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30 pages, 19731 KiB

Open AccessArticle

Path Planning and Obstacle Avoidance of Formation Flight

by Yi-Sin Yang and Jih-Gau Juang

Sensors 2025, 25(8), 2447; https://doi.org/10.3390/s25082447 - 12 Apr 2025

Viewed by 282

This study applies path planning and obstacle avoidance to drone control for conducting riverbank inspections. Given that the river’s surrounding environments are often windy and filled with overgrown branches and unknown obstacles, this study improves path planning and obstacle avoidance to enable drones [...] Read more.

This study applies path planning and obstacle avoidance to drone control for conducting riverbank inspections. Given that the river’s surrounding environments are often windy and filled with overgrown branches and unknown obstacles, this study improves path planning and obstacle avoidance to enable drones to complete inspection tasks using the planned optimal route. Multiple drones are used for larger-scale areas to reduce time consumption and increase efficiency. Regarding path planning, the A* algorithm is improved using a grid-based approach. For obstacle avoidance, depth cameras are installed on the drones, and the obtained images are processed by reinforcement Q-learning with a genetic algorithm to navigate around obstacles. Since maintaining formation is necessary during task execution, the leader–follower method of formation flight ensures that multiple drones can complete inspection tasks while maintaining formation. Full article

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

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22 pages, 7775 KiB

Open AccessArticle

Density-Based Detection Rapid Exploration Random Tree for Multirobot Formation Cooperative Path Planning

by Yuzhuo Shi, Yang Yang, Jinjun Liu, Kun Hao, Jiale Zhao and Haoyi Chai

Sensors 2025, 25(7), 2201; https://doi.org/10.3390/s25072201 - 31 Mar 2025

Viewed by 175

Abstract

This paper proposes a multirobot formation path planning method based on the leader–follower formation control method to ensure smooth operation in the multirobot formation control area. First, on the basis of the rapidly exploring random tree (RRT), a density detection rapidly exploring random [...] Read more.

This paper proposes a multirobot formation path planning method based on the leader–follower formation control method to ensure smooth operation in the multirobot formation control area. First, on the basis of the rapidly exploring random tree (RRT), a density detection rapidly exploring random tree (DDRRT) algorithm is designed to avoid repeated exploration of by the RRT, to quickly generate a global path from the starting point to the destination for the leader robot, and to propose a rope shrinkage path optimization mechanism for path optimization. Second, the repulsion field function in the artificial potential field (APF) is optimized for local collaborative obstacle avoidance to enable multiple robots, and a rotational potential field is introduced to solve the problems of unreachable targets and local oscillations. Finally, a control law based on consistency control is used to control the followers and introduce a formation change mechanism based on polar coordinate transformation to enhance the formation control capability. The simulation results show that the proposed strategy can provide high-quality paths for robot formations in multiple obstacle areas and guide robot formations to avoid various local obstacles quickly through formation transformation. Full article

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

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20 pages, 4329 KiB

Open AccessArticle

Improving Sensor Adaptability and Functionality in Cartographer Simultaneous Localization and Mapping

by Wonseok Jeong, Chanho Lee, Namyeong Lee, Seungwoo Hong, Donghyun Kang and Donghyeok An

Sensors 2025, 25(6), 1808; https://doi.org/10.3390/s25061808 - 14 Mar 2025

Viewed by 415

Abstract

This paper aims to address sensor-related challenges in simultaneous localization and mapping (SLAM) systems, specifically within the open-source Google Cartographer project, which implements graph-based SLAM. The primary problem tackled is the adaptability and functionality of SLAM systems in diverse robotic applications. To solve [...] Read more.

This paper aims to address sensor-related challenges in simultaneous localization and mapping (SLAM) systems, specifically within the open-source Google Cartographer project, which implements graph-based SLAM. The primary problem tackled is the adaptability and functionality of SLAM systems in diverse robotic applications. To solve this, we developed a novel SLAM framework that integrates five additional functionalities into the existing Google Cartographer and Robot Operating System (ROS). These innovations include an inertial data generation system and a sensor data preprocessing system to mitigate issues arising from various sensor configurations. Additionally, the framework enhances system utility through real-time 3D topographic mapping, multi-node SLAM capabilities, and elliptical sensor data filtering. The average execution times for sensor data preprocessing and virtual inertial data generation are 0.55 s and 0.15 milliseconds, indicating a low computational overhead. Elliptical filtering has nearly the same execution speed as the existing filtering scheme. Full article

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

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21 pages, 8437 KiB

Open AccessArticle

Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV

by Cheng Peng, Guanyu Qiao and Bing Ge

Sensors 2025, 25(4), 1177; https://doi.org/10.3390/s25041177 - 14 Feb 2025

Cited by 1 | Viewed by 452

Abstract

Unknown variables in the environment, such as wind disturbance during a flight, affect the accurate trajectory of multi-rotor UAVs. This study focuses on the intelligent supervisory neurocontrol of trajectory tracking for a nonplanar twelve-rotor UAV to address this issue. Firstly, a twelve-rotor UAV [...] Read more.

