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Special Issue "Mobile Robot Navigation"

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

Deadline for manuscript submissions: 31 August 2019

Special Issue Editors

Guest Editor
Prof. Dr. Jesús Ureña

Department of Electronics, School of Engineering, University of Alcala, Campus Universitario s/n, 28805 Alcala de Henares, Madrid, Spain
Website | E-Mail
Interests: ultrasonic signal processing; Local Positioning Systems (LPSs); mobile robots; electronic control, tracking and navigation; daily live monitoring; algorithm implementation on software and hardware
Guest Editor
Prof. Dr. Felipe Espinosa Zapata

Department of Electronics, School of Engineering, University of Alcala, Campus Universitario s/n, 28805 Alcala de Henares, Madrid, Spain
Website | E-Mail
Interests: network control systems; wireless sensor networks; event-based control; event-based estimation; electronic control engineering; robot formation; target approaching; trajectory tracking
Guest Editor
Dr. Roberto Iglesias Rodríguez

CiTIUS Research Centre, University of Santiago de Compostela , Rúa de Jenaro de la Fuente Domínguez, Campus Vida, 15782, Santiago de Compostela, Spain
Website | E-Mail
Interests: control and navigation in robotics; continuous and on-line robot and machine learning; indoor localization; scientific methods in robotics (modelling and characterization of robot behavior); pattern recognition

Special Issue Information

Dear Colleagues,

Navigation is one of the main challenges in robotics. Loads of works, from theoretical research to practical applications, have been devoted in the last decades to endow robots with the ability of navigating.  Yet, important advances in many topics are still required to handle the increasingly complex environments and tasks, imposed by the continuous evolution of robot technology in a great variety of domains (from autonomous cars, service robots, underwater vehicles to aerial robots). Nowadays, the massive use of drones has extended the navigation from 2D restricted spaces to 3D. Advances in perception and localization, computer vision, context aware navigation and route planning, dynamic guidance to the target, adaptation through online learning, are some of the challenges, to mention but a few, which are still required.

Different technologies and strategies are involved: sensing, positioning, mapping, approaching, tracking, formation, control, communication, human-interface, learning, etc.

The aim of this Special Issue is to contribute to the state-of-the-art and present current applications of robot navigation. This is why the Guest Editors invite papers related to the following topics, but the list is non-exhaustive:

  • Perception and localization. Stand-alone and cooperative approaches. SLAM.
  • Map-based, landmark-based, beacon-based navigation (2D and 3D)
  • Data fusion for mobile robot navigation.
  • Wireless sensor networks for mobile robot navigation.
  • Network control systems
  • Robot formation and tracking
  • Adaptive robot navigation and control
  • Tracking algorithms
  • Biologically inspired robot navigation
  • Applications of mobile robot navigation

Prof. Dr. Jesús Ureña
Prof. Dr. Felipe Espinosa Zapata
Dr. Roberto Iglesias Rodríguez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (13 papers)

