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Abstract This article proposes a method for sensor fusion between odometers, gyroscope, accelerometer, magnetometer and visual landmark localization system. The method is designed for estimation of all 6 degrees of freedom (both translation and attitude) of a wheeled robot moving in uneven terrain. The fusion method is based on continuous estimation of the mean square error of each estimated value and allows different sampling rates of each sensor. Due to the simple implementation, it is suitable for real-time processing in the low-cost hardware. In order to evaluate the precision of the estimated position, stochastical models of sensors (with parameters matching real hardware sensors) were used and random trajectories were simulated. The virtual experiments showed that the method is resistant to the failure of any sensor except the odometers; however, each sensor provides improvement in the resultant precision. Highlights • Article proposes new sensor fusion method combining variety of sensors' readings. • Method utilizes inertial sensors, landmarks, odometers and magnetometer. • The method is suitable for estimating the position of a wheeled vehicle. • The simplicity of the method allows its implementation into low-cost hardware. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a new approach to view-based localization and navigation in outdoor environments, which are indispensable functions for mobile robots. Several approaches have been proposed for autonomous navigation. GPS-based systems are widely used especially in the case of automobiles, however, they can be unreliable or non-operational near tall buildings. Localization with a precise 3D digital map of the target environment also enables mobile robots equipped with range sensors to estimate accurate poses, but maintaining a large-scale outdoor map is often costly. We have therefore developed a novel view-based localization method SeqSLAM++ by extending the conventional SeqSLAM in order not only to robustly estimate the robot position comparing image sequences but also to cope with changes in a robot's heading and speed as well as view changes using wide-angle images and a Markov localization scheme. According to the direction to move provided by the SeqSLAM++, the local-level path planner navigates the robot by setting subgoals repeatedly considering the structure of the surrounding environment using a 3D LiDAR. The entire navigation system has been implemented in the ROS framework, and the effectiveness and accuracy of the proposed method was evaluated through off-line/on-line navigation experiments. [ABSTRACT FROM AUTHOR]

Abstract A world model representing the elements in a robot's environment needs to maintain a correspondence between the objects being observed and their internal representations, which is known as the anchoring problem. Anchoring is a key aspect for an intelligent robot operation, since it enables high-level functions such as task planning and execution. This work presents an anchoring system that continually integrates new observations from a 3D object recognition algorithm into a probabilistic world model. Our system takes advantage of the contextual relations inherent to human-made spaces in order to improve the classification results of the baseline object recognition system. To achieve that, the system builds a graph-based world model containing the objects in the scene (both in the current and previously perceived observations), which is exploited by a Probabilistic Graphical Model (PGM) in order to leverage contextual information during recognition. The world model also enables the system to exploit information about objects beyond the current field of view of the robot sensors. Most importantly, this is done in an online fashion, overcoming both the disadvantages of single-shot recognition systems (e.g., limited sensor aperture) and offline recognition systems that require prior registration of all frames of a scene (e.g., dynamic scenes, unsuitability for plan-based robot control). We also propose a novel way to include the outcome of local object recognition methods in the PGM, which results in a decrease in the usually high model learning complexity and an increase in the system performance. The system performance has been assessed with a dataset collected by a mobile robot from restaurant-like settings, obtaining positive results for both its data association and object recognition capabilities. The system has been successfully used in the RACE robotic architecture. Highlights • Anchoring system keeps track of objects for robot planning, learning and execution. • Builds online graph-based 3D world model of objects in the environment. • Can integrate arbitrary object recognition method. • Exploits context between objects to improve object recognition results. [ABSTRACT FROM AUTHOR]

Abstract This study proposes a mechanism and a methodology for large force input to the environment by a mobile robot. To determine the limits on the force that a mobile robot can apply to a target object, we analyzed the forces between the robot, ground, and object, and the frictional-force limits between any two of these three bodies. To prevent the mobile robot from falling during the large-force application, the manipulator is connected to the robot via a passive rotational joint. This mechanism enables the mobile robot to search the environmental parameters. A new mobile robot fitted with the proposed mechanism, named ARODA, was developed. In a validation experiment, the developed mobile robot successfully tilted a relatively large and heavy object while searching the environmental parameters (the frictional coefficients of the floor and object and the size of the object). Equipped with the proposed mechanism, the mobile robot refrained from falling while applying a large force to the object by trial and error. Highlights • A mechanism for a mobile robot to apply a large force without falling is proposed. • The mobile robot with the proposed mechanism named ARODA is developed. • The ARODA successfully tilted a heavy object while searching the unknown parameters. [ABSTRACT FROM AUTHOR]

