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31 pages, 21091 KiB

Open AccessArticle

Design, Construction, and Validation of an Experimental Electric Vehicle with Trajectory Tracking

by Joel Artemio Morales Viscaya, Alejandro Israel Barranco Gutiérrez and Gilberto González Gómez

Sensors 2024, 24(9), 2769; https://doi.org/10.3390/s24092769 - 26 Apr 2024

Viewed by 257

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine [...] Read more.

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine was replaced with an electric one. Subsequently, sensors used to measure the vehicle states were placed, calibrated, and instrumented. Additionally, a mathematical model was developed along with a strategy for the parametric identification of this model. A communication scheme was implemented consisting of four slave devices responsible for controlling the accelerator, brake, steering wheel, and measuring the sensors related to odometry. The master device is responsible for communicating with the slaves, displaying information on a screen, creating a log, and implementing trajectory tracking techniques based on classical, geometric, and predictive control. Finally, the performance of the control algorithms implemented on the experimental prototype was compared in terms of tracking error and control input across three different types of trajectories: lane change, right-angle curve, and U-turn. Full article

(This article belongs to the Special Issue Advanced Research in Intelligent Autonomous Mobile Robots System, Learning and Control)

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22 pages, 3074 KiB

Open AccessArticle

A Deep Learning Approach to Lunar Rover Global Path Planning Using Environmental Constraints and the Rover Internal Resource Status

by Toshiki Tanaka and Heidar Malki

Sensors 2024, 24(3), 844; https://doi.org/10.3390/s24030844 - 28 Jan 2024

Viewed by 714

Abstract

This research proposes a novel approach to global path and resource planning for lunar rovers. The proposed method incorporates a range of constraints, including static, time-variant, and path-dependent factors related to environmental conditions and the rover’s internal resource status. These constraints are integrated [...] Read more.

This research proposes a novel approach to global path and resource planning for lunar rovers. The proposed method incorporates a range of constraints, including static, time-variant, and path-dependent factors related to environmental conditions and the rover’s internal resource status. These constraints are integrated into a grid map as a penalty function, and a reinforcement learning-based framework is employed to address the resource constrained shortest path problem (RCSP). Compared to existing approaches referenced in the literature, our proposed method enables the simultaneous consideration of a broader spectrum of constraints. This enhanced flexibility leads to improved path search optimality. To evaluate the performance of our approach, this research applied the proposed learning architecture to lunar rover path search problems, generated based on real lunar digital elevation data. The simulation results demonstrate that our architecture successfully identifies a rover path while consistently adhering to user-defined environmental and rover resource safety criteria across all positions and time epochs. Furthermore, the simulation results indicate that our approach surpasses conventional methods that solely rely on environmental constraints. Full article

(This article belongs to the Special Issue Advanced Research in Intelligent Autonomous Mobile Robots System, Learning and Control)

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30 pages, 9204 KiB

Open AccessArticle

Path Planning of a Mobile Delivery Robot Operating in a Multi-Story Building Based on a Predefined Navigation Tree

by Jordi Palacín, Elena Rubies, Ricard Bitriá and Eduard Clotet

Sensors 2023, 23(21), 8795; https://doi.org/10.3390/s23218795 - 28 Oct 2023

Cited by 2 | Viewed by 1613

Abstract

Planning the path of a mobile robot that must transport and deliver small packages inside a multi-story building is a problem that requires a combination of spatial and operational information, such as the location of origin and destination points and how to interact [...] Read more.

Planning the path of a mobile robot that must transport and deliver small packages inside a multi-story building is a problem that requires a combination of spatial and operational information, such as the location of origin and destination points and how to interact with elevators. This paper presents a solution to this problem, which has been formulated under the following assumptions: (1) the map of the building’s floors is available; (2) the position of all origin and destination points is known; (3) the mobile robot has sensors to self-localize on the floors; (4) the building is equipped with remotely controlled elevators; and (5) all doors expected in a delivery route will be open. We start by defining a static navigation tree describing the weighted paths in a multi-story building. We then proceed to describe how this navigation tree can be used to plan the route of a mobile robot and estimate the total length of any delivery route using Dijkstra’s algorithm. Finally, we show simulated routing results that demonstrate the effectiveness of this proposal when applied to an autonomous delivery robot operating in a multi-story building. Full article

(This article belongs to the Special Issue Advanced Research in Intelligent Autonomous Mobile Robots System, Learning and Control)

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18 pages, 7059 KiB

Open AccessArticle

A Graph-Based Hybrid Reconfiguration Deformation Planning for Modular Robots

by Ruopeng Wei, Yubin Liu, Huijuan Dong, Yanhe Zhu and Jie Zhao

Sensors 2023, 23(18), 7892; https://doi.org/10.3390/s23187892 - 14 Sep 2023

Viewed by 791

Abstract

The self-reconfigurable modular robotic system is a class of robots that can alter its configuration by rearranging the connectivity of their component modular units. The reconfiguration deformation planning problem is to find a sequence of reconfiguration actions to transform one reconfiguration into another. [...] Read more.

The self-reconfigurable modular robotic system is a class of robots that can alter its configuration by rearranging the connectivity of their component modular units. The reconfiguration deformation planning problem is to find a sequence of reconfiguration actions to transform one reconfiguration into another. In this paper, a hybrid reconfiguration deformation planning algorithm for modular robots is presented to enable reconfiguration between initial and goal configurations. A hybrid algorithm is developed to decompose the configuration into subconfigurations with maximum commonality and implement distributed dynamic mapping of free vertices. The module mapping relationship between the initial and target configurations is then utilized to generate reconfiguration actions. Simulation and experiment results verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Advanced Research in Intelligent Autonomous Mobile Robots System, Learning and Control)

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19 pages, 21643 KiB

Open AccessArticle

Cross-Domain Indoor Visual Place Recognition for Mobile Robot via Generalization Using Style Augmentation

by Piotr Wozniak and Dominik Ozog

Sensors 2023, 23(13), 6134; https://doi.org/10.3390/s23136134 - 04 Jul 2023

Cited by 2 | Viewed by 1126

Abstract

The article presents an algorithm for the multi-domain visual recognition of an indoor place. It is based on a convolutional neural network and style randomization. The authors proposed a scene classification mechanism and improved the performance of the models based on synthetic and [...] Read more.

