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Intelligent Control and Robotic Technologies in Path Planning
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Intelligent Control and Robotic Technologies in Path Planning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (25 January 2025) | Viewed by 4753

Special Issue Editor

Prof. Dr. Jonghoek Kim

E-Mail Website
Guest Editor
System Engineering Department, Sejong University, Seoul 05006, Republic of Korea
Interests: robotics; target tracking; multi-agent robotics; optimal estimation; path planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Path planning is fundamental and crucial for various kinds of robots, such as autonomous vehicles, multi-connection robots, or robot arms. It is crucial to generate a safe path that avoids collision with obstacles or other robots in the path planning of multiple robots. Considering aerial or underwater robots, a safe path must be planned by considering 3D environments. The complexity of the path planning of a robot arm increases significantly as the number of degrees of freedom increases. Thus, safe paths must be generated for high-dimensional systems in a time-efficient manner. In practice, an obstacle may move, sometimes necessitating the replanning of a robot’s path. Moreover, it is desirable to consider the dynamic model of a robot when generating a path for the robot. The need for advanced sensing systems, navigation, guidance, and controls for autonomous vehicles is growing as the demands for such vehicles to undertake more complex missions increase. This Special Issue will present the recent advances in the research on the above-mentioned topics.

Prof. Dr. Jonghoek Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • energy-efficient path planning
  • time-efficient path planning
  • motion planning
  • simultaneous localization and mapping (SLAM)
  • coverage path plan
  • mobile robots
  • multiple robots
  • robot arms
  • underwater robots
  • unmanned aerial vehicle
  • safe path plan
  • navigation, guidance, and controls for autonomous vehicles
  • 3D path plan

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Published Papers (5 papers)

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Research

25 pages, 5934 KiB  
Article
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
Viewed by 643
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  
Article
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 592
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  
Article
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
Viewed by 801
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  
Article
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 1 | Viewed by 1169
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  
Article
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
Viewed by 915
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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