Advances in Mobile Robotics Navigation, 2nd Volume

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Actuators actuators	2.6	3.2	2012	16.7 Days	CHF 2400	Submit
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Machines machines	2.6	2.1	2013	15.6 Days	CHF 2400	Submit
Robotics robotics	3.7	5.9	2012	17.3 Days	CHF 1800	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit
Inventions inventions	3.4	5.4	2016	17.4 Days	CHF 1800	Submit
Technologies technologies	3.6	5.5	2013	19.7 Days	CHF 1600	Submit

21 pages, 12780 KiB

Open AccessArticle

Position Checking-Based Sampling Approach Combined with Attraction Point Local Optimization for Safe Flight of UAVs

by Hai Zhu, Baoquan Li, Ruiyang Tong, Haolin Yin and Canlin Zhu

Sensors 2024, 24(7), 2157; https://doi.org/10.3390/s24072157 - 27 Mar 2024

Viewed by 341

Trading off the allocation of limited computational resources between front-end path generation and back-end trajectory optimization plays a key role in improving the efficiency of unmanned aerial vehicle (UAV) motion planning. In this paper, a sampling-based kinodynamic planning method that can reduce the [...] Read more.

Trading off the allocation of limited computational resources between front-end path generation and back-end trajectory optimization plays a key role in improving the efficiency of unmanned aerial vehicle (UAV) motion planning. In this paper, a sampling-based kinodynamic planning method that can reduce the computational cost as well as the risks of UAV flight is proposed. Firstly, an initial trajectory connecting the start and end points without considering obstacles is generated. Then, a spherical space is constructed around the topological vertices of the environment, based on the intersections of the trajectory with the obstacles. Next, some unnecessary sampling points, as well as node rewiring, are discarded by the designed position-checking strategy to minimize the computational cost and reduce the risks of UAV flight. Finally, in order to make the planning framework adaptable to complex scenarios, the strategies for selecting different attraction points according to the environment are designed, which further ensures the safe flight of the UAV while improving the success rate of the front-end trajectory. Simulations and real-world experiment comparisons are conducted on a vision-based platform to verify the performance of the proposed method. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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27 pages, 10349 KiB

Open AccessArticle

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

by Gabriel G. R. de Castro, Tatiana M. B. Santos, Fabio A. A. Andrade, José Lima, Diego B. Haddad, Leonardo de M. Honório and Milena F. Pinto

Machines 2024, 12(3), 200; https://doi.org/10.3390/machines12030200 - 18 Mar 2024

Viewed by 887

Abstract

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the [...] Read more.

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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16 pages, 13841 KiB

Open AccessArticle

Distributed Fixed-Time Formation Tracking Control for the Multi-Agent System and an Application in Wheeled Mobile Robots

by Ling Ma, Yufeng Gao and Bo Li

Actuators 2024, 13(2), 68; https://doi.org/10.3390/act13020068 - 11 Feb 2024

Viewed by 982

Abstract

This work addresses the issue of multi-agent system (MAS) formation control under external disturbances and a directed communication topology. Firstly, a new disturbance observer is proposed to effectively reconstruct and compensate for external disturbances within a short period of time. Then, the integral [...] Read more.

This work addresses the issue of multi-agent system (MAS) formation control under external disturbances and a directed communication topology. Firstly, a new disturbance observer is proposed to effectively reconstruct and compensate for external disturbances within a short period of time. Then, the integral terminal sliding mode technology is introduced to devise a novel distributed formation control protocol, ultimately realizing the stability of the MAS within a fixed time. Moreover, by means of rigorous Lyapunov theory analyses, a faster formation convergence rate and more accurate consensus accuracies are achieved in the proposed fixed-time strategy with variable exponent form. Finally, the formation tracking control scheme is applied to a multi-wheeled mobile robot (WMR) system. The experimental results strongly support the fine effectiveness of the control scheme designed in this work. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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15 pages, 1308 KiB

Open AccessArticle

Distributed Fixed-Time Leader-Following Consensus for Multi-Agent Systems: An Event-Triggered Mechanism

by Cheng Ge, Ling Ma and Shoulin Xu

Actuators 2024, 13(1), 40; https://doi.org/10.3390/act13010040 - 18 Jan 2024

Viewed by 1233

Abstract

In this work, a fixed-time leader-following event-triggered (ET) consensus problem for multi-agent systems (MASs) with external disturbances is investigated. A distributed observer is developed to achieve the estimated state of the leader. By means of the observation information, the consensus error system for [...] Read more.

