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Novel Sensors and Algorithms for Outdoor Mobile Robot

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 20266

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Dr. Levente Tamás

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Guest Editor

Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Interests: pose estimation; mapping; 3D recognition; sensor fusion; active perception

Dr. Andras Majdik

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Guest Editor

Machine Perception Research Laboratory (MPLab), Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, H-1111 Budapest, Hungary
Interests: spatial AI; understanding of semantic scenes and their applications for mobile robots
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Special Issue Information

Dear Colleagues,

Mobile robot navigation greatly depends on two components: the sensors used for the environment perception and the algorithms applied for localization and mapping in a navigation feedback loop. Recently, both components have made significant progress and require advanced machine learning techniques in order to be implemented efficiently in embedded robot hardware.

The recent advances in the field of deep learning has also had a great impact on mobile robot navigation, especially on the 2D–3D perception pipelines including the segmentation, recognition and pose estimation parts, while for the path planning, the deep reinforcement learning also proved to be feasible with limited hardware resources.

Thus, the use of machine learning algorithms seems to be a promising direction for the future of embedded mobile robot navigation software stacks. Combined with novel sensing techniques, this yields efficient real-time robot platforms for both the industrial and entertainment markets.

This Special Issue focuses on novel sensors and algorithms based on advanced machine learning techniques for outdoor mobile robot navigation. The aim is to highlight the current trend in this specific field of robotics of machine learning with constraint processing power. Thus, this Special Issue welcomes the contributions in the field of mobile robotics with the focus on novel sensing techniques used in real-life outdoor navigation scenarios.

The topics of interest for this special issue include, but are not limited to:

Robot sensing and perception;
SLAM;
Mobile robot exploration;
Machine learning for mobile robot;
Edge computing.

Dr. Levente Tamás
Dr. Andras Majdik
Guest Editors

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Published Papers (10 papers)

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Research

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20 pages, 7440 KiB

Open AccessArticle

Hardware Schemes for Smarter Indoor Robotics to Prevent the Backing Crash Framework Using Field Programmable Gate Array-Based Multi-Robots

by Mudasar Basha, Munuswamy Siva Kumar, Mangali Chinna Chinnaiah, Siew-Kei Lam, Thambipillai Srikanthan, Janardhan Narambhatla, Hari Krishna Dodde and Sanjay Dubey

Sensors 2024, 24(6), 1724; https://doi.org/10.3390/s24061724 - 07 Mar 2024

Viewed by 466

Abstract

The use of smart indoor robotics services is gradually increasing in real-time scenarios. This paper presents a versatile approach to multi-robot backing crash prevention in indoor environments, using hardware schemes to achieve greater competence. Here, sensor fusion was initially used to analyze the state of multi-robots and their orientation within a static or dynamic scenario. The proposed novel hardware scheme-based framework integrates both static and dynamic scenarios for the execution of backing crash prevention. A round-robin (RR) scheduling algorithm was composed for the static scenario. Dynamic backing crash prevention was deployed by embedding a first come, first served (FCFS) scheduling algorithm. The behavioral control mechanism of the distributed multi-robots was integrated with FCFS and adaptive cruise control (ACC) scheduling algorithms. The integration of multiple algorithms is a challenging task for smarter indoor robotics, and the Xilinx-based partial reconfiguration method was deployed to avoid computational issues with multiple algorithms during the run-time. These methods were coded with Verilog HDL and validated using an FPGA (Zynq)-based multi-robot system. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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19 pages, 11791 KiB

Open AccessArticle

Robot Grasp Planning: A Learning from Demonstration-Based Approach

by Kaimeng Wang, Yongxiang Fan and Ichiro Sakuma

Sensors 2024, 24(2), 618; https://doi.org/10.3390/s24020618 - 18 Jan 2024

Viewed by 796

Abstract

Robot grasping constitutes an essential capability in fulfilling the complexities of advanced industrial operations. This field has been extensively investigated to address a range of practical applications. However, the generation of a stable grasp remains challenging, principally due to the constraints imposed by object geometries and the diverse objectives of the tasks. In this work, we propose a novel learning from demonstration-based grasp-planning framework. This framework is designed to extract crucial human grasp skills, namely the contact region and approach direction, from a single demonstration. Then, it formulates an optimization problem that integrates the extracted skills to generate a stable grasp. Distinct from conventional methods that rely on learning implicit synergies through human demonstration or on mapping the dissimilar kinematics between human hands and robot grippers, our approach focuses on learning the intuitive human intent that involves the potential contact regions and the grasping approach direction. Furthermore, our optimization formulation is capable of identifying the optimal grasp by minimizing the surface fitting error between the demonstrated contact regions on the object and the gripper finger surface and imposing a penalty for any misalignment between the demonstrated and the gripper’s approach directions. A series of experiments is conducted to verify the effectiveness of the proposed algorithm through both simulations and real-world scenarios. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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18 pages, 6304 KiB