Unknown variables in the environment, such as wind disturbance during a flight, affect the accurate trajectory of multi-rotor UAVs. This study focuses on the intelligent supervisory neurocontrol of trajectory tracking for a nonplanar twelve-rotor UAV to address this issue. Firstly, a twelve-rotor UAV is developed with a nonplanar structure, which makes up for the defects of conventional multi-rotors with weak yaw movement. A characteristic model of the twelve-rotor UAV is devised so as to facilitate intelligent controller design without losing model information. For the purpose of achieving accurate and fast trajectory tracking and strong self-learning ability, an intelligent composite controller combining adaptive sliding-mode feedback control and dynamic cascade spiking neural network (DCSNN) supervisory feedforward control is proposed. The novel dynamic cascade network structure is constructed to better adapt to changing data and unstable environments. The weight learning algorithm and dynamic cascade structure learning algorithm work together to ensure network stability and robustness. Finally, comparative numerical simulations and twelve-rotor UAV prototype experiments verify the superior tracking control performance, even outdoors with wind disturbances. Full article

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

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25 pages, 5934 KiB

Open AccessArticle

Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks

by Juhi Agrawal and Muhammad Yeasir Arafat

Sensors 2025, 25(1), 72; https://doi.org/10.3390/s25010072 - 26 Dec 2024

Cited by 1 | Viewed by 938

Abstract

The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utilization and low latency. To address these issues, [...] Read more.

The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utilization and low latency. To address these issues, we propose a hybrid bio-inspired algorithm, HMAO, combining the mountain gazelle optimizer (MGO) and the aquila optimizer (AO). HMAO improves cluster stability and enhances data delivery reliability in FANETs. The algorithm uses MGO for efficient cluster head (CH) selection, considering UAV energy levels, mobility patterns, intra-cluster distance, and one-hop neighbor density, thereby reducing re-clustering frequency and ensuring coordinated operations. For cluster maintenance, a congestion-based approach redistributes UAVs in overloaded or imbalanced clusters. The AO-based routing algorithm ensures reliable data transmission from CHs to the base station by leveraging predictive mobility data, load balancing, fault tolerance, and global insights from ferry nodes. According to the simulations conducted on the network simulator (NS-3.35), the HMAO technique exhibits improved cluster stability, packet delivery ratio, low delay, overhead, and reduced energy consumption compared to the existing methods. Full article

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

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14 pages, 5077 KiB

Open AccessArticle

Development of a Collision-Free Path Planning Method for a 6-DoF Orchard Harvesting Manipulator Using RGB-D Camera and Bi-RRT Algorithm

by Zifu Liu, Rizky Mulya Sampurno, R. M. Rasika D. Abeyrathna, Victor Massaki Nakaguchi and Tofael Ahamed

Sensors 2024, 24(24), 8113; https://doi.org/10.3390/s24248113 - 19 Dec 2024

Viewed by 1017

Abstract

With the decreasing and aging agricultural workforce, fruit harvesting robots equipped with higher degrees of freedom (DoF) manipulators are seen as a promising solution for performing harvesting operations in unstructured and complex orchard environments. In such a complex environment, guiding the end-effector from [...] Read more.

With the decreasing and aging agricultural workforce, fruit harvesting robots equipped with higher degrees of freedom (DoF) manipulators are seen as a promising solution for performing harvesting operations in unstructured and complex orchard environments. In such a complex environment, guiding the end-effector from its starting position to the target fruit while avoiding obstacles poses a significant challenge for path planning in automatic harvesting. However, existing studies often rely on manually constructed environmental map models and face limitations in planning efficiency and computational cost. Therefore, in this study, we introduced a collision-free path planning method for a 6-DoF orchard harvesting manipulator using an RGB-D camera and the Bi-RRT algorithm. First, by transforming the RGB-D camera’s point cloud data into collision geometries, we achieved 3D obstacle map reconstruction, allowing the harvesting robot to detect obstacles within its workspace. Second, by adopting the URDF format, we built the manipulator’s simulation model to be inserted with the reconstructed 3D obstacle map environment. Third, the Bi-RRT algorithm was introduced for path planning, which performs bidirectional expansion simultaneously from the start and targets configurations based on the principles of the RRT algorithm, thereby effectively shortening the time required to reach the target. Subsequently, a validation and comparison experiment were conducted in an artificial orchard. The experimental results validated our method, with the Bi-RRT algorithm achieving reliable collision-free path planning across all experimental sets. On average, it required just 0.806 s and generated 12.9 nodes per path, showing greater efficiency in path generation compared to the Sparse Bayesian Learning (SBL) algorithm, which required 0.870 s and generated 15.1 nodes per path. This method proved to be both effective and fast, providing meaningful guidance for implementing path planning for a 6-DoF manipulator in orchard harvesting tasks. Full article