The article presents an algorithm for the multi-domain visual recognition of an indoor place. It is based on a convolutional neural network and style randomization. The authors proposed a scene classification mechanism and improved the performance of the models based on synthetic and real data from various domains. In the proposed dataset, a domain change was defined as a camera model change. A dataset of images collected from several rooms was used to show different scenarios, human actions, equipment changes, and lighting conditions. The proposed method was tested in a scene classification problem where multi-domain data were used. The basis was a transfer learning approach with an extension style applied to various combinations of source and target data. The focus was on improving the unknown domain score and multi-domain support. The results of the experiments were analyzed in the context of data collected on a humanoid robot. The article shows that the average score was the highest for the use of multi-domain data and data style enhancement. The method of obtaining average results for the proposed method reached the level of 92.08%. The result obtained by another research team was corrected. Full article

(This article belongs to the Special Issue Advanced Research in Intelligent Autonomous Mobile Robots System, Learning and Control)

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31 pages, 5695 KiB

Open AccessArticle

Autonomous Robots for Services—State of the Art, Challenges, and Research Areas

by Marius Misaros, Ovidiu-Petru Stan, Ionut-Catalin Donca and Liviu-Cristian Miclea

Sensors 2023, 23(10), 4962; https://doi.org/10.3390/s23104962 - 22 May 2023

Cited by 3 | Viewed by 4132

Abstract

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at [...] Read more.

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted. Full article

(This article belongs to the Special Issue Advanced Research in Intelligent Autonomous Mobile Robots System, Learning and Control)

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16 pages, 8130 KiB

Open AccessArticle

Collective Cognition on Global Density in Dynamic Swarm

by Phan Gia Luan and Nguyen Truong Thinh

Sensors 2023, 23(10), 4648; https://doi.org/10.3390/s23104648 - 11 May 2023

Viewed by 1020

Abstract

Swarm density plays a key role in the performance of a robot swarm, which can be averagely measured by swarm size and the area of a workspace. In some scenarios, the swarm workspace may not be fully or partially observable, or the swarm [...] Read more.

Swarm density plays a key role in the performance of a robot swarm, which can be averagely measured by swarm size and the area of a workspace. In some scenarios, the swarm workspace may not be fully or partially observable, or the swarm size may decrease over time due to out-of-battery or faulty individuals during operation. This can result in the average swarm density over the whole workspace being unable to be measured or changed in real-time. The swarm performance may not be optimal due to unknown swarm density. If the swarm density is too low, inter-robot communication will rarely be established, and robot swarm cooperation will not be effective. Meanwhile, a densely-packed swarm compels robots to permanently solve collision avoidance issues rather than performing the main task. To address this issue, in this work, the distributed algorithm for collective cognition on the average global density is proposed. The main idea of the proposed algorithm is to help the swarm make a collective decision on whether the current global density is larger, smaller or approximately equal to the desired density. During the estimation process, the swarm size adjustment is acceptable for the proposed method in order to reach the desired swarm density. Full article

(This article belongs to the Special Issue Advanced Research in Intelligent Autonomous Mobile Robots System, Learning and Control)

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23 pages, 9862 KiB

Open AccessArticle

Multi-Robot Exploration of Unknown Space Using Combined Meta-Heuristic Salp Swarm Algorithm and Deterministic Coordinated Multi-Robot Exploration

by Ali El Romeh and Seyedali Mirjalili

Sensors 2023, 23(4), 2156; https://doi.org/10.3390/s23042156 - 14 Feb 2023

Cited by 9 | Viewed by 2573

Abstract

Multi-robot exploration means constructing a finite map using a group of robots in an obstacle chaotic space. Uncertainties are reduced by distributing search tasks to robots and computing the best action in real time. Many previous methods are based on deterministic or meta-heuristic [...] Read more.

Multi-robot exploration means constructing a finite map using a group of robots in an obstacle chaotic space. Uncertainties are reduced by distributing search tasks to robots and computing the best action in real time. Many previous methods are based on deterministic or meta-heuristic algorithms, but limited work has combined both techniques to consolidate both classes’ benefits and alleviate their drawbacks. This paper proposes a new hybrid method based on deterministic coordinated multi-robot exploration (CME) and the meta-heuristic salp swarm algorithm (SSA) to perform the search of a space. The precedence of adjacent cells around a robot is determined by deterministic CME using cost and utility. Then, the optimization process of the search space, improving the overall solution, is achieved utilizing the SSA. Three performance measures are considered to evaluate the performance of the proposed method: run time, percentage of the explored area, and the number of times when a method failed to continue a complete run. Experimental results compared four different methods, CME-GWO, CME-GWOSSA, CME-SCA, and CME, over seven maps with extra complexity varying from simple to complex. The results demonstrate how the proposed CME-SSA can outperform the four other methods. Moreover, the simulation results demonstrate that the proposed CME-SSA effectively distributes the robots over the search space to run successfully and obtain the highest exploration rate in less time. Full article

(This article belongs to the Special Issue Advanced Research in Intelligent Autonomous Mobile Robots System, Learning and Control)

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