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In this article, the fault-attack control problem is investigated for wheeled mobile robot (WMR) systems subjected to actuator faults, disturbances, communication attacks, and limited communication resources. An event-observer based compensation controller is presented. With the help of the observer estimation values and the attack sleep/active instant trigger information, the tracking control performance is realized for the robot system with the assistance of the neural network approximation technology. Concretely, first, the robot system dynamic model with actuator faults, disturbances, and attacks is established. Then, an event-based proportional–integral observer (PIO) is established. In the observer framework, a state estimator, an actuator fault efficiency estimator, and a disturbance estimator are embedded. Based on the observer outputs, a second-order adaptive sliding mode fault-compensation reliable controller is presented. In this controller framework, the fault compensation, disturbance attenuation, and the attack sleep/active time instant information are contained to guarantee the reliability and performance recoverability of the robot system. Furthermore, a dynamic even condition and an adaptive trigger scheme are constructed in the sensor and actuator channel to achieve the communication-efficient purpose. Finally, two cases of the robot system are performed to verify the system recoverability of the presented approach. • A class of wheeled mobile robot systems with actuator faults, disturbances, and communication attacks is established. • An event-based proportional–integral observer framework is designed for the robot system to estimate the states, actuator faults and lumped disturbances. • A novel observer-event-based adaptive fault-attack-tolerant control architecture is presented to simultaneously preserve and recover control performance for the robot system while saving the communication resources. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed control design problem for Nash equilibrium (NE) seeking of a multi-agent mobile robot system using only sampled neighbor measurements. To this end, we define new continuously differentiable variables, the so-called S-GRANE variables, that smoothly incorporate the sampled neighbor information within a sampling period. It is proven that the NE seeking problem can be restated as a regulation problem for the S-GRANE variables. Communication time-delays are also allowed with the new variables since the samples are processed after some fixed time has passed from their sampling time. Distributed control laws are then designed for the mobile robot multi-agent system dynamics based on the prescribed performance control (PPC) methodology that approximately solve the NE seeking problem by regulating the S-GRANE variables to a neighborhood of zero. Simulation results are shown that verify our theoretical analysis. [ABSTRACT FROM AUTHOR]

Aiming at the problem of trajectory tracking control for mobile robot nonlinear systems with non-repetitive uncertain parameters, we propose a novel neural network compensator-based robust iterative learning control (NNRILC) scheme to achieve excellent tracking performance and uncertainty compensation. The NNRILC scheme consists of a parallel structure that includes a robust iterative learning control (RILC) term and a neural network (NN) compensator. The RILC term is used to ensure robust control performance and promise closed-loop stability. The compensator based on the neural network is employed to handle the uncertainties resulted from the nonlinear dynamics as well as the suppressed disturbances in the mobile robot nonlinear systems. Additionally, the fourth-order Runge-Kutta algorithm is employed to solve the state differential equation of the mobile robot. Consequently, The H ∞ robust technique is used to construct an iterative learning control update rule to reduce the impact of disturbances. Meanwhile, the RILC scheme is designed to optimize the neural network initial parameters and weights of the NN compensator at each trial. Then the convergence of the proposed NNRILC scheme is analyzed, and the results are incorporated into the control update for the next process trial. Finally, the effectiveness of the proposed method is verified by mobile robot simulation. • A novel ILC scheme combining neural network compensator is designed to solve trajectory tracking problem. • The Runge-Kutta algorithm is introduced to solve the state differential equation and the controller equation. • A neural network compensator is constructed to compensate the suppressed noises, disturbances, and uncertainties. • The H∞ robust technique is used to suppress the noises, disturbances in the ILC process. [ABSTRACT FROM AUTHOR]

Abstract This work deals with the robust position tracking control problem for an omnidirectional mobile robot. To this aim, four continuous Sliding-Mode Control strategies are presented. The position and orientation of the platform are assumed to be the only available information about the system. To implement the controllers as output-feedback controllers, a High-Order Sliding-Mode Observer is implemented for each output signal. The proposed robust control strategies are able to deal with some classes of external disturbances. The closed-loop stability of each controller is proved by means of Lyapunov functions and homogeneity concepts. Simulations and experiments validate the applicability of the proposed controllers. [ABSTRACT FROM AUTHOR]

