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30 pages, 4125 KiB

Open AccessArticle

A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios

by Diego Arce, Jans Solano and Cesar Beltrán

Sensors 2023, 23(24), 9672; https://doi.org/10.3390/s23249672 - 7 Dec 2023

Viewed by 1069

Abstract

At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the [...] Read more.

At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that—according to the application and the robot’s characteristics—it is recommended to use each of them, based on different conditions. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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16 pages, 2675 KiB

Open AccessArticle

An Innovative Collision-Free Image-Based Visual Servoing Method for Mobile Robot Navigation Based on the Path Planning in the Image Plan

by Mohammed Albekairi, Hassen Mekki, Khaled Kaaniche and Amr Yousef

Sensors 2023, 23(24), 9667; https://doi.org/10.3390/s23249667 - 7 Dec 2023

Viewed by 913

Abstract

In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera’s field of view. A single monocular sensor’s sole visual data [...] Read more.

In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera’s field of view. A single monocular sensor’s sole visual data serves as the basis for our method. The fundamental idea is to manage and control the dynamics associated with any trajectory generated in the image plane. We show that the differential flatness of the system’s dynamics can be used to limit arbitrary paths based on the number of points on the object that need to be reached in the image plane. This creates a link between the current configuration and the desired configuration. The number of required points depends on the number of control inputs of the robot used and determines the dimension of the flat output of the system. For a two-wheeled mobile robot, for instance, the coordinates of a single point on the object in the image plane are sufficient, whereas, for a quadcopter with four rotating motors, the trajectory needs to be defined by the coordinates of two points in the image plane. By guaranteeing precise tracking of the chosen trajectory in the image plane, we ensure that problems of collision with obstacles and leaving the camera’s field of view are avoided. Our approach is based on the principle of the inverse problem, meaning that when any point on the object is selected in the image plane, it will not be occluded by obstacles or leave the camera’s field of view during movement. It is true that proposing any trajectory in the image plane can lead to non-intuitive movements (back and forth) in the Cartesian plane. In the case of backward motion, the robot may collide with obstacles as it navigates without direct vision. Therefore, it is essential to perform optimal trajectory planning that avoids backward movements. To assess the effectiveness of our method, our study focuses exclusively on the challenge of implementing the generated trajectory in the image plane within the specific context of a two-wheeled mobile robot. We use numerical simulations to illustrate the performance of the control strategy we have developed. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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21 pages, 1965 KiB

Open AccessArticle

Exploration–Exploitation Tradeoff in the Adaptive Information Sampling of Unknown Spatial Fields with Mobile Robots

by Aiman Munir and Ramviyas Parasuraman

Sensors 2023, 23(23), 9600; https://doi.org/10.3390/s23239600 - 4 Dec 2023

Cited by 1 | Viewed by 731

Abstract

Adaptive information-sampling approaches enable efficient selection of mobile robots’ waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized [...] Read more.

Adaptive information-sampling approaches enable efficient selection of mobile robots’ waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot’s energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm’s information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point’s informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map’s accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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19 pages, 12292 KiB

Open AccessArticle

Hierarchical Vision Navigation System for Quadruped Robots with Foothold Adaptation Learning

by Junli Ren, Yingru Dai, Bowen Liu, Pengwei Xie and Guijin Wang

Sensors 2023, 23(11), 5194; https://doi.org/10.3390/s23115194 - 30 May 2023

Cited by 1 | Viewed by 1812

Abstract

Legged robots can travel through complex scenes via dynamic foothold adaptation. However, it remains a challenging task to efficiently utilize the dynamics of robots in cluttered environments and to achieve efficient navigation. We present a novel hierarchical vision navigation system combining foothold adaptation [...] Read more.