Abstract Public datasets are becoming extremely important for the scientific and industrial community to accelerate the development of new approaches and to guarantee identical testing conditions for comparing methods proposed by different researchers. This research presents the Urban@CRAS dataset that captures several scenarios of one iconic region at Porto Portugal These scenario presents a multiplicity of conditions and urban situations including, vehicle-to-vehicle and vehicle-to-human interactions, cross-sides, turn-around, roundabouts and different traffic conditions. Data from these scenarios are timestamped, calibrated and acquired at 10 to 200 Hz by through a set of heterogeneous sensors installed in a roof of a car. These sensors include a 3D LIDAR, high-resolution color cameras, a high-precision IMU and a GPS navigation system. In addition, positioning information obtained from a real-time kinematic satellite navigation system (with 0.05m of error) is also included as ground-truth. Moreover, a benchmarking process for some typical methods for visual odometry and SLAM is also included in this research, where qualitative and quantitative performance indicators are used to discuss the advantages and particularities of each implementation. Thus, this research fosters new advances on the perception and navigation approaches of autonomous robots (and driving). Highlights • Public dataset that captures a multiplicity of conditions and urban situations. • Dataset acquired by through a set of heterogeneous sensors (in roof of a car). • Ground-truth with an accurate positioning information obtained from a RTK system. • Benchmarking of vOdometry and vSLAM methods. [ABSTRACT FROM AUTHOR]

Abstract Despite the comprehensive development in the field of navigation algorithms for mobile robots, the research on performance metrics and evaluation procedures for making standardized quantitative comparison between different algorithms has gained attention only recently. This work attempts to contribute with such effort by introducing a performance metric for the assessment of collision avoidance algorithms for mobile robots. The proposed metric comprehensively evaluates the actions taken by the objects and their consequences, in a given scenario of any given collision avoidance algorithm, based on the concept of probability of collision. The contribution of the paper encompasses the definition of the metric, the methodology to estimate the metric, and the framework to apply the metric for any given scenario. Experiments and numerical simulations are conducted to validate and demonstrate the effectiveness of the proposed metric in performance evaluation and comparison among different collision avoidance algorithms. [ABSTRACT FROM AUTHOR]

Abstract Robust and fast motion estimation and mapping is a key prerequisite for autonomous operation of mobile robots. The goal of performing this task solely on a stereo pair of video cameras is highly demanding and bears conflicting objectives: on one hand, the motion has to be tracked fast and reliably, on the other hand, high-level functions like navigation and obstacle avoidance depend crucially on a complete and accurate environment representation. In this work, we propose a two-layer approach for visual odometry and SLAM with stereo cameras that runs in real-time and combines feature-based matching with semi-dense direct image alignment. Our method initializes semi-dense depth estimation, which is computationally expensive, from motion that is tracked by a fast but robust keypoint-based method. Experiments on public benchmark and proprietary datasets show that our approach is faster than state-of-the-art methods without losing accuracy and yields comparable map building capabilities. Moreover, our approach is shown to handle large inter-frame motion and illumination changes much more robustly than its direct counterparts. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a human-inspired framework for grasping domestic flat objects placed on planar support surfaces. In particular, three grasp strategies are proposed which aim to pinch small flat objects from different scenes. The framework uses representations of the robotic hand, the support surface and the target object which encapsulate rough information for the scene. Furthermore, the strategies exploit the environmental constraint of the support surface by establishing compliant contact with it, which leads to increased robustness against object geometry uncertainties as well as pose estimation errors possibly introduced by the perception system. This is inspired by how humans perform relative grasping tasks with object pose and geometry uncertainties by using compliant contact with the support surfaces. Finally, the strategy selection is determined by a decision making procedure which uses the current scene representation. Highlights • Grasping flat object off planar surfaces utilizing contact with the environment. • Three grasp strategies enabled by a decision maker given a scene. • Object point cloud availability and lack of clutter around the object is required. • Extensive experimentation with various objects and robotic hands. [ABSTRACT FROM AUTHOR]