The paper presents the implementation of the high-level decentralized control system for a group of mobile robots (MR), which provides a common information space in a group, and performs decomposition of the main task (task from operator) and generates subgroups on the basis of agents' functional purpose. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a new path planning algorithm based on Probability and Fuzzy Logic (PFL) as a duality technique to enhance the performance of Fuzzy Logic alone. Fuzzy Logic interacts with the grading of obstacles existed in the path and probability lies over the decision to move the mobile robot. The fuzzy grading correspondence with the probabilistic decision is the primary function of moving the mobile robot towards the goal and the secondary is path planning which lies over the probability distribution function. The distance–speed combination rule is developed for effective navigation. The single and multiple mobile robot systems have been tested successfully in a dense environment in presence of obstacles (static and dynamic) and moving goal. The obtained results are optimal when compared to other navigational approaches in sense of navigational path length and time in the static and dynamic environment. Highlights • It studies the geometric constraints present in the environment and classifies the decision for generation of the obstacle-free path. • The probability is defined over fuzzy by selecting the path gradient with certainty. Optimized path decision, path transformation, and distributed decision are major advantages of the PFL controller over fuzzy Logic alone. • It is robust for navigation of single and multi-robot system in a complex-crowded environment in the presence of obstacles (static and dynamic) and moving goal situations. • It provides the capability of handling uncertain and imprecise information using distance–speed rules, performs real-time operation efficiently, gives easy combination and coordination of various behavior, ability to develop perception-action based strategies and easy implementation. • It gives better results when compared to other AI Controller in terms of path length and navigational time. [ABSTRACT FROM AUTHOR]

Abstract The development of a virtual scenario is desirable in many hostile environments such as in mining or sewer pipelines to inspect its current condition and to identify possible failures. Obtaining a virtual image through the reconstruction of the mineshaft or the inner pipe is a valuable tool for assessment and fault detection. The approach presented in this paper uses a fuzzy controlled robot so that the video information can reliably be taken. The robot fuzzy control guarantees its motion in a straight line while taking the images from the pipe. The control algorithm therefore prevents the robot from overturning while moving inside the pipe. Results from our 3D reconstruction approach using the autonomous mobile robot is presented. The proposed method overcomes the limitations of different detectors by using a novel combination of DoG and Harris Laplace. Once the frame selection is made, then the regions with affine transformations are used to find image features as correspondences using image descriptors such as SIFT (Scale-Invariant Feature Transform). The correspondences are required to obtain the orientation and position of the camera and to obtain the scene structure leading to the creation of the 3D virtual environment. The 3D reconstruction method outperformed similar commercial software which highlights its potential. Highlights • Novel 3D reconstruction approach using an autonomous sewer robot. • The novel algorithm is used to select appropriate correspondences. • Affine transformations are used to find features as dense local correspondences. • Image descriptors are used to obtain the orientation and position of the camera. • Image descriptors are used to obtain the scene structure for 3D reconstruction. [ABSTRACT FROM AUTHOR]

This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behavior for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behavior of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental results. [ABSTRACT FROM AUTHOR]

With the increasing application of wheeled mobile robots on soft terrains, the challenge of lateral and longitudinal slippage existing in the contact surface between the wheels and the terrain has attracted more attention. To address the difficulties caused by the lateral and longitudinal slippage, this paper proposes an improved linear active disturbance rejection control (LADRC) method for path tracking control of a six-wheeled corner steering rover. Based on the LADRC, the tracking differentiator and nonlinear state error feedback are introduced into the improved LADRC. By using the improved LADRC, the influence of disturbances in inputs can be attenuated and a higher regulating efficiency than LADRC can be achieved. The simulations validate the effectiveness of the proposed approach with a good tracking performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for mobile robot localization from images. The proposal is based on supervised learning using topological representations for the environment. The whole system comprises feature extraction and classification methods. With respect to feature extraction, we consider standard methods in digital image processing, e.g. Scale-Invariant Feature Transform and Local Binary Patterns. For classification, we apply machine learning methods with rejection option. A thorough assessment of the proposal is carried out using data from virtual and real indoor environments. Additionally, we compare the proposed architectures with classic localization systems using an omnidirectional camera. Based on the results, Spatial Moments combined with Bayes classifier is the best performing model, providing high accuracy rate (99.94%) and small computational time (47.3 μ  s and 0.165 s for classification and extraction, respectively). Finally, we observe that localization with rejection option increases efficiency and reliability of navigation in mobile robotics. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is presented for getting a solution of the mobile robot path planning problem based on Adaptive Particle Swarm Optimization (APSO). The proposed APSO algorithm is smarter than conventional PSO and widely used for solving the real time problems. In this work an objective function is framed considering the distance between robot to goal and obstacle respectively. The objective function is optimized with of APSO for solving the path planning process of robot. The different simulated experiments are performed to test the ability of the proposed algorithm. The performance of the robot path planning using APSO is compared to the performance of the conventional PSO in terms path length and time in static environments. It is focused that using new approach the robot can successfully avoid obstacle and reach the target with shorter time than conventional PSO. [ABSTRACT FROM AUTHOR]

