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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 38959

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Dr. Nuno Lau

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Departament of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal
Interests: intelligent robotics; artificial intelligence; simulation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Luis Paulo Reis

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Universidade do Porto, Praça de Gomes Teixeira, 4099-002 Porto, Portugal
Interests: intelligent robotics; Artificial Intelligence; multiagent systems

Prof. Dr. Rui P. Rocha

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Guest Editor

Universidade de Coimbra, 3004-531 Coimbra, Portugal
Interests: multirobot systems; cooperative perception; autonomous robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Daer Colleagues,

Robotics is a very important domain for Artificial Intelligence (AI) research. From the beginning of AI, robotics has always played an important role both in providing real problems that need intelligent behavior and in enabling AI to perform tasks that involve physical interaction with the real world. However, the Robotics and AI communities have often worked separately and with little sharing of the developments in both areas.

Intelligent Robotics, where AI and Robotics merge to provide better solutions, is an essential area when designing robots or teams of robots that perform complex tasks in environments that are shared with humans. These robots must be endowed with high levels of adaptability to changes in task or the environment, so as to enrich their performance over their lifetime and enable a richer and more natural interaction with humans. In some cases, these robots will be mobile, work in teams, and be connected to a larger ecosystem that, in addition to robots, encompasses other intelligent networked devices, thus scaling to arbitrarily larger distributed intelligent and pervasive systems. Simulation and modeling can play an important role. Intelligent robotics can provide improvements in many aspects of human life as important as health, mobility, work, education, recreation, and domestic tasks.

This Special Issue intends to provide a forum for the dissemination of works that exploit this synergy between AI and Intelligent Robotics to solve complex tasks. Recent developments in both fields, together with hardware developments that make available to robots and other intelligent physical agents a higher computing power and more capable sensors and actuators, provide the grounding for innovative solutions lying in the intersection of AI and Intelligent Robotics, which may be published in this Special Issue.

Prof. Dr. Nuno Lau
Prof. Dr. Luis Paulo Reis
Prof. Dr. Rui P. Rocha
Guest Editors

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Keywords

Autonomous robots
Cognitive robotics
Computer vision
Distributed multirobot or multiagent coordination
Embodied multiagent systems
Evolutionary robotics and swarm robotics
Humanoid robots
Human–robot interaction
Modeling and simulation of complex robots
Multirobot systems
Robot behavior engineering
Robot learning
Robot planning
SLAM, navigation and exploration
Social and service robots

Published Papers (12 papers)

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Research

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17 pages, 6659 KiB

Open AccessArticle

Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing

by Alireza Rastegarpanah, Ali Aflakian and Rustam Stolkin

Appl. Sci. 2021, 11(23), 11566; https://doi.org/10.3390/app112311566 - 06 Dec 2021

Cited by 3 | Viewed by 2061

Abstract

This study proposes a hybrid visual servoing technique that is optimised to tackle the shortcomings of classical 2D, 3D and hybrid visual servoing approaches. These shortcomings are mostly the convergence issues, image and robot singularities, and unreachable trajectories for the robot. To address these deficiencies, 3D estimation of the visual features was used to control the translations in Z-axis as well as all rotations. To speed up the visual servoing (VS) operation, adaptive gains were used. Damped Least Square (DLS) approach was used to reduce the robot singularities and smooth out the discontinuities. Finally, manipulability was established as a secondary task, and the redundancy of the robot was resolved using the classical projection operator. The proposed approach is compared with the classical 2D, 3D and hybrid visual servoing methods in both simulation and real-world. The approach offers more efficient trajectories for the robot, with shorter camera paths than 2D image-based and classical hybrid VS methods. In comparison with the traditional position-based approach, the proposed method is less likely to lose the object from the camera scene, and it is more robust to the camera calibrations. Moreover, the proposed approach offers greater robot controllability (higher manipulability) than other approaches. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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14 pages, 9074 KiB