Abstract Recently, MANET (Mobile Ad Hoc Network) researchers have shown increased interest in using mobile robot technology for their testbed platforms. Despite the existence of review papers that discuss the usage of mobile robot technology pertaining to a MANET testbed from the perspective of a MANET researcher, said findings are rather lacklustre as it is not the sole purpose of said reviews. Hence, this review aims to comprehensively discuss and analyse MANET testbeds that were facilitated with mobile robot technology for previous undertaken research. To enable readers to keep abreast with mobile robot technology used in previous research, whilst presenting the advantages and disadvantages of said methods, this review will first superficially discuss prior robot based MANET testbed facilities, before presenting technical analysis overview and critical analysis. Additionally, suggestions to heighten mobility mechanisms by using mobile robots to be more practical, easy and inexpensive are also included in this paper. The technical and critical content of this review is expected to be a source of reference for other MANET researchers interested in the most suitable mobile robots to ensure real mobility in their MANET testbeds. [ABSTRACT FROM AUTHOR]

Abstract Point cloud registration is an important and fundamental building block of mobile robotics. It forms an integral part of the processes of mapping, localization, object detection and recognition, loop closure and many other applications. Throughout the years, registration has been addressed in different ways, based on local features, global descriptor or object-based. However, all these techniques give meaningful results only if the input data are of the same type and density (resolution). Recently, with the technological revolution of 3D sensors, accurate ones producing dense clouds have appeared as well as others faster, more compatible with real-time applications, producing sparse clouds. Accuracy and speed are two sought-after concepts in every robotic application including those cited above, which involves the simultaneous use of both types of sensors, resulting in sparse–dense (or dense–sparse) point cloud registration. The difficulty of sparse to dense registration lies in the fact that there is no direct correspondence between each point in the two clouds, but rather a point equivalent to a set of points. In this paper, a novel approach that surpasses the notion of density is proposed. Its main idea consists in matching points representing each local surface of source cloud with the points representing the corresponding local surfaces in the target cloud. Experiments and comparisons with state-of-the-art methods show that our approach gives better performance. It handles registration of point clouds of different densities acquired by the same sensor with varied resolution or taken from different sensors. [ABSTRACT FROM AUTHOR]

Sampling-based, search-based, and optimization-based motion planners are just some of the different approaches developed for motion planning problems. Given the wide variety of application tackled by autonomous mobile manipulators, the question “which planner to choose” may be tough. In this paper, we review the state of the art of the most common approaches, and present a set of benchmarks with the aim to provide not only a theoretical review but also a qualitative/quantitative comparison of the algorithms. Our purpose is to provide an insight and analyze their performance with respect to different metrics. The results are based on an Underwater Vehicle Manipulator System UVMS , although they can be extended to terrestrial and aerial robots as well. The paper uses these results to formalize a set of guidelines for the selection process of the most appropriate approach, for a given problem/requirements. [ABSTRACT FROM AUTHOR]

A novel transformable wheel-legged robot, namely Land Devil Ray (LDR), is proposed for search and rescue mission in complex terrains. Inspired from spatial folding mechanism and metamorphic mechanism, a multi-four-bar linkage transformable wheel-legged locomotion mechanism is proposed for LDR, which can transform from circle-wheeled mobility to wheel-legged mobility, or vice versa, for adaptability of different terrains. Aimed to minimize the robot actuators, the wheel-legged transformation is designed to be triggered passively when contacting the obstacles, or be actively driven without extra actuator by active triggering mechanism. The parameters of wheel-legged structure are optimized as well for an optimal transformation success rate and better obstacle-negotiation capability. A prototype of LDR robot is conducted, and its experiment results show that the proposed robot integrates the advantages of both wheeled and legged mobility, and has excellent performance in maneuverability, stability, maximum obstacle-negotiation height and mode switch process, such as transformation ratio as 1.88, transformation success rate as 100%, and ability to over obstacles 2.8 times as tall as its wheel radius. Lessons learned from the proposed robotic mobility and its results have general applicability to search / rescue robots, and other types of mobile robots as well. [ABSTRACT FROM AUTHOR]