Open AccessArticle

Hardware-Efficient Scheme for Trailer Robot Parking by Truck Robot in an Indoor Environment with Rendezvous

by Divya Vani G, Srinivasa Rao Karumuri, Chinnaiah M C, Siew-Kei Lam, Janardhan Narambhatlu and Sanjay Dubey

Sensors 2023, 23(11), 5097; https://doi.org/10.3390/s23115097 - 26 May 2023

Cited by 3 | Viewed by 1232

Abstract

Autonomous grounded vehicle-based social assistance/service robot parking in an indoor environment is an exciting challenge in urban cities. There are few efficient methods for parking multi-robot/agent teams in an unknown indoor environment. The primary objective of autonomous multi-robot/agent teams is to establish synchronization between them and to stay in behavioral control when static and when in motion. In this regard, the proposed hardware-efficient algorithm addresses the parking of a trailer (follower) robot in indoor environments by a truck (leader) robot with a rendezvous approach. In the process of parking, initial rendezvous behavioral control between the truck and trailer robots is established. Next, the parking space in the environment is estimated by the truck robot, and the trailer robot parks under the supervision of the truck robot. The proposed behavioral control mechanisms were executed between heterogenous-type computational-based robots. Optimized sensors were used for traversing and the execution of the parking methods. The truck robot leads, and the trailer robot mimics the actions in the execution of path planning and parking. The truck robot was integrated with FPGA (Xilinx Zynq XC7Z020-CLG484-1), and the trailer was integrated with Arduino UNO computing devices; this heterogenous modeling is adequate in the execution of trailer parking by a truck. The hardware schemes were developed using Verilog HDL for the FPGA (truck)-based robot and Python for the Arduino (trailer)-based robot. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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16 pages, 4091 KiB

Open AccessArticle

Object-Based Change Detection Algorithm with a Spatial AI Stereo Camera

by Levente Göncz and András László Majdik

Sensors 2022, 22(17), 6342; https://doi.org/10.3390/s22176342 - 23 Aug 2022

Cited by 3 | Viewed by 2127

Abstract

This paper presents a real-time object-based 3D change detection method that is built around the concept of semantic object maps. The algorithm is able to maintain an object-oriented metric-semantic map of the environment and can detect object-level changes between consecutive patrol routes. The proposed 3D change detection method exploits the capabilities of the novel ZED 2 stereo camera, which integrates stereo vision and artificial intelligence (AI) to enable the development of spatial AI applications. To design the change detection algorithm and set its parameters, an extensive evaluation of the ZED 2 camera was carried out with respect to depth accuracy and consistency, visual tracking and relocalization accuracy and object detection performance. The outcomes of these findings are reported in the paper. Moreover, the utility of the proposed object-based 3D change detection is shown in real-world indoor and outdoor experiments. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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22 pages, 2677 KiB

Open AccessArticle

A Simulator and First Reinforcement Learning Results for Underwater Mapping

by Matthias Rosynski and Lucian Buşoniu

Sensors 2022, 22(14), 5384; https://doi.org/10.3390/s22145384 - 19 Jul 2022

Cited by 2 | Viewed by 1265

Abstract

Underwater mapping with mobile robots has a wide range of applications, and good models are lacking for key parts of the problem, such as sensor behavior. The specific focus here is the huge environmental problem of underwater litter, in the context of the Horizon 2020 SeaClear project, where a team of robots is being developed to map and collect such litter. No reinforcement-learning solution to underwater mapping has been proposed thus far, even though the framework is well suited for robot control in unknown settings. As a key contribution, this paper therefore makes a first attempt to apply deep reinforcement learning (DRL) to this problem by exploiting two state-of-the-art algorithms and making a number of mapping-specific improvements. Since DRL often requires millions of samples to work, a fast simulator is required, and another key contribution is to develop such a simulator from scratch for mapping seafloor objects with an underwater vehicle possessing a sonar-like sensor. Extensive numerical experiments on a range of algorithm variants show that the best DRL method collects litter significantly faster than a baseline lawn mower trajectory. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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24 pages, 629 KiB

Open AccessArticle

A Simulation-Based Approach to Aid Development of Software-Based Hardware Failure Detection and Mitigation Algorithms of a Mobile Robot System