Open AccessArticle

Genetic Optimization of a Manipulator: Comparison between Straight, Rounded, and Curved Mechanism Links

by Robert Pastor, Zdenko Bobovský, Daniel Huczala and Stefan Grushko

Appl. Sci. 2021, 11(6), 2471; https://doi.org/10.3390/app11062471 - 10 Mar 2021

Cited by 6 | Viewed by 1845

Abstract

There are several ubiquitous kinematic structures that are used in industrial robots, with the most prominent being a six-axis angular structure. However, researchers are experimenting with task-based mechanism synthesis that could provide higher efficiency with custom optimized manipulators. Many studies have focused on finding the most efficient optimization algorithm for task-based robot manipulators. These manipulators, however, are usually optimized from simple modular joints and links, without exploring more elaborate modules. Here, we show that link modules defined by small numbers of parameters have better performance than more complicated ones. We compare four different manipulator link types, namely basic predefined links with fixed dimensions, straight links that can be optimized for different lengths, rounded links, and links with a curvature defined by a Hermite spline. Manipulators are then built from these modules using a genetic algorithm and are optimized for three different tasks. The results demonstrate that manipulators built from simple links not only converge faster, which is expected given the fewer optimized parameters, but also converge on lower cost values. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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29 pages, 10108 KiB

Open AccessArticle

Intelligent Exploration Approaches Based on Utility Functions Optimization for Multi-Agent Environment Applications

by José Oñate-López, Loraine Navarro, Christian G. Quintero M. and Mauricio Pardo

Appl. Sci. 2021, 11(5), 2408; https://doi.org/10.3390/app11052408 - 09 Mar 2021

Viewed by 1652

Abstract

In this work, the problem of exploring an unknown environment with a team of agents and search different targets on it is considered. The key problem to be solved in multiple agents is choosing appropriate target points for the individual agents to simultaneously explore different regions of the environment. An intelligent approach is presented to coordinate several agents using a market-based model to identify the appropriate task for each agent. It is proposed to compare the fitting of the market utility function using neural networks and optimize this function using genetic algorithms to avoid heavy computation in the Non-Polynomial (NP: nondeterministic polynomial time) path-planning problem. An indoor environment inspires the proposed approach with homogeneous physical agents, and its performance is tested in simulations. The results show that the proposed approach allocates agents effectively to the environment and enables them to carry out their mission quickly. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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16 pages, 6287 KiB

Open AccessArticle

Real–Sim–Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning

by Naijun Liu, Yinghao Cai, Tao Lu, Rui Wang and Shuo Wang

Appl. Sci. 2020, 10(5), 1555; https://doi.org/10.3390/app10051555 - 25 Feb 2020

Cited by 16 | Viewed by 5075

Abstract

Compared to traditional data-driven learning methods, recently developed deep reinforcement learning (DRL) approaches can be employed to train robot agents to obtain control policies with appealing performance. However, learning control policies for real-world robots through DRL is costly and cumbersome. A promising alternative is to train policies in simulated environments and transfer the learned policies to real-world scenarios. Unfortunately, due to the reality gap between simulated and real-world environments, the policies learned in simulated environments often cannot be generalized well to the real world. Bridging the reality gap is still a challenging problem. In this paper, we propose a novel real–sim–real (RSR) transfer method that includes a real-to-sim training phase and a sim-to-real inference phase. In the real-to-sim training phase, a task-relevant simulated environment is constructed based on semantic information of the real-world scenario and coordinate transformation, and then a policy is trained with the DRL method in the built simulated environment. In the sim-to-real inference phase, the learned policy is directly applied to control the robot in real-world scenarios without any real-world data. Experimental results in two different robot control tasks show that the proposed RSR method can train skill policies with high generalization performance and significantly low training costs. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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16 pages, 1466 KiB

Open AccessArticle

Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

by Xing Wu, Jorge Angeles, Ting Zou, Chao Sun, Qi Sun and Longjun Wang

Appl. Sci. 2020, 10(2), 676; https://doi.org/10.3390/app10020676 - 18 Jan 2020

Cited by 5 | Viewed by 2449

Abstract

Applying computer vision to mobile robot navigation has been studied for over two decades. One of the most challenging problems for a vision-based mobile robot involves accurately and stably tracking a guide path in the robot limited field of view under high-speed manoeuvres. Pure pursuit controllers are a prevalent class of path tracking algorithms for mobile robots, while their performance is rather limited to relatively low speeds. In order to cope with the demands of high-speed manoeuvres, a multi-loop receding-horizon control framework, including path tracking, robot control, and drive control, is proposed in this paper. This is done within the vision guidance of differential-driving wheeled mobile robots (DWMRs). Lamé curves are used to synthesize a trajectory with