This paper presents NAR-RM, a method for learning robot reaching motions from a set of demonstrations using Nonlinear AutoRegressive (NAR) polynomial models. Reaching motions are modeled as solutions to autonomous discrete-time nonlinear dynamical systems, so that the movements started near the data of the demonstrations follow the trained trajectories and always reach and stop at the target. Since NAR models obtained using standard system identification techniques do not always adequately model the reaching motions, in this paper we present a method that uses a least-squares estimator with constraints to impose the location of fixed points in the model. With the imposition of new fixed points it is possible to change the location of the original fixed points of the model, thus allowing the learning of stable reaching motions. We evaluate our method using a library of human handwriting motions, a mobile robot and an industrial manipulator. [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 order to move safely and accurately, mobile platforms using steerable wheels require adequate coordination of their actuators. One possibility to achieve actuator coordination is to control the motion of the chassis’ instantaneous centre of rotation (ICR) and motion around it. Considering the chassis as a rigid body, the ICR is located at the intersection of each wheel’s zero motion axis. In practice however, these axes may not concur, in particular when compliant actuators are used for wheel steering. They then no more define precisely an ICR and only an estimation of its position can be computed. Moreover, most parametrizations of the ICR position bring in singularities with no physical meaning, which hinder estimation. This paper introduces the H representation, a new parametrization of the motion state space free of any non-structural singularities, and presents an algorithm which estimates the ICR within the joint space. The proposed approach is compared in terms of reliability, efficiency, accuracy and robustness with three methods working within the operational space. Results suggest that the proposed estimation approach provides the best compromise for these performance indicators. [ABSTRACT FROM AUTHOR]

AZIMUT-3 is a nonholonomic omnidirectional platform design using sidewards off-centred compliant wheels. This design makes it possible to experiment with the use of the chassis’ instantaneous centre of rotation (ICR) for motion control. Research on ICR-based motion controllers has focused on handling structural singularities and misses a more general consideration of the chassis’ kinematic and physical constraints like steering, velocity and acceleration constraints. This paper presents the design of an ICR-based motion controller for AZIMUT-3. Leveraging a new parametrization of the motion state space and the associated representation in R 3 (collectively referred to as the H representation) and adapting a time scaling principle initially developed for manipulator trajectories, the designed motion controller is able to handle actuators coordination and their physical limits, as well as structural singularities. Results of tests done with the platform are presented, demonstrating the applicability of the proposed motion controller in efficiently handling these issues. [ABSTRACT FROM AUTHOR]

Smartphone-based human indoor localization was previously implemented using wireless sensor networks at the cost of sensing infrastructure deployment. Motivated by increasing research attention on location-aware human–robot interaction, we propose a robot-assisted human indoor localization scheme utilizing acoustic ranging between a self-localized mobile robot and smartphones. Data from the low-cost Kinect vision sensor are fused with smartphone-based acoustic ranging, and an extended Kalman filter based localization algorithm is developed for real-time dynamic position estimation and tracking. Real robot–smartphone experiments are performed, and performances are evaluated in various indoor environments under different environmental noises and with different human walking speed. Comparing to existing indoor smartphone localization methods, the proposed system does not rely on wireless sensing infrastructure, and has comparable localization accuracy with increased flexibility and scalability due to the mobility of the robot. [ABSTRACT FROM AUTHOR]