The physical system design is more complex day after day due to the advancement in technology. In this paper, bond graph modelling of a multi domain physical system like skid steered mobile is presented. The proposed method is used to describe the dynamics of the system in various physical domains. By applying the bond and junction properties, mathematical model is extracted. In the second part of the paper, an unified technique is proposed to control the robot along the predefined complex trajectory. To track the desired trajectory, a flatness-based controller is proposed for the mobile robot. The developed dynamic model and the controller is modelled using BG V20 toolbox in MATLAB. Open loop as well as closed loop simulation are carried out to check the efficacy of the proposed model. Experimental trials performed in simulation environment revealed that the developed dynamic model and the controller for the robot can efficiently track the desired trajectory. [ABSTRACT FROM AUTHOR]

This paper presents a path planning algorithm for car-like mobile robots operating on a known static rough terrain environment. The purpose of this approach is to find collision free and feasible paths with minimum length and terrain roughness. First, a new workspace modeling method is proposed to model the rough terrain environment. Then, considering the nonholonomic constraints of car-like robots, a MOPSO (multi-objective particle swarm optimization) based method is used to solve the problem. In the proposed algorithm, a new updating method for particle’s global best position based on crowding radius is used to increase population diversity. And to improve the algorithm efficiency, a nonuniformity factor is adopted to update the particle’s position when the path collides with obstacles. Finally, two simulation tests are designed using Microsoft Robotics Developer Studio 4 and Matlab. Results show the advantages of the proposed algorithm in finding Pareto optimal paths. [ABSTRACT FROM AUTHOR]

This paper describes a new, innovative method by which multiple mobile robots can be detected by a laser scanner. Each robot incorporates bars in its construction which generate a significant pattern in the laser scan. The proposed technique allows a robust detection and control of successively moving robots, despite the partial shadowing through the bars. In this paper the optimal number of bars and their best arrangement for detection is shown. Furthermore the impact of different bar diameters is described. Increased visibility of the bars by the use of multiple laser scanners and their positioning to ensure detection of the robots is also described. Finally it describes the accuracy that can be achieved with this system. The position accuracy was determined by trials on an actual system. Simulations and experiments confirm that this is a reliable and precise method for position determination of multiple robots using a single sensor. [ABSTRACT FROM AUTHOR]

The article proposes a new robot programming-by-demonstration framework, which integrates a visual servoing tracking control to robustly follow a trajectory generated from observed demonstrations. The constraints originating from the use of a visual servoing controller are incorporated into the trajectory learning phase, to guarantee feasibility of the generated plan for task execution. The observational learning is solved as a constrained optimization problem, with an objective to generalize from a set of trajectories of salient features in the image space of a vision camera. The proposed approach is evaluated experimentally for learning trajectories acquired from kinesthetic demonstrations. [ABSTRACT FROM AUTHOR]

For robots to successfully integrate into everyday life, it is important that they can be effectively controlled by laypeople. However, the task of manually controlling mobile robots can be challenging due to demanding cognitive and sensorimotor requirements. This research explores the effect that the built environment has on the manual control of domestic service robots. In this study, a virtual reality simulation of a domestic robot control scenario was developed. The performance of fifty novice users was evaluated, and their subjective experiences recorded through questionnaires. Through quantitative and qualitative analysis, it was found that untrained operators frequently perform poorly at navigation-based robot control tasks. The study found that passing through doorways accounted for the largest number of collisions, and was consistently identified as a very difficult operation to perform. These findings suggest that homes and other human-orientated settings present significant challenges to robot control. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots (WMRs) often suffer from the longitudinal slipping when moving on the loose soil of the surface of the moon during exploration. Longitudinal slip is the main cause of WMRs' delay in trajectory tracking. In this paper, a nonlinear extended state observer (NESO) is introduced to estimate the longitudinal velocity in order to estimate the slip ratio and the derivative of the loss of velocity which are used in modelled disturbance compensation. Owing to the uncertainty and disturbance caused by estimation errors, a multi-objective controller using the mixed H 2 / H ∞ method is employed to ensure the robust stability and performance of the WMR system. The final inputs of the trajectory tracking consist of the feedforward compensation, compensation for the modelled disturbances and designed multi-objective control inputs. Finally, the simulation results demonstrate the effectiveness of the controller, which exhibits a satisfactory tracking performance. [ABSTRACT FROM AUTHOR]