by Jacopo Sini, Andrea Passarino, Stefano Bonicelli and Massimo Violante

Sensors 2022, 22(13), 4665; https://doi.org/10.3390/s22134665 - 21 Jun 2022

Viewed by 1359

Abstract

Mechatronic systems, like mobile robots, are fairly complex. They are composed of electromechanical actuation components and sensing elements supervised by microcontrollers running complex embedded software. This paper proposes a novel approach to aid mobile robotics developers in adopting a rigorous development process to design and verify the robot’s detection and mitigation capabilities against random hardware failures affecting its sensors or actuators. Unfortunately, assessing the interactions between the various safety/mission-critical subsystem is quite complex. The failure mode effect analysis (FMEA) alongside an analysis of the failure detection capabilities (FMEDA) are the state-of-the-art methodologies for performing such an analysis. Various guidelines are available, and the authors decided to follow the one released by AIAG&VDA in June 2019. Since the robot’s behavior is based on embedded software, the FMEA has been integrated with the hardware/software interaction analysis described in the ECSS-Q-ST-30-02C manual. The core of this proposal is to show how a simulation-based approach, where the mechanical and electrical/electronic components are simulated alongside the embedded software, can effectively support FMEA. As a benchmark application, we considered the mobility system of a proof-of-concept assistance rover for Mars exploration designed by the D.I.A.N.A. student team at Politecnico di Torino. Thanks to the adopted approach, we described how to develop the detection and mitigation strategies and how to determine their effectiveness, with a particular focus on those affecting the sensors. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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21 pages, 1708 KiB

Open AccessArticle

Sensor Faults Isolation in Networked Control Systems: Application to Mobile Robot Platoons

by Wijaya Kurniawan and Lorinc Marton

Sensors 2021, 21(20), 6702; https://doi.org/10.3390/s21206702 - 09 Oct 2021

Cited by 2 | Viewed by 1544

Abstract

In networked control systems, sensor faults in a subsystem have a major influence on the entire network as the fault effect reaches the other subsystems through the network interconnections. In this paper, a fault diagnosis-oriented model is proposed for linear networked control systems that can be applied to the robotics platoon. In addition, this model can also be used to design distributed Unknown Input Observers (UIO) in each subsystem to accomplish weak sensor faults isolation by treating the network disturbances and fault propagation through the network as unknown inputs. A case study was developed in which the subsystems were represented by robots that are connected in a wireless communication-based leader-follower scheme. The simulation results show that the model successfully reproduces the expected behaviour of the robotics platoon in the presence of sensor faults. Furthermore, weak sensor faults isolation is also achieved by observing the residual signals produced by the UIOs in each of the subsystems. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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18 pages, 9821 KiB

Open AccessArticle

Feature Pyramid Network Based Efficient Normal Estimation and Filtering for Time-of-Flight Depth Cameras

by Szilárd Molnár, Benjamin Kelényi and Levente Tamas

Sensors 2021, 21(18), 6257; https://doi.org/10.3390/s21186257 - 18 Sep 2021

Cited by 4 | Viewed by 1774

Abstract

In this paper, an efficient normal estimation and filtering method for depth images acquired by Time-of-Flight (ToF) cameras is proposed. The method is based on a common feature pyramid networks (FPN) architecture. The normal estimation method is called ToFNest, and the filtering method ToFClean. Both of these low-level 3D point cloud processing methods start from the 2D depth images, projecting the measured data into the 3D space and computing a task-specific loss function. Despite the simplicity, the methods prove to be efficient in terms of robustness and runtime. In order to validate the methods, extensive evaluations on public and custom datasets were performed. Compared with the state-of-the-art methods, the ToFNest and ToFClean algorithms are faster by an order of magnitude without losing precision on public datasets. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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17 pages, 13344 KiB

Open AccessArticle

OctoPath: An OcTree-Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots

by Bogdan Trăsnea, Cosmin Ginerică, Mihai Zaha, Gigel Măceşanu, Claudiu Pozna and Sorin Grigorescu

Sensors 2021, 21(11), 3606; https://doi.org/10.3390/s21113606 - 22 May 2021

Cited by 5 | Viewed by 2779

Abstract

Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan the driving path and execute their motion. For addressing the issue of perception and path planning, in this paper, we introduce OctoPath, which is an encoder-decoder deep neural network, trained in a self-supervised manner to predict the local optimal trajectory for the ego-vehicle. Using the discretization provided by a 3D octree environment model, our approach reformulates trajectory prediction as a classification problem with a configurable resolution. During training, OctoPath minimizes the error between the predicted and the manually driven trajectories in a given training dataset. This allows us to avoid the pitfall of regression-based trajectory estimation, in which there is an infinite state space for the output trajectory points. Environment sensing is performed using a 40-channel mechanical LiDAR sensor, fused with an inertial measurement unit and wheels odometry for state estimation. The experiments are performed both in simulation and real-life, using our own developed GridSim simulator and RovisLab’s Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath in different driving scenarios, both indoor and outdoor, while benchmarking our system against a baseline hybrid A-Star algorithm and a regression-based supervised learning method, as well as against a CNN learning-based optimal path planning method. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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Review

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21 pages, 3926 KiB

Open AccessReview

Road Curb Detection: A Historical Survey

by Lucero M. Romero, Jose A. Guerrero and Gerardo Romero

Sensors 2021, 21(21), 6952; https://doi.org/10.3390/s21216952 - 20 Oct 2021

Cited by 11 | Viewed by 5116

Abstract

Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions. Full article

(This article belongs to the Special Issue Novel Sensors and Algorithms for Outdoor Mobile Robot)

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