This paper provides a new framework for the collective motion control of a team of n -link doubly nonholonomic mobile manipulators in a constrained environment. A continuous decentralized motion planner is proposed. It guarantees the establishment and strict maintenance of a team formation using the Lyapunov-based control scheme (LbCS), which takes into account all the practical limitations, and the constraints due to fixed obstacles, nonholonomy and globally rigid formation requirements. The control scheme inherently utilizes artificial potential fields within an overarching leader–follower framework to mobilize the prescribed globally rigid formation. The designated leader of the team is modeled as a moving reference point, referred to as the virtual leader . With its protective polygonal region, it directs the motion of the team and manipulate the geometry of the formation. It is the authors’ belief that this navigation and globally rigid formation control problem of n -link doubly nonholonomic mobile manipulators is treated for the first time with the use of continuous time-invariant control laws within the framework of LbCS and a variant of the leader–follower scheme. The effectiveness of the motion planner and the resulting acceleration-based control laws are demonstrated via computer simulations. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning regression algorithms are trained considering proprioceptive sensing. This contribution is validated by using the MIT single-wheel testbed equipped with an MSL spare wheel. The combination of IMU-related and torque-related features outperforms the torque-related features only. Gaussian process regression results in a proper trade-off between accuracy and computation time. Another advantage of this algorithm is that it returns the variance associated with each prediction, which might be used for future route planning and control tasks. The paper also provides a comparison between machine learning regression and classification algorithms. [ABSTRACT FROM AUTHOR]

Quadruped robots can mimic animal locomotion mode and have great potential usage in unstructured environments. However, as mobile platforms, quadruped robots often lack manipulation capabilities. In this project, we equipped the quadruped robot SDU-ADog with a torque-controlled 6-DOF arm. A novel control framework which combines virtual model control (VMC) and prioritized whole-body control (WBC) for the whole system is proposed in this paper. VMC finds optimal target ground reaction forces while compensating the arm's inertia, and then makes the robot compliant to external disturbance. Prioritized WBC deals with multiple tasks in an optimal fashion and achieves efficiency and robustness of robot's locomotion and manipulation. The effectiveness of our framework has been evaluated through a set of robot dynamic simulations conducted in Webots. The robot can finish balance maintaining with moving arm, fixed point tracking while trotting, and locomotion over different obstacles with an end-point task. • We devise a control framework for legged mobile manipulators which considers the full kinematics and dynamics model of the robot. • We stabilize the robot's CoM position and orientation by combining VMC and WBC. • We validate the method's robustness by simulations of fixed-point tracking while trotting over obstacles. • The robustness is further validated by experiments on our robot. The results show that our control frame work is valid and easy to transfer from simulation to reality. [ABSTRACT FROM AUTHOR]

Learning from demonstration (LfD) is an intuitive strategy for transferring human motion skills to robots in an agile and adaptable manner. The major goal of LfD is to identify significant movement primitives (MPs) from human demonstrations and then recompose those intrinsic primitives to adapt to a variety of new situations. However, maintaining the simplicity of MPs representation while guaranteeing their adaptability is not an easy undertaking. To achieve these two goals, two approaches are possible: (1) learning models that can capture and utilize the inherent patterns and main characteristics of the human demonstrations, and (2) dynamical systems that can respond to perturbations online without requiring to re-plan the entire trajectory. In this paper, we present a novel and efficient model that combines these two benefits to formulate MPs using a fuzzy dynamical system (Fuzzy-DS), which enables robots to adaptively alter the learned motion skills to meet various additional constraints in the process of performing tasks. Due to the joint use of a fuzzy inference system and a dynamic system, Fuzzy-DS is well-suited to human inputs and intuitive fuzzy rules, resulting in a computationally efficient model. To verify the effectiveness of the proposed method, experiments have been designed where the robot learned a plant pruning task and a pick-and-place task, subsequently, it can replicate and generalize these tasks to novel situations. [ABSTRACT FROM AUTHOR]

This study investigates operability and acceptability issues in the teleoperation of robots. Prior studies have proposed efficient approaches to increase human perceptual ability and robot autonomy but with reduced operability and acceptance. We propose a novel teleoperation method that overcomes the weaknesses of existing approaches while inheriting their strengths. The key feature of our method is switching the teleoperated robot world from real to virtual. The user study results showed that the proposed method offered an improved user experience compared to the conventional methods, while task efficiency was equivalent in all methods. The contributions of this paper include the proposal of the teleoperation method by seamless switching between real and virtual space, the proposal of an image transformation method and visual effect to achieve seamless switching, and verification of the practicality of the proposed system through experiments on actual mobile robots. • Illusory control improved the operability and acceptability of the robot system. • An image transformation using neural networks for seamless transitions. • A visual effect that amplifies the sense of speed for seamless transitions. [ABSTRACT FROM AUTHOR]