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Research

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Open AccessArticle A Precise and GNSS-Free Landing System on Moving Platforms for Rotary-Wing UAVs
Sensors 2019, 19(4), 886; https://doi.org/10.3390/s19040886
Received: 9 January 2019 / Revised: 12 February 2019 / Accepted: 15 February 2019 / Published: 20 February 2019
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Abstract
This article presents a precise landing system that allows rotary-wing UAVs to approach and land safely on moving platforms, without using GNSS at any stage of the landing maneuver, and with a centimeter level accuracy and high level of robustness. This system implements [...] Read more.
This article presents a precise landing system that allows rotary-wing UAVs to approach and land safely on moving platforms, without using GNSS at any stage of the landing maneuver, and with a centimeter level accuracy and high level of robustness. This system implements a novel concept where the relative position and velocity between the aerial vehicle and the landing platform are calculated from the angles of a cable that physically connects the UAV and the landing platform. The use of a cable also incorporates a number of extra benefits, such as increasing the precision in the control of the UAV altitude. It also facilitates centering the UAV right on top of the expected landing position, and increases the stability of the UAV just after contacting the landing platform. The system was implemented in an unmanned helicopter and many tests were carried out under different conditions for measuring the accuracy and the robustness of the proposed solution. Results show that the developed system allowed landing with centimeter accuracy by using only local sensors and that the helicopter could follow the landing platform in multiple trajectories at different velocities. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Positioning, Navigation, and Book Accessing/Returning in an Autonomous Library Robot using Integrated Binocular Vision and QR Code Identification Systems
Sensors 2019, 19(4), 783; https://doi.org/10.3390/s19040783
Received: 24 January 2019 / Revised: 8 February 2019 / Accepted: 12 February 2019 / Published: 14 February 2019
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Abstract
With rapid advancements in artificial intelligence and mobile robots, some of the tedious yet simple jobs in modern libraries, like book accessing and returning (BAR) operations that had been fulfilled manually before, could be undertaken by robots. Due to the limited accuracies of [...] Read more.
With rapid advancements in artificial intelligence and mobile robots, some of the tedious yet simple jobs in modern libraries, like book accessing and returning (BAR) operations that had been fulfilled manually before, could be undertaken by robots. Due to the limited accuracies of the existing positioning and navigation (P&N) technologies and the operational errors accumulated within the robot P&N process, however, most of the current robots are not able to fulfill such high-precision operations. To address these practical issues, we propose, for the first time (to the best of our knowledge), to combine the binocular vision and Quick Response (QR) code identification techniques together to improve the robot P&N accuracies, and then construct an autonomous library robot for high-precision BAR operations. Specifically, the binocular vision system is used for dynamic digital map construction and autonomous P&N, as well as obstacle identification and avoiding functions, while the QR code identification technique is responsible for both robot operational error elimination and robotic arm BAR operation determination. Both simulations and experiments are conducted to verify the effectiveness of the proposed technique combination, as well as the constructed robot. Results show that such a technique combination is effective and robust, and could help to significantly improve the P&N and BAR operation accuracies, while reducing the BAR operation time. The implemented autonomous robot is fully-autonomous and cost-effective, and may find applications far beyond libraries with only sophisticated technologies employed. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Obstacle Avoidance of Two-Wheel Differential Robots Considering the Uncertainty of Robot Motion on the Basis of Encoder Odometry Information
Sensors 2019, 19(2), 289; https://doi.org/10.3390/s19020289
Received: 10 December 2018 / Revised: 7 January 2019 / Accepted: 9 January 2019 / Published: 12 January 2019
Cited by 1 | PDF Full-text (4025 KB) | HTML Full-text | XML Full-text
Abstract
It is important to overcome different types of uncertainties for the safe and reliable navigation of mobile robots. Uncertainty sources can be categorized into recognition, motion, and environmental sources. Although several challenges of recognition uncertainty have been addressed, little attention has been paid [...] Read more.
It is important to overcome different types of uncertainties for the safe and reliable navigation of mobile robots. Uncertainty sources can be categorized into recognition, motion, and environmental sources. Although several challenges of recognition uncertainty have been addressed, little attention has been paid to motion uncertainty. This study shows how the uncertainties of robot motions can be quantitatively modeled through experiments. Although the practical motion uncertainties are affected by various factors, this research focuses on the velocity control performance of wheels obtained by encoder sensors. Experimental results show that the velocity control errors of practical robots are not negligible. This paper proposes a new motion control scheme toward reliable obstacle avoidance by reflecting the experimental motion uncertainties. The presented experimental results clearly show that the consideration of the motion uncertainty is essential for successful collision avoidance. The presented simulation results show that a robot cannot move through narrow passages owing to a risk of collision when the uncertainty of motion is high. This research shows that the proposed method accurately reflects the motion uncertainty and balances the collision safety with the navigation efficiency of the robot. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV
Sensors 2019, 19(1), 174; https://doi.org/10.3390/s19010174
Received: 7 December 2018 / Revised: 31 December 2018 / Accepted: 31 December 2018 / Published: 5 January 2019
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Abstract
Exploring large, unknown, and unstructured environments is challenging for Unmanned Aerial Vehicles (UAVs), but they are valuable tools to inspect large structures safely and efficiently. The Lazy Theta* path-planning algorithm is revisited and adapted to generate paths fast enough to be used in [...] Read more.
Exploring large, unknown, and unstructured environments is challenging for Unmanned Aerial Vehicles (UAVs), but they are valuable tools to inspect large structures safely and efficiently. The Lazy Theta* path-planning algorithm is revisited and adapted to generate paths fast enough to be used in real time and outdoors in large 3D scenarios. In real unknown scenarios, a given minimum safety distance to the nearest obstacle or unknown space should be observed, increasing the associated obstacle detection queries, and creating a bottleneck in the path-planning algorithm. We have reduced the dimension of the problem by considering geometrical properties to speed up these computations. On the other hand, we have also applied a non-regular grid representation of the world to increase the performance of the path-planning algorithm. In particular, a sparse resolution grid in the form of an octree is used, organizing the measurements spatially, merging voxels when they are of the same state. Additionally, the number of neighbors is trimmed to match the sparse tree to reduce the number of obstacle detection queries. The development methodology adopted was Test-Driven Development (TDD) and the outcome was evaluated in real outdoors flights with a multirotor UAV. In the results, the performance shows over 90 percent decrease in overall path generation computation time. Furthermore, our approach scales well with the safety distance increases. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field
Sensors 2019, 19(1), 31; https://doi.org/10.3390/s19010031
Received: 29 November 2018 / Revised: 18 December 2018 / Accepted: 19 December 2018 / Published: 21 December 2018
Cited by 1 | PDF Full-text (684 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the problem of obstacle avoidance with bearing-only measurements in the case that the obstacle motion is model-free, i.e., its acceleration is absolutely unknown, which cannot be dealt with by the mainstream Kalman-like schemes based on the known motion model. First, [...] Read more.