This paper presents the design of fuzzy control system for an omnidirectional robot. The control structure of omnidirectional robot was described. The kinematics and dynamics of the mobile robot are presented. The design of control system is implemented for position and orientation angle in order to control the linear and angular speed of the omnidirectional robot. The designs of fuzzy controllers are performed and then these are used for the control of the holonomic 4-wheel-driven soccer robots. The designed fuzzy control algorithm has been extensively tested in simulation and provided satisfactory results in run time. The controller presented in this paper provides an optimal solution to minimize the differences between the reference trajectory and the current output. The obtained results demonstrate the effectiveness of proposed algorithms in robot soccer control. [ABSTRACT FROM AUTHOR]

This paper presents a novel, decentralized control algorithm to address the problem of controlling of multiple, network connected, nonholonomic mobile robots to achieve a collision-free formation and heading consensus simultaneously. Our approach transforms the formation and heading consensus problem to a pose regulation problem. The pose is derived by a virtual graph, which is an isomorphic mapping of the graph of the network connected systems. If the system is holonomic, traditional consensus on the virtual graph can stabilize the real system to the desired formation. In the case of nonholonomic systems, such as mobile robots, each robot’s pose then is regulated to the known consensus pose by a novel smooth and continuous control law. The control law also induces the real robot systems to achieve not only the desired formation but also heading consensus. We prove the proposed control schemes are globally asymptotically stable. Theoretical analysis, several simulations, and experiments have been conducted to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a method of designing variable structure control systems for robots. As the on-board robot computational resources are limited, but in some cases the demands imposed on the robot by the user are virtually limitless, the solution is to produce a variable structure system. The task dependent part has to be exchanged, however the task governs the activities of the robot. Thus not only exchange of some task-dependent modules is required, but also supervisory responsibilities have to be switched. Such control systems are necessary in the case of robot companions, where the owner of the robot may demand from it to provide many services. [ABSTRACT FROM AUTHOR]

This paper presents a computationally inexpensive generic method to utilize the maximum velocity and acceleration of an omnidirectional mobile robot. The proposed method is based on the inverse kinematic and inverse dynamic models by defining novel velocity and acceleration reserve multipliers respectively. The defined multipliers enable a computationally inexpensive solution and give representative index numbers, showing the amount of utilized resources during online computation. The model was applied to control a kiwi drive mobile robot and validated by experimental measurements. An open-source Robot Operating System (ROS) catkin C + + package was published to enable the feasible implementation of the results. [ABSTRACT FROM AUTHOR]

This paper contributes an extensive review of the history from the former robotics to the present providing a particular emphasis on innovative technical contents of robotic exploration vehicles. To this end, a comprehensive study with a representative collection of 100 mobile robots along the history was performed for which a robot’s statistical profile was obtained considering aspects such as weight, size, number of wheels, and speed of movement. In addition, an exhaustive bibliometric analysis has been conducted over 8120 contributions between 1963 and 2015. The study on the scientific literature found that, thought mobile robotics is a research field being displaced by other disciplines of higher scientific return (e.g., humanoid robots, unmanned aircraft systems or intelligent autonomous vehicles), it is nevertheless confirmed the continuity of mobile robotics with the aim of developing advanced science at the expense of forthcoming space exploration missions. Therefore, this paper attempts to address what is the current state-of-the-art and what are the future challenges set in mobile robotics. [ABSTRACT FROM AUTHOR]

Trajectory generation methods play an important role in robotics since they are essential for the execution of actions. In this paper we present a novel trajectory generation method for generalization of accurate movements with boundary conditions. Our approach originates from optimal control theory and is based on a second order dynamic system. We evaluate our method and compare it to the state of the art movement generation methods in both simulations and real robot experiments. We show that the new method is very compact in its representation and can reproduce reference trajectories with zero error. Moreover, it has most of the features of the state of the art movement generation methods such as robustness to perturbations and generalization to new position and velocity boundary conditions. We believe that, due to these features, our method may have potential for robotic applications where high accuracy is required paired with flexibility, for example, in modern industrial robotic applications, where more flexibility will be demanded as well as in medical robotics. [ABSTRACT FROM AUTHOR]

Feature-based and holistic methods present two fundamentally different approaches to relative-pose estimation from pairs of camera images. Until now, there has been a lack of direct comparisons between these methods in the literature. This makes it difficult to evaluate their relative merits for their many applications in mobile robotics. In this work, we compare a selection of such methods in the context of an autonomous domestic cleaning robot. We find that the holistic Min-Warping method gives good and fast results. Some of the feature-based methods can provide excellent and robust results, but at much slower speeds. Other such methods also achieve high speeds, but at reduced robustness to illumination changes. We also provide novel image databases and supporting data for public use. [ABSTRACT FROM AUTHOR]