In this work, a fixed-time leader-following event-triggered (ET) consensus problem for multi-agent systems (MASs) with external disturbances is investigated. A distributed observer is developed to achieve the estimated state of the leader. By means of the observation information, the consensus error system for multi-agents is reformulated into a tracking error system, wherein individual follower agent aims to track the leader agent. Building upon Lyapunov technology and fixed-time stability theory, a new ET protocol is introduced to mitigate communication wastes. Notably, the proposed controller incorporates a strong robust fixed-time control form with lower complexity, and a reliable dynamic triggering condition also ensures the excellent performance of the system. Rigorous demonstrations underscore the stability and robustness of the ET method, while guaranteeing the avoidance of Zeno behavior. Finally, several numerical simulations are provided to underscore the efficacy of the proposed protocols. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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15 pages, 11156 KiB

Open AccessArticle

Autonomous Obstacle Avoidance and Trajectory Planning for Mobile Robot Based on Dual-Loop Trajectory Tracking Control and Improved Artificial Potential Field Method

by Kunming Zheng

Actuators 2024, 13(1), 37; https://doi.org/10.3390/act13010037 - 17 Jan 2024

Cited by 1 | Viewed by 1338

Abstract

In order to better meet the practical application needs of mobile robots, this study innovatively designs an autonomous obstacle avoidance and trajectory planning control strategy with low computational complexity, high cost-effectiveness, and the ability to quickly plan a collision-free smooth trajectory curve. This [...] Read more.

In order to better meet the practical application needs of mobile robots, this study innovatively designs an autonomous obstacle avoidance and trajectory planning control strategy with low computational complexity, high cost-effectiveness, and the ability to quickly plan a collision-free smooth trajectory curve. This article constructs the kinematic model of the mobile robot, designs a dual-loop trajectory tracking control strategy for position control law and attitude control law algorithms, and improves the traditional artificial potential field method to achieve a good obstacle avoidance strategy for mobile robots. Based on the dual-loop trajectory tracking control and the improved artificial potential field method, the autonomous obstacle avoidance and trajectory planning scheme of the mobile robot is designed, and closed-loop stability verification and analysis are conducted on the overall control system. And through the detailed simulation and experiments, the advantages of the proposed method in trajectory tracking accuracy and motion stability compared to the existing methods are verified, showing good effectiveness and feasibility and laying a good foundation for the application of mobile robots in practical complex scenes. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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30 pages, 8339 KiB

Open AccessArticle

A Hierarchical Lane-Changing Trajectory Planning Method Based on the Least Action Principle

by Ke Liu, Guanzheng Wen, Yao Fu and Honglin Wang

Actuators 2024, 13(1), 10; https://doi.org/10.3390/act13010010 - 26 Dec 2023

Cited by 1 | Viewed by 1137

Abstract

This paper presents a hierarchical lane-changing trajectory planner based on the least action principle for autonomous driving. Our approach aims to achieve reliable real-time avoidance of static and moving obstacles in multi-vehicle interaction scenarios on structured urban roads. Unlike previous studies that rely [...] Read more.

This paper presents a hierarchical lane-changing trajectory planner based on the least action principle for autonomous driving. Our approach aims to achieve reliable real-time avoidance of static and moving obstacles in multi-vehicle interaction scenarios on structured urban roads. Unlike previous studies that rely on subjective weight allocation and single weighting methods, we propose a novel trajectory planning strategy that decomposes the process into two stages: candidate trajectory generation and optimal trajectory decision-making. The candidate trajectory generation employs a path-velocity decomposition method, using B-spline curves to generate a multi-objective optimal lane-changing candidate path. Collision checking eliminates paths at risk of collision with static obstacles. Dynamic programming (DP) and quadratic programming (QP) are then used to plan the velocity of safe paths, generating candidate lane-changing trajectories based on curvature checking. The optimal trajectory decision-making process follows the decision mechanism of excellent drivers. We introduce a comprehensive evaluation function, the average action, which considers safety, comfort, and efficiency based on the least action principle. Feasible trajectories are ranked based on their average action, and the trajectory with the minimum average action and no collision risk with moving obstacles is selected as the tracking target. The effectiveness of the proposed method is validated through two common lane-changing scenarios. The results demonstrate that our approach enables smooth, efficient, and safe lane-changing while effectively tracking the planned velocity and path. This method offers a solution to local trajectory planning problems in complex environments and holds promising prospects in the field of autonomous driving. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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28 pages, 1589 KiB

Open AccessArticle

A Safe Heuristic Path-Planning Method Based on a Search Strategy

by Xiaozhen Yan, Xinyue Zhou and Qinghua Luo

Sensors 2024, 24(1), 101; https://doi.org/10.3390/s24010101 - 24 Dec 2023

Viewed by 591

Abstract

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the [...] Read more.