This paper presents the problem of obstacle avoidance with bearing-only measurements in the case that the obstacle motion is model-free, i.e., its acceleration is absolutely unknown, which cannot be dealt with by the mainstream Kalman-like schemes based on the known motion model. First, the essential reason of the collision caused by local minimum problem in the standard artificial potential field method is proved, and hence a revised method with angle dependent factor is proposed. Then, an unknown input observer is proposed to estimate the position and velocity of the obstacle. Finally, the numeric simulation demonstrates the effectiveness in terms of estimation accuracy and terminative time. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Validation of a Dynamic Planning Navigation Strategy Applied to Mobile Terrestrial Robots
Sensors 2018, 18(12), 4322; https://doi.org/10.3390/s18124322
Received: 3 November 2018 / Revised: 22 November 2018 / Accepted: 27 November 2018 / Published: 7 December 2018
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Abstract
This work describes the performance of a DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm) algorithm applied to autonomous navigation in unknown static and dynamic terrestrial environments. The main aim was to validate the functionality and robustness of the DPNA-GA, with variations [...] Read more.
This work describes the performance of a DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm) algorithm applied to autonomous navigation in unknown static and dynamic terrestrial environments. The main aim was to validate the functionality and robustness of the DPNA-GA, with variations of genetic parameters including the crossover rate and population size. To this end, simulations were performed of static and dynamic environments, applying the different conditions. The simulation results showed satisfactory efficiency and robustness of the DPNA-GA technique, validating it for real applications involving mobile terrestrial robots. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment
Sensors 2018, 18(12), 4254; https://doi.org/10.3390/s18124254
Received: 29 October 2018 / Revised: 21 November 2018 / Accepted: 29 November 2018 / Published: 4 December 2018
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Abstract
Aiming at the problem of how to enable the mobile robot to navigate and traverse efficiently and safely in the unknown indoor environment and map the environment, an eight-direction scanning detection (eDSD) algorithm is proposed as a new pathfinding algorithm. Firstly, we use [...] Read more.
Aiming at the problem of how to enable the mobile robot to navigate and traverse efficiently and safely in the unknown indoor environment and map the environment, an eight-direction scanning detection (eDSD) algorithm is proposed as a new pathfinding algorithm. Firstly, we use a laser-based SLAM (Simultaneous Localization and Mapping) algorithm to perform simultaneous localization and mapping to acquire the environment information around the robot. Then, according to the proposed algorithm, the 8 certain areas around the 8 directions which are developed from the robot’s center point are analyzed in order to calculate the probabilistic path vector of each area. Considering the requirements of efficient traverse and obstacle avoidance in practical applications, the proposal can find the optimal local path in a short time. In addition to local pathfinding, the global pathfinding is also introduced for unknown environments of large-scale and complex structures to reduce the repeated traverse. The field experiments in three typical indoor environments demonstrate that deviation of the planned path from the ideal path can be kept to a low level in terms of the path length and total time consumption. It is confirmed that the proposed algorithm is highly adaptable and practical in various indoor environments. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments
Sensors 2018, 18(12), 4181; https://doi.org/10.3390/s18124181
Received: 20 October 2018 / Revised: 23 November 2018 / Accepted: 24 November 2018 / Published: 28 November 2018
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Abstract
In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. [...] Read more.
In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Bilevel Optimization-Based Time-Optimal Path Planning for AUVs
Sensors 2018, 18(12), 4167; https://doi.org/10.3390/s18124167
Received: 28 September 2018 / Revised: 12 November 2018 / Accepted: 26 November 2018 / Published: 27 November 2018
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Abstract
Using the bilevel optimization (BIO) scheme, this paper presents a time-optimal path planner for autonomous underwater vehicles (AUVs) operating in grid-based environments with ocean currents. In this scheme, the upper optimization problem is defined as finding a free-collision channel from a starting point [...] Read more.
Using the bilevel optimization (BIO) scheme, this paper presents a time-optimal path planner for autonomous underwater vehicles (AUVs) operating in grid-based environments with ocean currents. In this scheme, the upper optimization problem is defined as finding a free-collision channel from a starting point to a destination, which consists of connected grids, and the lower optimization problem is defined as finding an energy-optimal path in the channel generated by the upper level algorithm. The proposed scheme is integrated with ant colony algorithm as the upper level and quantum-behaved particle swarm optimization as the lower level and tested to find an energy-optimal path for AUV navigating through an ocean environment in the presence of obstacles. This arrangement prevents discrete state transitions that constrain a vehicle’s motion to a small set of headings and improves efficiency by the usage of evolutionary algorithms. Simulation results show that the proposed BIO scheme has higher computation efficiency with a slightly lower fitness value than sliding wavefront expansion scheme, which is a grid-based path planner with continuous motion directions. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Three Landmark Optimization Strategies for Mobile Robot Visual Homing
Sensors 2018, 18(10), 3180; https://doi.org/10.3390/s18103180
Received: 18 August 2018 / Revised: 14 September 2018 / Accepted: 17 September 2018 / Published: 20 September 2018
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Abstract
Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, [...] Read more.
Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision. In this paper, we propose three strategies to solve this problem. We use scale-invariant feature transform (SIFT) features as natural landmarks, and the proposed strategies can optimize the landmark distribution without over-eliminating landmarks or increasing calculation amount. Experiments on both panoramic image databases and a real mobile robot have verified the effectiveness and feasibility of the proposed strategies. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle Automatic Calibration of Odometry and Robot Extrinsic Parameters Using Multi-Composite-Targets for a Differential-Drive Robot with a Camera
Sensors 2018, 18(9), 3097; https://doi.org/10.3390/s18093097
Received: 14 August 2018 / Revised: 4 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
Cited by 1 | PDF Full-text (19528 KB) | HTML Full-text | XML Full-text
Abstract
This paper simultaneously calibrates odometry parameters and the relative pose between a monocular camera and a robot automatically. Most camera pose estimation methods use natural features or artificial landmark tools. However, there are mismatches and scale ambiguity for natural features; the large-scale precision [...] Read more.
This paper simultaneously calibrates odometry parameters and the relative pose between a monocular camera and a robot automatically. Most camera pose estimation methods use natural features or artificial landmark tools. However, there are mismatches and scale ambiguity for natural features; the large-scale precision landmark tool is also challenging to make. To solve these problems, we propose an automatic process to combine multiple composite targets, select keyframes, and estimate keyframe poses. The composite target consists of an aruco marker and a checkerboard pattern. First, an analytical method is applied to obtain initial values of all calibration parameters; prior knowledge of the calibration parameters is not required. Then, two optimization steps are used to refine the calibration parameters. Planar motion constraints of the camera are introduced in these optimizations. The proposed solution is automatic; manual selection of keyframes, initial values, and robot construction within a specific trajectory are not required. The competing accuracy and stability of the proposed method under different target placements and robot paths are tested experimentally. Positive effects on calibration accuracy and stability are obtained when (1) composite targets are adopted; (2) two optimization steps are used; (3) plane motion constraints are introduced; and (4) target numbers are increased. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Review