Legged robots can travel through complex scenes via dynamic foothold adaptation. However, it remains a challenging task to efficiently utilize the dynamics of robots in cluttered environments and to achieve efficient navigation. We present a novel hierarchical vision navigation system combining foothold adaptation policy with locomotion control of the quadruped robots. The high-level policy trains an end-to-end navigation policy, generating an optimal path to approach the target with obstacle avoidance. Meanwhile, the low-level policy trains the foothold adaptation network through auto-annotated supervised learning to adjust the locomotion controller and to provide more feasible foot placement. Extensive experiments in both simulation and the real world show that the system achieves efficient navigation against challenges in dynamic and cluttered environments without prior information. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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20 pages, 26768 KiB

Open AccessArticle

Real-Time Stereo-Based Ocean Surface Mapping for Robotic Floating Platforms: Concept and Methodology

by Or Greenberg and Boaz Ben-Moshe

Sensors 2023, 23(8), 3857; https://doi.org/10.3390/s23083857 - 10 Apr 2023

Viewed by 1454

Abstract

Consider the case of a small, unmanned boat that is performing an autonomous mission. Naturally, such a platform might need to approximate the ocean surface of its surroundings in real-time. Much like obstacle mapping in autonomous (off-road) rovers, an approximation of the ocean [...] Read more.

Consider the case of a small, unmanned boat that is performing an autonomous mission. Naturally, such a platform might need to approximate the ocean surface of its surroundings in real-time. Much like obstacle mapping in autonomous (off-road) rovers, an approximation of the ocean surface in a vessel’s surroundings in real-time can be used for improved control and optimized route planning. Unfortunately, such an approximation seems to require either expensive and heavy sensors or external logistics that are mostly not available for small or low-cost vessels. In this paper, we present a real-time method for detecting and tracking ocean waves around a floating object that is based on stereo vision sensors. Based on a large set of experiments, we conclude that the presented method allows reliable, real-time, and cost-effective ocean surface mapping suitable for small autonomous boats. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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19 pages, 6096 KiB

Open AccessArticle

ROS-Based Autonomous Navigation Robot Platform with Stepping Motor

by Shengmin Zhao and Seung-Hoon Hwang

Sensors 2023, 23(7), 3648; https://doi.org/10.3390/s23073648 - 31 Mar 2023

Cited by 5 | Viewed by 4569

Abstract

Indoor navigation robots, which have been developed using a robot operating system, typically use a direct current motor as a motion actuator. Their control algorithm is generally complex and requires the cooperation of sensors such as wheel encoders to correct errors. For this [...] Read more.

Indoor navigation robots, which have been developed using a robot operating system, typically use a direct current motor as a motion actuator. Their control algorithm is generally complex and requires the cooperation of sensors such as wheel encoders to correct errors. For this study, an autonomous navigation robot platform named Owlbot was designed, which is equipped with a stepping motor as a mobile actuator. In addition, a stepping motor control algorithm was developed using polynomial equations, which can effectively convert speed instructions to generate control signals for accurately operating the motor. Using 2D LiDAR and an inertial measurement unit as the primary sensors, simultaneous localization, mapping, and autonomous navigation are realised based on the particle filtering mapping algorithm. The experimental results show that Owlbot can effectively map the unknown environment and realise autonomous navigation through the proposed control algorithm, with a maximum movement error being smaller than 0.015 m. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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13 pages, 2325 KiB

Open AccessArticle

Crowd-Aware Mobile Robot Navigation Based on Improved Decentralized Structured RNN via Deep Reinforcement Learning

by Yulin Zhang and Zhengyong Feng

Sensors 2023, 23(4), 1810; https://doi.org/10.3390/s23041810 - 6 Feb 2023

Cited by 1 | Viewed by 1422

Abstract

Efficient navigation in a socially compliant manner is an important and challenging task for robots working in dynamic dense crowd environments. With the development of artificial intelligence, deep reinforcement learning techniques have been widely used in the robot navigation. Previous model-free reinforcement learning [...] Read more.