G^{2}

-continuity in the field of view of the mobile robot for path tracking, from its current posture towards the guide path. The platform twist—point velocity and angular velocity—is calculated according to the curvature of the Lamé-curve trajectory, then transformed into actuated joint rates by means of the inverse-kinematics model; finally, the motor torques needed by the driving wheels are obtained based on the inverse-dynamics model. The whole multi-loop control process, initiated from Lamé-curve blending to computational torque control, is conducted iteratively by means of receding-horizon guidance to robustly drive the mobile robot manoeuvring close to the guide path. The results of numerical simulation show the effectiveness of our approach. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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20 pages, 3514 KiB

Open AccessArticle

Construction of Human Behavior Cognitive Map for Robots

by Wei-Zhi Lin, Sui-Hsien Wang and Han-Pang Huang

Appl. Sci. 2019, 9(23), 5026; https://doi.org/10.3390/app9235026 - 21 Nov 2019

Cited by 1 | Viewed by 2181

Abstract

With the advancement of robotics, the importance of service robots in society is increasing. It is crucial for service robots to understand their environment so that they can offer suitable responses to humans. To realize the use of space, robots primarily use an environment model. This paper is focused on the development of an environment model based on human behaviors. In this model, a new neural network structure called dynamic highway networks is applied to recognize humans’ behaviors. In addition, a two-dimensional pose estimator, Laban movement analysis, and the fuzzy integral are employed. With these methods, two new behavior-recognition algorithms are developed, and a method to record the relationship between behavior and environment is proposed. Based on the proposed environmental model, robots can identify abnormal behavior, provide an appropriate response and guide a person toward the desired normal behavior by identifying abnormal behavior. Simulations and experiments justify the proposed method with satisfactory results. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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15 pages, 3450 KiB

Open AccessArticle

Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network

by Hua Wang, Xi Chen and Zhonghua Miao

Appl. Sci. 2019, 9(18), 3806; https://doi.org/10.3390/app9183806 - 11 Sep 2019

Viewed by 1999

Abstract

In order to efficiently and exactly in tracking the desired path points, autonomous cleaning vehicles have to adapt their own behavior according to the perceived environmental information. This paper proposes a target points tracking control algorithm based on the Long Short-Term Memory network, which can generate the speed and yaw rate to arrive at the target point in real time. The target point is obtained by a parameter named foresight distance that is deduced based on the fuzzy control, whose inputs are the speed and yaw rate of the vehicle at the current point. The effectiveness of the proposed algorithm is illustrated by the simulation and field experiments. Compared with other classical algorithms, this algorithm can track the point sequence on straight path and multiple curvature path more accurately. The field experiment indicates the proposed controller is efficient in following the pre-defined path points, furthermore, it can make the autonomous cleaning vehicle run smoothly in the path which is disturbed by bounded disturbances. The distance errors can meet the actual requirement of the cleaning vehicle during the tracking process. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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20 pages, 11466 KiB

Open AccessArticle

3-D Point Cloud Registration Using Convolutional Neural Networks

by Wen-Chung Chang and Van-Toan Pham

Appl. Sci. 2019, 9(16), 3273; https://doi.org/10.3390/app9163273 - 09 Aug 2019

Cited by 14 | Viewed by 5814

Abstract

This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, this paper addresses the time-consuming problem of 3-D point cloud registration which is essential for the closed-loop industrial automated assembly systems that demand fixed time for accurate pose estimation. Firstly, two different descriptors are developed in order to extract coarse and detailed features of these point cloud data sets for the purpose of creating training data sets according to diversified orientations. Secondly, in order to guarantee fast pose estimation in fixed time, a seemingly novel registration architecture by employing two consecutive convolutional neural network (CNN) models is proposed. After training, the proposed CNN architecture can estimate the rotation between the model point cloud and a data point cloud, followed by the translation estimation based on computing average values. By covering a smaller range of uncertainty of the orientation compared with a full range of uncertainty covered by the first CNN model, the second CNN model can precisely estimate the orientation of the 3-D point cloud. Finally, the performance of the algorithm proposed in this paper has been validated by experiments in comparison with baseline methods. Based on these results, the proposed algorithm significantly reduces the estimation time while maintaining high precision. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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12 pages, 3099 KiB

Open AccessArticle

A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area

by Cheol Won Lee, Jun Dong Lee, Junho Ahn, Hyung Jun Oh, Jung Kyu Park and Heung Seok Jeon

