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Intelligent Control and Robotic System in Path Planning

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A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Robotics".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 30135

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Special Issue Editor

Prof. Dr. Jih-Gau Juang

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Guest Editor

National Taiwan Ocean University, Taiwan
Interests: artificial intelligence; intelligent control; vehicle control; robotic systems

Special Issue Information

Dear Colleagues,

In the past decade, intelligent systems has become one of the most used design methods for path planning. Its solution provides a feasible collision-free geometric path from an initial to a final point, passing through pre-defined via-points. To perform optimal operation, a vehicle or a robot must be equipped with suitable high-level intelligence capabilities. Recently, path planning has been applied to complex environment, whether it is known or unknown. For unknown environments, systems that are capable of SLAM can use optimum coverage path planning approaches to achieve systematic coverage of the entire free space. Some common global path-planning algorithms include rapidly-exploring random trees and graph search algorithms. Examples include A* and D* algorithms, optimization of predefined paths, artificial potential field methods, mathematical programming and optimization, tangent graph-based planning, evolutionary algorithms, simulated annealing, particle swarm optimization, and partially observable Markov decision processes. Path planning and trajectory planning are important issues in the field of robotics, vehicles, and, automation. Contributions from all fields related to path planning using intelligent system methods are welcome for this Special Issue.

Prof. Dr. Jih-Gau Juang
Guest Editor

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Keywords

path planning
artificial intelligence
obstacle avoidance
image processing
intelligent control
autonomous vehicles
robotic systems
navigation and guidance
SLAM

Published Papers (11 papers)

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Research

32 pages, 32753 KiB

Open AccessArticle

UAV Path Planning and Obstacle Avoidance Based on Reinforcement Learning in 3D Environments

by Guan-Ting Tu and Jih-Gau Juang

Actuators 2023, 12(2), 57; https://doi.org/10.3390/act12020057 - 28 Jan 2023

Cited by 12 | Viewed by 4494

Abstract

This study proposes using unmanned aerial vehicles (UAVs) to carry out tasks involving path planning and obstacle avoidance, and to explore how to improve work efficiency and ensure the flight safety of drones. One of the applications under consideration is aquaculture cage detection; the net-cages used in sea-farming are usually numerous and are scattered widely over the sea. It is necessary to save energy consumption so that the drones can complete all cage detections and return to their base on land. In recent years, the application of reinforcement learning has become more and more extensive. In this study, the proposed method is mainly based on the Q-learning algorithm to enable improvements to path planning, and we compare it with a well-known reinforcement learning state–action–reward–state–action (SARSA) algorithm. For the obstacle avoidance control procedure, the same reinforcement learning method is used for training in the AirSim virtual environment; the parameters are changed, and the training results are compared. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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16 pages, 1155 KiB

Open AccessArticle

Route Planning for Autonomous Mobile Robots Using a Reinforcement Learning Algorithm

by Fatma M. Talaat, Abdelhameed Ibrahim, El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Amel Ali Alhussan, Doaa Sami Khafaga and Dina Ahmed Salem

Actuators 2023, 12(1), 12; https://doi.org/10.3390/act12010012 - 26 Dec 2022

Cited by 3 | Viewed by 1784

Abstract

This research suggests a new robotic system technique that works specifically in settings such as hospitals or emergency situations when prompt action and preserving human life are crucial. Our framework largely focuses on the precise and prompt delivery of medical supplies or medication inside a defined area while avoiding robot collisions or other obstacles. The suggested route planning algorithm (RPA) based on reinforcement learning makes medical services effective by gathering and sending data between robots and human healthcare professionals. In contrast, humans are kept out of the patients’ field. Three key modules make up the RPA: (i) the Robot Finding Module (RFM), (ii) Robot Charging Module (RCM), and (iii) Route Selection Module (RSM). Using such autonomous systems as RPA in places where there is a need for human gathering is essential, particularly in the medical field, which could reduce the risk of spreading viruses, which could save thousands of lives. The simulation results using the proposed framework show the flexible and efficient movement of the robots compared to conventional methods under various environments. The RSM is contrasted with the leading cutting-edge topology routing options. The RSM’s primary benefit is the much-reduced calculations and updating of routing tables. In contrast to earlier algorithms, the RSM produces a lower AQD. The RSM is hence an appropriate algorithm for real-time systems. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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18 pages, 5878 KiB

Open AccessArticle

Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks

by Matthias Rowold, Levent Ögretmen, Tobias Kerbl and Boris Lohmann

Actuators 2022, 11(11), 319; https://doi.org/10.3390/act11110319 - 03 Nov 2022

Cited by 8 | Viewed by 2080

Abstract

Autonomous racing has increasingly become a research subject as it provides insights into dynamic, high-speed situations. One crucial aspect of handling these situations, especially in the presence of dynamic obstacles, is the generation of a collision-free trajectory that represents a safe behavior and is also competitive in the case of racing. We propose a local planning approach that generates such trajectories for a racing car on an oval race track by searching a spatiotemporal graph. A considerable challenge of search-based methods in a spatiotemporal domain is the curse of dimensionality. Therefore, we propose how a previously presented graph structure that is based on intervals instead of discrete values can be searched more efficiently without losing optimality by using a uniform-cost search strategy. We extend the search method to make it anytime-capable so that it can provide a suboptimal trajectory even if the search has to be terminated early. The graph-based planning approach allows us to apply a flexible cost function so that our approach can operate fully autonomously on an oval race track, including the pit lane. We present a cost function for oval racing and explain how the terms contribute to the desired behaviors. This is supported by results with a full-scale prototype. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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17 pages, 11434 KiB