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

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17 pages, 4935 KiB

Open AccessArticle

Improved RRT* Path-Planning Algorithm Based on the Clothoid Curve for a Mobile Robot Under Kinematic Constraints

by Kemeng Ran, Yujun Wang, Can Fang, Qisen Chai, Xingxiang Dong and Guohui Liu

Sensors 2024, 24(23), 7812; https://doi.org/10.3390/s24237812 - 6 Dec 2024

Cited by 4 | Viewed by 1296

Abstract

In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed [...] Read more.

In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed algorithm are as follows: First, we introduce a bidirectional expansion strategy that quickly identifies a direct path to the goal point in a short time. Second, a node reconnection strategy is used to eliminate unnecessary nodes, thereby reducing the path length and saving memory. Third, a path deformation strategy based on the Clothoid curve is devised to enhance obstacle avoidance and path-planning capability, ensuring collision-free paths that comply with the kinematic constraints of mobile robots. Simulation results demonstrate that our algorithm is simpler, more computationally efficient, expedites pathfinding, achieves higher success rates, and produces smoother paths compared to existing algorithms. Full article

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

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23 pages, 11795 KiB

Open AccessArticle

Collision Avoidance Path Planning for Automated Vehicles Using Prediction Information and Artificial Potential Field

by Sumin Ahn, Taeyoung Oh and Jinwoo Yoo

Sensors 2024, 24(22), 7292; https://doi.org/10.3390/s24227292 - 14 Nov 2024

Cited by 3 | Viewed by 1735

Abstract

With the advancement of autonomous driving systems, the need for effective emergency avoidance path planning has become increasingly important. To enhance safety, the predicted paths of surrounding vehicles anticipate risks and incorporate them into avoidance strategies, enabling more efficient and stable driving. Although [...] Read more.

With the advancement of autonomous driving systems, the need for effective emergency avoidance path planning has become increasingly important. To enhance safety, the predicted paths of surrounding vehicles anticipate risks and incorporate them into avoidance strategies, enabling more efficient and stable driving. Although the artificial potential field (APF) method is commonly employed for path planning due to its simplicity and effectiveness, it can suffer from the local minimum problem when using gradient descent, causing the vehicle to become stuck before reaching the target. To address this issue and improve the efficiency and stability of path planning, this study proposes integrating prediction data into the APF and optimizing the control points of the quintic Bézier curve using sequential quadratic planning. The validity of the proposed method was confirmed through simulation using IPG CarMaker 12.0.1 and MATLAB/Simulink 2022b. Full article

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

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19 pages, 2861 KiB

Open AccessArticle

Autonomous Lunar Rover Localization while Fully Scanning a Bounded Obstacle-Rich Workspace

by Jonghoek Kim

Sensors 2024, 24(19), 6400; https://doi.org/10.3390/s24196400 - 2 Oct 2024

Cited by 1 | Viewed by 1226

Abstract

This article addresses the scanning path plan strategy of a rover team composed of three rovers, such that the team explores unknown dark outer space environments. This research considers a dark outer space, where a rover needs to turn on its light and [...] Read more.

This article addresses the scanning path plan strategy of a rover team composed of three rovers, such that the team explores unknown dark outer space environments. This research considers a dark outer space, where a rover needs to turn on its light and camera simultaneously to measure a limited space in front of the rover. The rover team is deployed from a symmetric base station, and the rover team’s mission is to scan a bounded obstacle-rich workspace, such that there exists no remaining detection hole. In the team, only one rover, the hauler, can locate itself utilizing stereo cameras and Inertial Measurement Unit (IMU). Every other rover follows the hauler, while not locating itself. Since Global Navigation Satellite System (GNSS) is not available in outer space, the localization error of the hauler increases as time goes on. For rover’s location estimate fix, one occasionally makes the rover home to the base station, whose shape and global position are known in advance. Once a rover is near the station, it uses its Lidar to measure the relative position of the base station. In this way, the rover fixes its localization error whenever it homes to the base station. In this research, one makes the rover team fully scan a bounded obstacle-rich workspace without detection holes, such that a rover’s localization error is bounded by letting the rover home to the base station occasionally. To the best of our knowledge, this article is novel in addressing the scanning path plan strategy, so that a rover team fully scans a bounded obstacle-rich workspace without detection holes, while fixing the accumulated localization error occasionally. The efficacy of the proposed scanning and localization strategy is demonstrated utilizing MATLAB-based simulations. Full article

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

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