Jump to: Research

Open AccessReview Towards the Internet of Flying Robots: A Survey
Sensors 2018, 18(11), 4038; https://doi.org/10.3390/s18114038
Received: 17 October 2018 / Revised: 15 November 2018 / Accepted: 17 November 2018 / Published: 19 November 2018
Cited by 1 | PDF Full-text (499 KB) | HTML Full-text | XML Full-text
Abstract
The Internet of Flying Robots (IoFR) has received much attention in recent years thanks to the mobility and flexibility of flying robots. Although a lot of research has been done, there is a lack of a comprehensive survey on this topic. This paper [...] Read more.
The Internet of Flying Robots (IoFR) has received much attention in recent years thanks to the mobility and flexibility of flying robots. Although a lot of research has been done, there is a lack of a comprehensive survey on this topic. This paper analyzes several typical problems in designing IoFR for real applications, including wireless communication support, monitoring targets of interest, serving a wireless sensor network, and collaborating with ground robots. In particular, an overview of the existing publications on the coverage problem, connectivity of flying robots, energy capacity limitation, target searching, path planning, flying robot navigation with collision avoidance, etc., is presented. Beyond the discussion of these available approaches, some shortcomings of them are indicated and some promising future research directions are pointed out. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessReview Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges
Sensors 2018, 18(9), 3170; https://doi.org/10.3390/s18093170
Received: 8 August 2018 / Revised: 14 September 2018 / Accepted: 17 September 2018 / Published: 19 September 2018
Cited by 9 | PDF Full-text (2393 KB) | HTML Full-text | XML Full-text
Abstract
Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. [...] Read more.
Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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