Assistive robotic systems are quickly becoming a core technology for the service sector as they are understood capable of supporting people in need of assistance in a wide variety of tasks. This step poses a number of ethical and technological questions. The research community is wondering how service robotics can be a step forward in human care and aid, and how robotics applications can be realized in order to put the human role at the forefront. Therefore, there is a growing demand for frameworks supporting robotic application designers in a "human-aware" development process. This paper presents a model-driven framework for analyzing and developing human–robot interactive scenarios in non-industrial settings with significant sources of uncertainty. The framework's core is a formal model of the agents at play – the humans and the robot – and the robot's mission, which is then put through verification to estimate the probability of completing the mission. The model captures non-trivial features related to human behavior, specifically the unpredictability of human choices and physiological aspects tied to their state of health. To foster the framework's accessibility, we present a verification tool-agnostic Domain-Specific Language that allows designers lacking expertise in formal modeling to configure the interactive scenarios in a user-friendly manner. We compare the formal analysis outputs with results obtained by deploying benchmark scenarios in the physical environment with a real mobile robot to assess whether the formal model adheres to reality and whether the verification results are accurate. The entire development pipeline is then tested on several scenarios from the healthcare setting to assess its flexibility and effectiveness in the application design process. • Design of scenarios where robots and humans interact closely. • Model-driven framework to design, deploy, and reconfigure scenarios. • Extensive coverage of realistic healthcare use cases. • Reliable estimations of quality metrics of the scenario. [ABSTRACT FROM AUTHOR]

"Motion generation by imitating" enables a robot to generate its trajectory in a new environment. Research works on dynamic movement primitives (DMP) has reported promising results, with good imitation effect and convergence to the target. However, DMP still has issues such as learning from multiple demonstrations for different initial conditions and achieving obstacle avoidance considering the distribution and motion of obstacles. One of the effective solutions is combining DMP and model predictive control (MPC). The imitation process was transformed into a receding horizon planning procedure, letting the robot to learn more from nearer demonstrations. It is solved as an optimization problem with obstacles modeled as constraints. However, its drawback includes the heavy computation burden, which can be even aggravated in a multi-obstacle scenario where complicated constraints occur. Thus, in this paper, we propose an enhanced MPDMP+ method that combines the advantages of MPC with potential function for both multi-demonstration imitation and multi-obstacle avoidance effect. A proximal augmented Lagrangian method is proposed to solve the optimization problem. This proposed method has a faster convergence rate and small errors. We conducted the simulation and robot experiments for imitation learning for obstacle avoidance scenarios. Our results illustrate the superior performance of the proposed method. • We propose the MPDMP+ for imitation learning from multiple demonstrations • Our method incorporates the obstacle avoidance in DMP for real-time control. • We apply proximal augmented Lagrangian solver to improve the computation speed. • We verify the effectiveness of the proposed method by simulation and experiments. [ABSTRACT FROM AUTHOR]

In this research, a global path planning method based on recurrent neural networks by means of a new Loss function is presented, which regardless of the complexity of the configuration space, generates the path in a relatively constant time. The new Loss function is defined in such a way that in addition to learning the input data of the network, it creates an adjustable safety margin around the obstacles and ultimately creates a safe path. Moreover, a new global path planning method is also introduced, which is used to create the dataset required to train the proposed neural network. The convergence of this method is mathematically proven and it is shown that this method can also produce a suboptimal path in a much shorter time than the common methods of global path planning reported in the literature. In short, the main purpose of this research consists in providing a method which can create a suboptimal, fast and safe path for a mobile robot from any random starting point to any random destination in a known environment. First, the proposed methods will be implemented for different two-dimensional environments consisting of convex and non-convex obstacles, considering the robot as a point-mass, and then it will be implemented in a simulation environment, AI2THOR. Compared to classical global path planning algorithms, such as RRT and A*, the proposed approach demonstrates better performance in complex and challenging environments. • Proposing a novel fast global path planner for generating the LSTM's input path data. • A novel loss function is proposed that helps the LSTM network predict safe paths. • Comparing and evaluating proposed and existing methods in different environments. • AI2THOR simulation environment results validate the effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

