paths up to 38.4%; moreover, HGAs have a consistent fitness for different starting positions in an environment. Furthermore, experimental results prove the validity of the fitness function.

The Depth-First Optimal Strategy Path Generation Algorithm for Passengers in a Metro Network

Passenger behavior analysis is a key issue in passenger assignment research, in which the path choice is a fundamental component. A highly complex transit network offers multiple paths for each origin–destination (OD) pair and thus resulting in more flexible choices for each passenger. To reflect a passenger’s flexible choice for the transit network, the optimal strategy was proposed by other researchers to determine passenger choice behavior. However, only strategy links have been searched in the optimal strategy algorithm and these links cannot complete the whole path. To determine the paths for each OD pair, this study proposes the depth-first path generation algorithm, in which a strategy node concept is newly defined. The proposed algorithm was applied to the Beijing metro network. The results show that, in comparison to the shortest path and the K-shortest path analysis, the proposed depth-first optimal strategy path generation algorithm better represents the passenger behavior more reliably and flexibly.

Optimal path generation in scala tympani and path planning for robotic cochlear implant of perimodiolar electrode.

In this study, a new idea of the optimal path generation method was proposed and a path planning strategy for robotic cochlear implant of perimodiolar electrode was designed. The centerline of scala tympani channel was taken as the optimal implant path of the perimodiolar electrode, which aimed to reduce the damage of the electrode to the cochlea during implantation. First, the three-dimensional cochlear model was reconstructed based on the micro-computed tomography images of cochlea, and it was re-segmented to obtain the cross sections of the scala tympani at different angles. Then, the image processing method was used to determine the central point of the scala tympani cross sections. The cubic B-spline interpolation method was used to fit these discrete central points to generate the optimal path. Finally, the coordinate information of the optimal path was combined with the stylet extraction state of perimodiolar electrode to conduct the path planning for robotic cochlear implant, and the result was sent to the robot for kinematic inverse solution to obtain the robot motion trajectory. The robotic cochlear implant experiment was performed with the model of scala tympani. The results showed that the maximum implant force based on path planning was 0.084 N, and the maximum implant force without path planning was 0.134 N. The optimal path generation and the path planning method effectively help to reduce the damage of the electrode to the cochlea.

An Algorithm for Finding the Optimal Path In Basis Path Testing using GABVIE Model

Software testing is one of the most vital factors in software development life cycle. It is mainly used for testing the program code, known as white box testing and to test the functionality of the program, known as black box testing. Manual generation of test data is very costly, error vulnerable and time consuming task. Subsequently, there is a need to make the process automated as could be expected under the circumstances. This paper presented the automated generation of optimal path with intention of attaining the maximum coverage. The work being done considers the optimal path coverage in minimum cost. The task of generating test cases can be done through the concept Genetic Algorithm with importance of variable (GABVIE). The proposed algorithm additionally considers programs having numerous modules. This is vital as a large portion of the current test data generators not succeeded to establish the communication between the modules. The approach has been implemented on various program code and the outcomes got have been confirmed. The proposed work considers the white box testing.

Density-weighted ant colony algorithm for ship trajectory reconstruction

With the development of the international ocean shipping and the increase in the automatic identification system-receiving equipment, the availability of ship trajectory data has shown explosive growth. To reduce the uncertainty in the application of sparse ship trajectory data in the open sea, this paper proposes the use of an ant colony algorithm based on node weights and edge weights for ship trajectory reconstruction. Ship trajectory is reconstructed from independent trajectory points through prior knowledge base construction, solving candidate path sets and optimal path generation. In addition, in order to further experiment the proposed method for the reconstruction of trajectory in the large-scale, this paper also uses the reconstructed trajectory to extract the distribution of ship traffic flow in the study area. The experimental results show that, the proposed trajectory reconstruction method can reconstruct ship trajectories in open seas more accurately.