In industrial production, it is very difficult to make a robot plan a safe, collision-free, smooth path with few inflection points. Therefore, this paper presents a safe heuristic path-planning method based on a search strategy. This method first expands the scope of the search node, then calculates the node state based on the search strategy, including whether it is a normal or dangerous state, and calculates the danger coefficient of the corresponding point to select the path with a lower danger coefficient. At the same time, the optimal boundary is obtained by incorporating the environmental facilities, and the optimal path between the starting point, the optimal boundary point and the end point is obtained. Compared to the traditional A-star algorithm, this method achieved significant improvements in various aspects such as path length, execution time, and path smoothness. Specifically, it reduced path length by 2.89%, decreased execution time by 13.98%, and enhanced path smoothness by 93.17%. The resulting paths are more secure and reliable, enabling robots to complete their respective tasks with reduced power consumption, thereby mitigating the drain on robot batteries. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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11 pages, 881 KiB

Open AccessArticle

Maze Solving Mobile Robot Based on Image Processing and Graph Theory

by Luis A. Avila-Sánchez, Carlos Sánchez-López, Rocío Ochoa-Montiel, Fredy Montalvo-Galicia, Luis A. Sánchez-Gaspariano, Carlos Hernández-Mejía and Hugo G. González-Hernández

Technologies 2023, 11(6), 171; https://doi.org/10.3390/technologies11060171 - 05 Dec 2023

Viewed by 2297

Abstract

Advances in the development of collision-free path planning algorithms are the main need not only to solve mazes with robotic systems, but also for their use in modern product transportation or green logistics systems and planning merchandise deliveries inside or outside a factory. [...] Read more.

Advances in the development of collision-free path planning algorithms are the main need not only to solve mazes with robotic systems, but also for their use in modern product transportation or green logistics systems and planning merchandise deliveries inside or outside a factory. This challenge increases as the complexity of the task in its structure also increases. This paper deals with the development of a novel methodology for solving mazes with a mobile robot, using image processing techniques and graph theory. The novelty is that the mobile robot can find the shortest path from a start-point to the end-point into irregular mazes with abundant irregular obstacles, a situation that is not far from reality. Maze information is acquired from an image and depending on the size of the mobile robot, a grid of nodes with the same dimensions of the maze is built. Another contribution of this paper is that the size of the maze can be scaled from 1 m × 1 m to 66 m × 66 m, maintaining the essence of the proposed collision-free path planning methodology. Afterwards, graph theory is used to find the shortest path within the grid of reduced nodes due to the elimination of those nodes absorbed by the irregular obstacles. To avoid the mobile robot to travel through those nodes very close to obstacles and borders, resulting in a collision, each image of the obstacle and border is dilated taking into account the size of the mobile robot. The methodology was validated with two case studies with a mobile robot in different mazes. We emphasize that the maze solution is found in a single computational step, from the maze image as input until the generation of the Path vector. Experimental results show the usefulness of the proposed methodology, which can be used in applications such as intelligent traffic control, military, agriculture and so on. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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13 pages, 6271 KiB

Open AccessArticle

GRI: General Reinforced Imitation and Its Application to Vision-Based Autonomous Driving

by Raphael Chekroun, Marin Toromanoff, Sascha Hornauer and Fabien Moutarde

Robotics 2023, 12(5), 127; https://doi.org/10.3390/robotics12050127 - 06 Sep 2023

Cited by 8 | Viewed by 1668

Abstract

Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications, such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stability. Prior knowledge, e.g., as expert demonstrations, is [...] Read more.

Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications, such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stability. Prior knowledge, e.g., as expert demonstrations, is often available but challenging to leverage to mitigate these issues. In this paper, we propose General Reinforced Imitation (GRI), a novel method which combines benefits from exploration and expert data and is straightforward to implement over any off-policy RL algorithm. We make one simplifying hypothesis: expert demonstrations can be seen as perfect data whose underlying policy gets a constant high reward. Based on this assumption, GRI introduces the notion of offline demonstration agent. This agent sends expert data which are processed both concurrently and indistinguishably with the experiences coming from the online RL exploration agent. We show that our approach enables major improvements on camera-based autonomous driving in urban environments. We further validate the GRI method on Mujoco continuous control tasks with different off-policy RL algorithms. Our method ranked first on the CARLA Leaderboard and outperforms World on Rails, the previous state-of-the-art method, by 17%. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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