Open AccessArticle

Smooth Trajectory Planning at the Handling Limits for Oval Racing

by Levent Ögretmen, Matthias Rowold, Marvin Ochsenius and Boris Lohmann

Actuators 2022, 11(11), 318; https://doi.org/10.3390/act11110318 - 03 Nov 2022

Cited by 7 | Viewed by 1896

Abstract

In motion planning for autonomous racing, the challenge arises in planning smooth trajectories close to the handling limits of the vehicle with a sufficient planning horizon. Graph-based trajectory planning methods can find the global discrete-optimal solution, but they suffer from the curse of dimensionality. Therefore, to achieve low computation times despite a long planning horizon, coarse discretization and simple edges that are efficient to generate must be used. However, the resulting rough trajectories cannot reach the handling limits of the vehicle and are also difficult to track by the controller, which can lead to unstable driving behavior. In this paper, we show that the initial edges connecting the vehicle’s estimated state with the actual graph are crucial for vehicle stability and race performance. We therefore propose a sampling-based approach that relies on jerk-optimal curves to generate these initial edges. The concept is introduced using a layer-based graph, but it can be applied to other graph structures as well. We describe the integration of the curves within the graph and the required adaptation to racing scenarios. Our approach enables stable driving at the handling limits and fully autonomous operation on the race track. While simulations show the comparison of our concept with an alternative approach based on uniform acceleration, we also present experimental results of a dynamic overtake with speeds up to 74 m/s on a full-size vehicle. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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18 pages, 4880 KiB

Open AccessArticle

An Enhanced Navigation Algorithm with an Adaptive Controller for Wheeled Mobile Robot Based on Bidirectional RRT

by Bing-Gang Jhong and Mei-Yung Chen

Actuators 2022, 11(10), 303; https://doi.org/10.3390/act11100303 - 21 Oct 2022

Cited by 2 | Viewed by 1654

Abstract

A navigation algorithm providing motion planning for two-wheeled mobile robots is proposed in this paper. The motion planning integrates path planning, velocity planning and controller design. Bidirectional rapidly-exploring random trees algorithms (RRT) with path pruning and smoothing mechanism are firstly used to obtain a collision-free path to the robot destination with directional continuity. Secondly, velocity planning based on trapezoidal velocity profile is used in both linear and angular velocities, but the position error of the endpoint of the curve appears due to the coupling problem of the nonlinear system. To reduce the error, an approximation method is used to gradually modify several parts of time length of the trapezoidal velocity profile, so the continuity of the path can still be maintained. Thirdly, the controller keeping the robot on the planned path and velocity is designed based on the dynamic model of the robot. The parameters of this controller are estimated by the adaptive low, and the gain of controller is dynamic adjusted by fuzzy logic control to avoid the case that the control value is saturated. The controller stability and the convergence of tracking error is guaranteed by Lyapunov theory. Simulation results are presented to illustrate the effectiveness and efficiency. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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18 pages, 4039 KiB

Open AccessArticle

Path Planning for Multiple Unmanned Vehicles (MUVs) Formation Shape Generation Based on Dual RRT Optimization

by Tianhao Gong, Yang Yu and Jianhui Song

Actuators 2022, 11(7), 190; https://doi.org/10.3390/act11070190 - 13 Jul 2022

Cited by 6 | Viewed by 1884

Abstract

In this paper, dual RRT optimization is proposed to solve the formation shape generation problem for a large number of MUVs. Since large numbers of MUVs are prone to collision during formation shape generation, this paper considers the use of path planning algorithms to solve the collision avoidance problem. Additionally, RRT as a commonly used path planning algorithm has non-optimal solutions and strong randomness. Therefore, this paper proposes a dual RRT optimization to improve the drawbacks of RRT, which is applicable to the formation shape generation of MUVs. First, an initial global path can be obtained quickly by taking advantage of RRT-connect. After that, RRT* is used to optimize the initial global path locally. After finding the section that needs to be optimized, RRT* performs a new path search on the section and replaces the original path. Due to its asymptotic optimality, the path obtained by RRT* is shorter and smoother than the initial path. Finally, the algorithm can further optimize the path results by introducing a path evaluation function to determine the results of multiple runs. The experimental results show that the dual RRT operation optimization can greatly reduce the running time while avoiding obstacles and obtaining better path results than the RRT* algorithm. Moreover, multiple runs still ensure stable path results. The formation shape generation of MUVs can be completed in the shortest time using dual RRT optimization. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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19 pages, 11220 KiB