Appl. Sci. 2019, 9(15), 3089; https://doi.org/10.3390/app9153089 - 31 Jul 2019

Cited by 4 | Viewed by 2457

Abstract

The grid map, representing area information with the number of cells, is a widely used mapping scheme for mobile robots and simultaneous localization and mapping (SLAM) processes. However, the tremendous amount of cells in a grid map for a detailed map representation results in overheads for memory space and computing paths in mobile robots. Therefore, to overcome the overhead of the grid map, this study proposes a new low overhead mapping scheme which the authors call as the Rmap that represents an area with variable sizes of rectangles instead of the number of cells in the grid map. This mapping scheme also provides an exploration path for obtaining new information for the unknown area. This study evaluated the performance of the Rmap in real environments as well as in simulation environments. The experiment results show that the Rmap can reduce the overhead of a grid map. In one of our experimental environments, the Rmap represented an area with 85% less memory than the grid map. The Rmap also showed better coverage performance compared with other previous algorithms. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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14 pages, 9269 KiB

Open AccessArticle

Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots

by Qiang Zhou and Xin Li

Appl. Sci. 2019, 9(14), 2908; https://doi.org/10.3390/app9142908 - 20 Jul 2019

Cited by 2 | Viewed by 3142

Abstract

When locating wall-climbing robots with vision-based methods, locating and controlling the wall-climbing robot in the pixel coordinate of the wall map is an effective alternative that eliminates the need to calibrate the internal and external parameters of the camera. The estimation accuracy of the homography matrix between the camera image and the wall map directly impacts the pixel positioning accuracy of the wall-climbing robot in the wall map. In this study, we focused on the homography estimation between the camera image and wall map. We proposed HomographyFpnNet and obtained a smaller homography estimation error for a center-aligned image pair compared with the state of the art. The proposed hierarchical HomographyFpnNet for a non-center-aligned image pair significantly outperforms the method based on artificially designed features + Random Sample Consensus. The experiments conducted with a trained three-stage hierarchical HomographyFpnNet model on wall images of climbing robots also achieved small mean corner pixel error and proved its potential for estimating the homography between the wall map and camera images. The three-stage hierarchical HomographyFpnNet model has an average processing time of 10.8 ms on a GPU. The real-time processing speed satisfies the requirements of wall-climbing robots. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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Review

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17 pages, 1313 KiB

Open AccessReview

Systematic Literature Review of Swarm Robotics Strategies Applied to Target Search Problem with Environment Constraints

by Zool Hilmi Ismail and Mohd Ghazali Mohd Hamami

Appl. Sci. 2021, 11(5), 2383; https://doi.org/10.3390/app11052383 - 08 Mar 2021

Cited by 12 | Viewed by 3204

Abstract

Target searching is a well-known but difficult problem in many research domains, including computational intelligence, swarm intelligence, and robotics. The main goal is to search for the targets within the specific boundary with the minimum time that is required and the obstacle avoidance that has been equipped in place. Swarm robotics (SR) is an extension of the multi-robot system that particularly discovers a concept of coordination, collaboration, and communication among a large number of robots. Because the robots are collaborating and working together, the task that is given will be completed faster compared to using a single robot. Thus, searching for single or multiple targets with swarm robots is a significant and realistic approach. Robustness, flexibility, and scalability, which are supported by distributed sensing, also make the swarm robots strategy suitable for target searching problems in real-world applications. The purpose of this article is to deliver a systematic literature review of SR strategies that are applied to target search problems, so as to show which are being explored in the fields as well as the performance of current state-of-the-art SR approaches. This review extracts data from four scientific databases and filters with two established high-indexed databases (Scopus and Web of Science). Notably, 25 selected articles fell under two main categories in environment complexity, namely empty space and cluttered. There are four strategies which have been compiled for both empty space and cluttered categories, namely, bio-inspired mechanism, behavior-based mechanism, random strategy mechanism, and hybrid mechanism. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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55 pages, 3123 KiB

Open AccessReview

A Survey of Planning and Learning in Games

by Fernando Fradique Duarte, Nuno Lau, Artur Pereira and Luis Paulo Reis

Appl. Sci. 2020, 10(13), 4529; https://doi.org/10.3390/app10134529 - 30 Jun 2020

Cited by 15 | Viewed by 5854

Abstract

In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications. Full article

(This article belongs to the Special Issue Intelligent Robotics)

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