Recent advances in visual motion detection and interpretation have made possible the rising of new robotic systems for autonomous and active surveillance. In this line of research, the current work discusses motion perception by proposing a novel technique that analyzes dense flow fields and distinguishes several regions with distinct motion models. The method is called Wise Optical Flow Clustering (WOFC) and extracts the moving objects by performing two consecutive operations: evaluating and resetting. Motion properties of the flow field are retrieved and described in the evaluation phase, which provides high level information about the spatial segmentation of the flow field. During the resetting operation, these properties are combined and used to feed a guided segmentation approach. The WOFC requires information about the number of motion models and, therefore, this paper introduces a model selection method based on a Bayesian approach that balances the model’s fitness and complexity. It combines the correlation of a histogram-based analysis with the decay ratio of the normalized entropy criterion. This approach interprets the flow field and gives an estimative about the number of moving objects. The experiments conducted in a realistic environment have proved that the WOFC presents several advantages that meet the requirements of common robotic and surveillance applications: is computationally efficient and provides a pixel-wise segmentation, comparatively to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Model-based predictive control approaches can be successfully applied to the trajectory tracking of wheeled mobile-robot applications if the process nonlinearity is considered, if real-time performance is achieved and if assumptions made in the control-law design are met when applied to a particular process. In this paper, continuous tracking-error model-based predictive control is presented. The controller’s optimal actions are obtained from an explicit solution of the optimization criteria, which enables fast real-time applications. Due to its design in continuous time, its usage is not limited to the uniform sampling restrictions of a host computer, as is usually the case in discrete time design. Therefore, better performance is obtained in applications with non-uniform sampling, which is natural in many situations due to imperfect sensors, mismatched clocks, nondeterministic control delays or because of the unknown time of the pre-processing. The controller-design parameters are insensitive to the sampling time period, which contributes to simpler applications and greater robustness of the controller. [ABSTRACT FROM AUTHOR]

Recent work on simultaneous trajectory estimation and mapping (STEAM) for mobile robots has used Gaussian processes (GPs) to efficiently represent the robot’s trajectory through its environment. GPs have several advantages over discrete-time trajectory representations: they can represent a continuous-time trajectory, elegantly handle asynchronous and sparse measurements, and allow the robot to query the trajectory to recover its estimated position at any time of interest. A major drawback of the GP approach to STEAM is that it is formulated as a batch trajectory estimation problem. In this paper we provide the critical extensions necessary to transform the existing GP-based batch algorithm for STEAM into an extremely efficient incremental algorithm. In particular, we are able to vastly speed up the solution time through efficient variable reordering and incremental sparse updates, which we believe will greatly increase the practicality of Gaussian process methods for robot mapping and localization. Finally, we demonstrate the approach and its advantages on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

Markov localization and its variants are widely used for mobile robot localization. These methods assume Markov independence of observations, implying that the observations can be entirely explained by a map. However, in real human environments, robots frequently make unexpected observations due to unmapped static objects like chairs and tables, and dynamic objects like humans. We therefore introduce Episodic non-Markov Localization (EnML), which reasons about the world as consisting of three classes of objects: long-term features corresponding to permanent mapped objects, short-term features corresponding to unmapped static objects, and dynamic features corresponding to unmapped moving objects. Long-term features are represented by a static map, while short-term features are detected and tracked in real-time. To reason about unexpected observations and their correlations across poses, we augment the Dynamic Bayesian Network for Markov localization to include varying edges and nodes, resulting in a novel Varying Graphical Network representation. The maximum likelihood estimate of the belief is incrementally computed by non-linear functional optimization. By detecting timesteps along the robot’s trajectory where unmapped observations prior to such time steps are unrelated to those afterwards, EnML limits the history of observations and pose estimates to “episodes” over which the belief is computed. We demonstrate EnML using different types of sensors including laser rangefinders and depth cameras, and over multiple datasets, comparing it with alternative approaches. We further include results of a team of indoor autonomous service mobile robots traversing hundreds of kilometers using EnML. [ABSTRACT FROM AUTHOR]

