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Frontiers in Mobile Robot Navigation

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 13713

Special Issue Editor

System Engineering and Automation Division, Carlos III University, C/ Butarque 15, 28911 Leganés, Spain
Interests: mobile robotics; environment modelling; environment sensing; robot navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Robot navigation remains one of the fundamental topics within the robotics research field. Although many techniques have already been developed, still new techniques appear with the aim to increase the level of autonomy in robot navigation, as well as increase the levels of abstraction, to more closely resemble the way humans navigate with those who are destined to share environments, tasks and communicate.

The objective of this Special Issue is to collect current and future trends in robot navigation in all research areas and levels. These trends include geometric navigation techniques, including geometric modeling with traditional lidar sensors or high-density sensors, with SLAM techniques, environment modeling with uncertainty and probabilistic techniques, with new planning techniques based on artificial intelligence. Hybrid and topological navigation techniques are also included with new techniques, such as those used for information on maps, based on multiple levels and new ways of exploring the environment. Finally, the incorporation and handling of semantic information is contemplated, which not only helps to label and segment the environments through which the robot is going to move, but also provides information that increases autonomy in the tasks of perception, modeling, location, planning and navigation of robots in an environment.

In short, it is about establishing new frontiers in terms of perception of the environment, modeling of the environment, location, planning and navigation of robots in an environment, both indoors and outdoors.

Dr. Ramon Barber
Guest Editor

Manuscript Submission Information

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Published Papers (5 papers)