Open AccessArticle

Vehicle Positioning and Navigation in Asynchronous Navigation System

by Xinyang Zhao and Bocheng Zhu

Actuators 2022, 11(2), 54; https://doi.org/10.3390/act11020054 - 10 Feb 2022

Cited by 1 | Viewed by 1778

Abstract

A Pseudo-satellite system that transmits signals similar to GNSS can provide positioning services in places where GNSS signals are not captured and have enormous potential for indoor machine system and airports. Different paths of the device have different carrier phase initial solution positioning accuracy. Existing methods rely on measuring instruments or use many coordinate points for solving ambiguity resolution (AR), which creates inconvenience for real-time ground positioning. This study aims to find a new on-the-fly (OTF) method to achieve high accuracy and convenient positioning. A new method is proposed based on a two-difference observation model for ground-based high-precision point positioning. We used an adaptive particle swarm algorithm to solve the initial solution, followed by a nonlinear least-squares method to optimize the localization solution. It is free of priori information or measuring instruments. We designed several different paths, such as circular trajectory and square trajectory, to study the positioning accuracy of the solution. Simulation experiments with different trajectories showed that geometric changes significantly impact solutions. In addition, it does not require precise time synchronization of the base stations, making the whole system much easier to deploy. We built a real-world pseudo-satellite system and used a multi-sensor crewless vehicle as a receiver. Real-world experiments showed that our approach could achieve centimeter-level positioning accuracy in applications. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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20 pages, 4618 KiB

Open AccessArticle

Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

by Shaobo Wang, Fen Lin, Tiancheng Wang, Youqun Zhao, Liguo Zang and Yaoji Deng

Actuators 2022, 11(2), 52; https://doi.org/10.3390/act11020052 - 08 Feb 2022

Cited by 12 | Viewed by 2963

Abstract

Different driving styles should be considered in path planning for autonomous vehicles that are travelling alongside other traditional vehicles in the same traffic scene. Based on the drivers’ characteristics and artificial potential field (APF), an improved local path planning algorithm is proposed in this paper. A large amount of driver data are collected through tests and classified by the K-means algorithm. A Keras neural network model is trained by using the above data. APF is combined with driver characteristic identification. The distances between the vehicle and obstacle are normalized. The repulsive potential field functions are designed according to different driver characteristics and road boundaries. The designed local path planning method can adapt to different surrounding manual driving vehicles. The proposed human-like decision path planning method is compared with the traditional APF planning method. Simulation tests of an individual driver and various drivers with different characteristics in overtaking scenes are carried out. The simulation results show that the curves of human-like decision-making path planning method are more reasonable than those of the traditional APF path planning method; the proposed method can carry out more effective path planning for autonomous vehicles according to the different driving styles of surrounding manual vehicles. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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17 pages, 1161 KiB

Open AccessArticle

Output-Feedback Position Tracking Servo System with Feedback Gain Learning Mechanism via Order-Reduction Speed-Error-Stabilization Approach

by Sung Hyun You, Seok-Kyoon Kim and Hyun Duck Choi

Actuators 2021, 10(12), 324; https://doi.org/10.3390/act10120324 - 05 Dec 2021

Cited by 2 | Viewed by 2906

Abstract

This paper presents a novel trajectory-tracking technique for servo systems treating only the position measurement as the output subject to practical concerns: system parameter and load uncertainties. There are two main contributions: (a) the use of observers without system parameter information for estimating the position reference derivative and speed and acceleration errors and (b) an order reduction exponential speed error stabilizer via active damping injection to enable the application of a feedback-gain-learning position-tracking action. A hardware configuration using a QUBE-servo2 and myRIO-1900 experimentally validates the closed-loop improvement under various scenarios. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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19 pages, 7864 KiB

Open AccessArticle

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

by Jiayi Wang, Yonghu Luo and Xiaojun Tan

Actuators 2021, 10(12), 314; https://doi.org/10.3390/act10120314 - 29 Nov 2021

Cited by 10 | Viewed by 3882

Abstract

In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal and faster converge global path within a short time, and the improved TEB can optimize both path smoothness and path length. The method is composed of a global path planning procedure and a local path planning procedure, and the Receding Horizon Planning (RHP) strategy is adopted to fuse these two modules. Firstly, the MH-RRT is utilized to generate a state tree structure as prior knowledge, as well as the global path. Then, a receding horizon window is established to select the local goal point. On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Various simulations both on static and dynamic environments are conducted to clarify the performance of the proposed MH-RRT and the improved two-step TEB. Furthermore, real applicative experiments verified the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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16 pages, 7958 KiB

Open AccessArticle

Constrained Path Planning for Unmanned Aerial Vehicle in 3D Terrain Using Modified Multi-Objective Particle Swarm Optimization

by Shuang Xia and Xiangyin Zhang

Actuators 2021, 10(10), 255; https://doi.org/10.3390/act10100255 - 29 Sep 2021

Cited by 7 | Viewed by 2420

Abstract

This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness. Full article

(This article belongs to the Special Issue Intelligent Control and Robotic System in Path Planning)

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