While recent advancements in using sophisticated onboard sensors on robotic platforms have made it possible to consign various tasks like exploration, mapping, and flocking to teams of mobile robots, some issues like handling extensive amount of data, high dependency on sensors’ performance, and high expenses emerge. In this paper, the problem of mapping unknown environments by a team of heterogeneous mobile robots with limited and inexpensive sensing abilities is addressed. The concepts of Information Space and sensor models have been employed to plan the motions of robots with limited sensory data in order to accomplish the common goal of mapping the entire workspace as complete as possible. Also, a cooperation architecture is proposed to fuse and interrelate the dissimilar data obtained by individual heterogeneous robots and allocate various exploratory tasks to each of them in order to complete the map. The algorithm works with various limited sensing models, such as depth-limited boundary distance sensor, quadridirectional depth sensor, depth-limited gap sensor, and depth-limited radially-bounded depth senor. Based on each sensor model, the best moving strategy is introduced to maximize the workspace coverage for each robot. The proposed algorithm, which yields a geometric map of the environment, is implemented in diverse simulated problems both with and without sensing noises, and the results and comparisons with a recent related work show that it is able to reliably construct maps of simply-connected and multiply-connected environments with convex and concave obstacles. In the presence of noises, the produced maps had about 12.4% false positive and 3.3% false negative errors on average. Also, some sensitivity analyses are done on the effects of workspace size and number of robots on the mapping time. [ABSTRACT FROM AUTHOR]

Industrial robots conceived for high-precision assembly processes are demanded to match the best trade-off between precision and speed. This research presents a new approach for defining the motion profiles of robots, based on a smooth trajectory generation model. Execution time is minimized by a novel multi-variable optimization approach, taking into account the performance of each joint and the requirements of extremely precise assembly tasks. The proposed method, tested on a modular robot for the optoelectronics industry, provides jerk-bounded trajectories up to 39% faster compared to the best performing motion planning approaches, while offering the possibility to adapt these trajectories for degraded operating conditions. [ABSTRACT FROM AUTHOR]

Intelligent navigation, path following and obstacle avoidance are necessary features for mobile robots to permit the navigation in a dynamic environment and to train intelligent robots. In this context, this paper examines a dynamic feedback control design, firstly to treat the trajectory tracking control of a mobile manipulator with model uncertainties and external disturbances and secondly to avoid collision with obstacles during the navigation to add more functionality to mobile robots. In order to overcome the unknown perturbation effects and uncertainties, we use an adaptive estimator such that the mobile manipulator velocity converges to the desired trajectory. Furthermore, we apply a fuzzy controller to avoid collision. The contribution of this work is that the mobile manipulator executes two acts together which are the tracking of the desired trajectory despite of the uncertainties and disturbances, and the avoiding of obstacles during the trajectory tracking. Simulation results are given to illustrate the efficiency of the proposed adaptive-fuzzy controller. [ABSTRACT FROM AUTHOR]

The paper explain analysis of inertial navigation system and accelerometric, gyroscopic sensors and describe possibilities of their application for inertial navigation of mobile robot. Such controlling system allows to monitor exact position of robot. These information can be applied for robot controlling, its autonomous control or its tracking. Inertial navigation is completely autonomous and independent from surroundings, i.e. the system is resistant from external influences as magnetic disturbances, electronically disturbance, signal deformation, etc. For mobile robots to be successful, they have to move safely in environments populated and dynamic. While recent research has led to a variety of localization methods that can track robots well in static environments, we still lack methods that can robustly localize mobile robots in dynamic environments. [ABSTRACT FROM AUTHOR]

The haptic terrain classification is an essential component of a mobile walking robot control system, ensuring proper gait adaptation to the changing environmental conditions. In practice, such components are a part of an autonomous system and thus have to be lightweight, provide fast inference time, and guarantee robustness to minor changes in recorded sensory data. We propose transformer-based HAPTR and HAPTR2 terrain classification methods that use force and torque measurements from feet to meet these requirements. For reliable comparison of the proposed solutions, we adapt two classical machine learning algorithms (DTW-KNN and ROCKET), one temporal convolution network (TCN), and use the state-of-the-art CNN-RNN. The experiments are performed on publicly available PUTany and QCAT datasets. We show that the proposed HAPTR and HAPTR2 methods achieve accuracy on par or better than state-of-the-art approaches with a lower number of parameters, faster inference time, and improved robustness to input signal distortions. These features make HAPTR and HAPTR2 excel in terrain recognition tasks when considering real-world requirements. • Fast and lightweight transformer-based approach for the terrain classification. • Modality Attention Layer improves the system's robustness in real-world scenarios. • Autonomy requirements inspired our evaluation of terrain classification algorithms. • The benchmark on two open source datasets creates a baseline for further research. [ABSTRACT FROM AUTHOR]