Efficient navigation in a socially compliant manner is an important and challenging task for robots working in dynamic dense crowd environments. With the development of artificial intelligence, deep reinforcement learning techniques have been widely used in the robot navigation. Previous model-free reinforcement learning methods only considered the interactions between robot and humans, not the interactions between humans and humans. To improve this, we propose a decentralized structured RNN network with coarse-grained local maps (LM-SRNN). It is capable of modeling not only Robot–Human interactions through spatio-temporal graphs, but also Human–Human interactions through coarse-grained local maps. Our model captures current crowd interactions and also records past interactions, which enables robots to plan safer paths. Experimental results show that our model is able to navigate efficiently in dense crowd environments, outperforming state-of-the-art methods. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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16 pages, 1194 KiB

Open AccessArticle

Navigation of Multiple Disk-Shaped Robots with Independent Goals within Obstacle-Cluttered Environments

by Panagiotis Vlantis, Charalampos P. Bechlioulis and Kostas J. Kyriakopoulos

Sensors 2023, 23(1), 221; https://doi.org/10.3390/s23010221 - 25 Dec 2022

Cited by 2 | Viewed by 1186

Abstract

In this work, we propose a hybrid control scheme to address the navigation problem for a team of disk-shaped robotic platforms operating within an obstacle-cluttered planar workspace. Given an initial and a desired configuration of the system, we devise a hierarchical cell decomposition [...] Read more.

In this work, we propose a hybrid control scheme to address the navigation problem for a team of disk-shaped robotic platforms operating within an obstacle-cluttered planar workspace. Given an initial and a desired configuration of the system, we devise a hierarchical cell decomposition methodology which is able to determine which regions of the configuration space need to be further subdivided at each iteration, thus avoiding redundant cell expansions. Furthermore, given a sequence of free configuration space cells with an arbitrary connectedness and shape, we employ harmonic transformations and harmonic potential fields to accomplish safe transitions between adjacent cells, thus ensuring almost-global convergence to the desired configuration. Finally, we present the comparative simulation results that demonstrate the efficacy of the proposed control scheme and its superiority in terms of complexity while yielding a satisfactory performance without incorporating optimization in the selection of the paths. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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23 pages, 10452 KiB

Open AccessArticle

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

by Yunchao Bai, Bing Li, Haosu Zhang, Sheng Wang, Debao Yan, Ziheng Gao and Wenchao Pan

Sensors 2022, 22(19), 7547; https://doi.org/10.3390/s22197547 - 5 Oct 2022

Viewed by 1225

Abstract

We propose a kind of fast and high-precision alignment algorithm based on the ESO technology. Firstly, in order to solve the problems of rapid, high-accuracy, and anti-interference alignment on the moving pedestal in the north-seeker, the ESO technology in control theory is introduced [...] Read more.

We propose a kind of fast and high-precision alignment algorithm based on the ESO technology. Firstly, in order to solve the problems of rapid, high-accuracy, and anti-interference alignment on the moving pedestal in the north-seeker, the ESO technology in control theory is introduced to improve the traditional Kalman fine-alignment model. This method includes two stages: the coarse alignment in the inertial frame and fine alignment based on the ESO technology. By utilizing the ESO technology, the convergence speed of the heading angle can be greatly accelerated. The advantages of this method are high-accuracy, fast-convergence, strong ability of anti-interference, and short time-cost (no need of KF recursive calculation). Then, the algorithm model, calculation process, and the setting initial-values of the filter are shown. Finally, taking the shipborne north-finder based on the FOG (fiber-optic gyroscope) as the investigated subject, the test on the moving ship is carried out. The results of first off-line simulation show that the misalignment angle of the heading angle of the proposed (traditional) method is ≤2.1′ (1.8′) after 5.5 (10) minutes of alignment. The results of second off-line simulation indicate that the misalignment angle of the heading angle of the proposed (traditional) method is ≤4.8′ (14.2′) after 5.5 (10) minutes of alignment. The simulations are based on the ship-running experimental data. The measurement precisions of Doppler velocity log (DVL) are different in these two experiments. Full article

(This article belongs to the Special Issue Mobile Robots for Navigation)

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