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Research

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20 pages, 6250 KiB  
Article
Loop Closure Detection Based on Residual Network and Capsule Network for Mobile Robot
by Xin Zhang, Liaomo Zheng, Zhenhua Tan and Suo Li
Sensors 2022, 22(19), 7137; https://doi.org/10.3390/s22197137 - 21 Sep 2022
Cited by 2 | Viewed by 1611
Abstract
Loop closure detection based on a residual network (ResNet) and a capsule network (CapsNet) is proposed to address the problems of low accuracy and poor robustness for mobile robot simultaneous localization and mapping (SLAM) in complex scenes. First, the residual network of a [...] Read more.
Loop closure detection based on a residual network (ResNet) and a capsule network (CapsNet) is proposed to address the problems of low accuracy and poor robustness for mobile robot simultaneous localization and mapping (SLAM) in complex scenes. First, the residual network of a feature coding strategy is introduced to extract the shallow geometric features and deep semantic features of images, reduce the amount of image noise information, accelerate the convergence speed of the model, and solve the problems of gradient disappearance and network degradation of deep neural networks. Then, the dynamic routing mechanism of the capsule network is optimized through the entropy peak density, and a vector is used to represent the spatial position relationship between features, which can improve the ability of image feature extraction and expression to optimize the overall performance of networks. Finally, the optimized residual network and capsule network are fused to retain the differences and correlations between features, and the global feature descriptors and feature vectors are combined to calculate the similarity of image features for loop closure detection. The experimental results show that the proposed method can achieve loop closure detection for mobile robots in complex scenes, such as view changes, illumination changes, and dynamic objects, and improve the accuracy and robustness of mobile robot SLAM. Full article
(This article belongs to the Special Issue Frontiers in Mobile Robot Navigation)
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21 pages, 10754 KiB  
Article
Parallel Sensor-Space Lattice Planner for Real-Time Obstacle Avoidance
by Bernardo Martinez Rocamora, Jr. and Guilherme A. S. Pereira
Sensors 2022, 22(13), 4770; https://doi.org/10.3390/s22134770 - 24 Jun 2022
Cited by 1 | Viewed by 1724
Abstract
This paper presents a parallel motion planner for mobile robots and autonomous vehicles based on lattices created in the sensor space of planar range finders. The planner is able to compute paths in a few milliseconds, thus allowing obstacle avoidance in real time. [...] Read more.
This paper presents a parallel motion planner for mobile robots and autonomous vehicles based on lattices created in the sensor space of planar range finders. The planner is able to compute paths in a few milliseconds, thus allowing obstacle avoidance in real time. The proposed sensor-space lattice (SSLAT) motion planner uses a lattice to tessellate the area covered by the sensor and to rapidly compute collision-free paths in the robot surroundings by optimizing a cost function. The cost function guides the vehicle to follow a vector field, which encodes the desired vehicle path. We evaluated our method in challenging cluttered static environments, such as warehouses and forests, and in the presence of moving obstacles, both in simulations and real experiments. In these experiments, we show that our algorithm performs collision checking and path planning faster than baseline methods. Since the method can have sequential or parallel implementations, we also compare the two versions of SSLAT and show that the run time for its parallel implementation, which is independent of the number and shape of the obstacles found in the environment, provides a speedup greater than 25. Full article
(This article belongs to the Special Issue Frontiers in Mobile Robot Navigation)
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17 pages, 7594 KiB  
Article
FM2 Path Planner for UAV Applications with Curvature Constraints: A Comparative Analysis with Other Planning Approaches
by Santiago Garrido, Javier Muñoz, Blanca López, Fernando Quevedo, Concepción A. Monje and Luis Moreno
Sensors 2022, 22(9), 3174; https://doi.org/10.3390/s22093174 - 21 Apr 2022
Cited by 4 | Viewed by 1789
Abstract
This paper studies the Fast Marching Square (FM2) method as a competitive path planner for UAV applications. The approach fulfills trajectory curvature constraints together with a significantly reduced computation time, which makes it overperform with respect to other planning [...] Read more.
This paper studies the Fast Marching Square (FM2) method as a competitive path planner for UAV applications. The approach fulfills trajectory curvature constraints together with a significantly reduced computation time, which makes it overperform with respect to other planning methods of the literature based on optimization. A comparative analysis is presented to demonstrate how the FM2 approach can easily adapt its performance thanks to the introduction of two parameters, saturation α and exponent β, that allow a flexible configuration of the paths in terms of curvature restrictions, among others. The main contributions of the method are twofold: first, a feasible path is directly obtained without the need of a later optimization process to accomplish curvature restrictions; second, the computation speed is significantly increased, up to 220 times faster than other optimization-based methods such as, for instance, Dubins, Euler–Mumford Elastica and Reeds–Shepp. Simulation results are given to demonstrate the superiority of the method when used for UAV applications in comparison with the three previously mentioned methods. Full article
(This article belongs to the Special Issue Frontiers in Mobile Robot Navigation)
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30 pages, 8978 KiB  
Article
A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
by Johann Laconte, Abderrahim Kasmi, François Pomerleau, Roland Chapuis, Laurent Malaterre, Christophe Debain and Romuald Aufrère
Sensors 2021, 21(22), 7562; https://doi.org/10.3390/s21227562 - 14 Nov 2021
Cited by 4 | Viewed by 1923
Abstract
In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision [...] Read more.
In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken. Full article
(This article belongs to the Special Issue Frontiers in Mobile Robot Navigation)
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Review

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28 pages, 427 KiB  
Review
A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios
by Johann Laconte, Abderrahim Kasmi, Romuald Aufrère, Maxime Vaidis and Roland Chapuis
Sensors 2022, 22(1), 247; https://doi.org/10.3390/s22010247 - 30 Dec 2021
Cited by 19 | Viewed by 5752
Abstract
In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and [...] Read more.
In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed. The second component consists of estimating the vehicle’s position in its lane. Finally, the third and last one aims at assessing on which lane the vehicle is currently driving. These two last components are mandatory for safe driving as actions such as overtaking a vehicle require precise information about the current localization of the vehicle. In this survey, we introduce a taxonomy of the localization methods for autonomous vehicles in highway scenarios. We present each main component of the localization process, and discuss the advantages and drawbacks of the associated state-of-the-art methods. Full article
(This article belongs to the Special Issue Frontiers in Mobile Robot Navigation)
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