The vision ability is fundamentally important for a mobile robot. Many aspects have been investigated during the past few years, but there still remain questions to be answered. This work mainly focuses on the task of road detection, which is considered as the first step for a robot to become moveable. The proposed method combines the depth clue with traditional RGB information and is divided into three steps: depth recovery and superpixel generation, weakly supervised SVM classification and context-aware label transfer. The main contributions made in this paper are (1) Design a novel superpixel based context-aware descriptor by utilizing depth map. (2) Conduct label transfer in an efficient nearest neighbor search and a temporal MRF model. (3) Update the learned model adaptively with the changing scene. Experimental results on a publicly available dataset justify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Indoor localization of mobile units is critical for subsequent task development of intelligent living technology. In this work, a modified wiimote-based 2D localization scheme is proposed and experimentally validated for indoor mobile robot tracking and control tasks. This scheme uses one wiimote to monitor the position of at least two IR LEDs simultaneously for determining both translation and rotation motions of the wiimote-mounted carrier. An algorithm is then developed for converting the wiimote readouts to the position and orientation of the corresponding mobile unit based on the major axes identification, coordinate transformation, and position updating. The scheme and the algorithm are then validated by tracking the wiimote location on a two-axis linear servomotor. Finally, by integrating this global positioning scheme with feedback control, trajectory tracking of an omni-wheel based mobile robot is performed to demonstrate the importance of the scheme in indoor smart living technology. [ABSTRACT FROM AUTHOR]

In this paper, a novel bio-inspired learning control approach (BILCA) for mobile robots based on Learning from Demonstration (LfD), Firefly Algorithm (FA), and homography between current and target camera view is developed. BILCA consists of two steps: (i) first step in which the actuator commands are learned using FA and demonstrations of desired behavior, and (ii) second step in which the obtained wheel commands are evaluated through the real world experiment. Two different problems are considered in this study: trajectory reproduction, and generation of visual control commands for correction of robot orientation. Developed simulations are used to evaluate BILCA in the domain of learning actuator commands for reproduction of different complex trajectories. Results show that the bigger firefly swarms produce better results in terms of accuracy in the final mobile robot pose, and that the desired trajectory is reproduced with minimal error in final control iteration. Likewise, simulations prove that the FA outperforms other metaheuristic techniques. Experiment conducted on a real mobile robot in indoor environment unifies two considered problems within a single transportation task. Depending of the feature position in the image plane, the homography controller for forward motion or the BILCA based controller for robot orientation correction is employed. Experimental results show the applicability and effectiveness of the developed intelligent approach in real world conditions. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of steering a single or a group of autonomous nonholonomic mobile robots to enclose a target of interest. We develop control schemes which require only local bearing measurements, and deal with encirclement of two types of targets: point target and disk target. When a single robot is used, circumnavigation schemes are proposed to achieve effective encirclement of the target. It is shown that under the proposed control schemes, the robot can circle the target from a prescribed distance without distance measurement. When multiple robots are deployed, the control schemes developed for single robot are modified by introducing a coordination mechanism, which drives the robots to distribute evenly around the target. We further discuss a control scheme that moves the robots to form a static circular formation around the target based on feedback linearization. All the proposed control schemes are validated in experiments on a group of two-wheeled differential drive mobile robots. [ABSTRACT FROM AUTHOR]

In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles. [ABSTRACT FROM AUTHOR]

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance. [ABSTRACT FROM AUTHOR]

Abstract: The Simultaneous Localization And Mapping by an autonomous mobile robot–known by its acronym SLAM–is a computationally demanding process for medium and large-scale scenarios, in spite of the progress both in the algorithmic and hardware sides. As a consequence, a robot with SLAM capabilities has to be equipped with the latest computers whose weight and power consumption might limit its autonomy. This paper describes a visual SLAM system based on a distributed framework where the expensive map optimization and storage is allocated as a service in the Cloud, while a light camera tracking client runs on a local computer. The robot onboard computers are freed from most of the computation, the only extra requirement being an internet connection. The data flow from and to the Cloud is low enough to be supported by a standard wireless connection. The experimental section is focused on showing real-time performance for single-robot and cooperative SLAM using an RGBD camera. The system provides the interface to a map database where: (1) a map can be built and stored, (2) stored maps can be reused by other robots, (3) a robot can fuse its map online with a map already in the database, and (4) several robots can estimate individual maps and fuse them together if an overlap is detected. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the output consensus problem of tracking a desired trajectory for a class of systems consisting of multiple nonlinear subsystems with intrinsic mismatched unknown parameters. The subsystems are allowed to have non-identical dynamics, whereas with similar structures and the same yet arbitrary system order. And the communication status among the subsystems can be represented by a directed graph. Different from the traditional centralized tracking control problem, only a subset of the subsystems can obtain the desired trajectory information directly. A distributed adaptive control approach based on backstepping technique is proposed. By introducing the estimates to account for the parametric uncertainties of the desired trajectory and its neighbors’ dynamics into the local controller of each subsystem, information exchanges of online parameter estimates and local synchronization errors among linked subsystems can be avoided. It is proved that the boundedness of all closed-loop signals and the asymptotically consensus tracking for all the subsystems’ outputs are ensured. A numerical example is illustrated to show the effectiveness of the proposed control scheme. Moreover, the design strategy is successfully applied to solve a formation control problem for multiple nonholonomic mobile robots. [Copyright &y& Elsevier]

Abstract: An adaptive output feedback tracking controller for nonholonomic mobile robots is proposed to guarantee that the tracking errors are confined to an arbitrarily small ball. The major difficulties are caused by simultaneous existence of nonholonomic constraints, unknown system parameters and a quadratic term of unmeasurable states in the mobile robot dynamic system as well as their couplings. To overcome these difficulties, we propose a new adaptive control scheme including designing a new adaptive state feedback controller and two high-gain observers to estimate the unknown linear and angular velocities respectively. It is shown that the closed loop adaptive system is stable and the tracking errors are guaranteed to be within the pre-specified bounds which can be arbitrarily small. Simulation results also verify the effectiveness of the proposed scheme. [Copyright &y& Elsevier]

Abstract: Adaptation of mobile robot controllers commonly requires the computation of optimal points of operation. Specifically, for miniature mobile robots with serious computational limitations, that are typical of embedded systems, one of the main challenges is the adaptation of efficient computational methods in order to find solutions of complex optimization problems, which demand large execution times. This drawback compels the design of high-performance parallel optimization algorithms which must run over embedded system platforms. This paper describes how adequate hardware implementations of the Particle Swarm Optimization (PSO) algorithm can be useful for real time adaptation of mobile robot controllers. For achieving this, a new architecture is proposed, which is based on an FPGA implementation of the opposition-based learning (OBL) approach applied to the PSO (for short HPOPSO), and which explores the intrinsic parallelism of this algorithm in order to adjust the weights of a neural robot controller in real time according to desired behaviors. The proposed HPOPSO was applied to the learning-from-demonstration problem in which a teacher performs executions of the desired behavior. Effectiveness of the proposed architecture was demonstrated by numerical simulations and the feasibility of the adaptive behavior of the neural robot controller was confirmed for two obstacle avoidance case studies that were preserved when one or more failures on the distance sensors occur. The HPOPSO, which uses the OBL technique, improves the quality of the solutions in comparison with the standard PSO. Comparisons of the adaptation time between hardware and software approaches have demonstrated the suitability of the FPGA implementation of the proposed HPOPSO for attending specific requirements of embedded system applications. [Copyright &y& Elsevier]

Abstract: This paper describes a novel approach in formation control for mobile robots in the active target tracking problem. A nonlinear model predictive formation controller (NMPFC) for target perception was implemented to converge a group of mobile robots toward a desired target. The team must also maintain a desired formation following a target while it is moving, or follow a leader in the case of target’s absence. The structure details of the controller, as well as a mathematical analysis of the formation model used, are presented. Furthermore, results of simulations and experiments with real robots are presented and discussed. [Copyright &y& Elsevier]

Abstract: As the applications of mobile robotics evolve it has become increasingly less practical for researchers to design custom hardware and control systems for each problem. This paper presents a new approach to control system design in order to look beyond end-of-lifecycle performance, and consider control system structure, flexibility, and extensibility. Towards these ends the Control ad libitum philosophy was proposed, stating that to make significant progress in the real-world application of mobile robot teams the control system must be structured such that teams can be formed in real-time from diverse components. The Control ad libitum philosophy was applied to the design of the HAA (Host, Avatar, Agent) architecture: a modular hierarchical framework built with provably correct distributed algorithms. A control system for mapping, exploration, and foraging was developed using the HAA architecture and evaluated in three experiments. First, the basic functionality of the HAA architecture was studied, specifically the ability to: (a) dynamically form the control system, (b) dynamically form the robot team, (c) dynamically form the processing network, and (d) handle heterogeneous teams and allocate robots between tasks based on their capabilities. Secondly, the control system was tested with different rates of software failure and was able to successfully complete its tasks even when each module was set to fail every 0.5–1.5 min. Thirdly, the control system was subjected to concurrent software and hardware failures, and was still able to complete a foraging task in a 216 m2 environment. [Copyright &y& Elsevier]