The paper details the first successful attempt at using model checking techniques to verify the correctness of distributed algorithms for robots evolving in a continuous environment. The study focuses on the problem of rendezvous of two robots with lights. There exist many different rendezvous algorithms that aim at finding the minimal number of colors needed to solve rendezvous in various synchrony models (e.g. , FSYNC, SSYNC, ASYNC). While these rendezvous algorithms are typically very simple, their analysis and proof of correctness tend to be extremely complex, tedious, and error-prone as impossibility results are based on subtle interactions between the activation schedules of the robots. The paper presents a generic verification model that can be concretely expressed in available software model-checkers. In particular, we explain the subtle design decisions that allow to keep the search space finite and tractable, as well as prove several important theorems that support them. As a sanity check, we use the model to verify several known rendezvous algorithms in six different models of synchrony. In each case, we find that the results obtained from the model checker are consistent with the results known in the literature. The model checker outputs a counter-example execution in every case that is known to fail. In the course of developing and proving the validity of the model, we identified several fundamental theorems, including the ability for a well chosen algorithm and ASYNC scheduler to produce an emerging property of memory in a system of oblivious mobile robots, and why it is not a problem when robots executing the gathering algorithms are equipped with lights. [Display omitted] • We describe a model to verify mobile robot gathering algorithms that evolve in a Euclidean continuous space. • We show that positive results obtained for the model extend to the initial system model. • We model check all known mobile robot gathering algorithms and verify previous pen-and-paper results. • We obtain new insights about key system hypotheses about problem solvability. • We introduce weaker models of consistency (inspired by regular registers). • We derive a new algorithm that solves mobile robot gathering in such a model. [ABSTRACT FROM AUTHOR]

The agricultural domain has been experiencing extensive automation interest over the past decade. The established process for measuring physiological and morphological traits (phenotypes) of crops is labour-intensive and error-prone. In this paper, a mobile robotic platform, namely The Autonomous Robot for Orchard Surveying (AROS), was developed to automate the process of collecting spatial and visual data autonomously. Furthermore, six different control frameworks are presented to evaluate the feasibility of using a kinematic model in agricultural environments. The kinematic model does not consider wheel slippage or any forces associated with dynamic motion. Thus, the following six controllers are evaluated: Proportional-Derivative (PD) controller, Sliding Mode Controller (SMC), Control-Lyapunov Function (CLF), Nonlinear Model Predictive Controller (NMPC), Tube-Based Nonlinear Model Predictive Controller (TBNMPC), and Model Predictive Sliding Mode Control (MPSMC). This paper provides insight into the degree of disturbance rejection that the mentioned control architectures can achieve in outdoor environments. Experimental results validate that all control architectures are capable of rejecting the present disturbances associated with unmodelled dynamics and wheel slip on soft ground conditions. Additionally, the optimal-based controllers managed to perform better than the non-optimal controllers. Performance improvements of the TBNMPC of up to 209.72% are realized when compared to non-optimal methods. Results also show that the non-optimal controllers had low performance due to the underactuated constraint present in the kinematic model. • Development of a robotic mobile platform with controllers used in apple orchards. • The robot is used to autonomously track the path to collect visual and spatial data. • A Model Predictive Sliding Mode Control (MPSMC) is proposed using only kinematic model. • The Tube-Based Nonlinear Model Predictive controller (TBNMPC) outperforms all the subsequent controllers. • The controller with the lowest control effort is the MPSMC. [ABSTRACT FROM AUTHOR]

A robot introduced into an animal group, accepted by the animals as conspecifics, and capable of interacting with them is an efficient tool for ethological research, particularly in studies of collective and social behaviour. In this paper, we present the implementation of an autonomous mobile robot developed by the authors to study group behaviour of chicks of the domestic chicken ( Gallus gallus domesticus ). We discuss the design of the robot and of the experimental framework that we built to run animal–robot experiments. The robot design was experimentally validated, we demonstrated that the robot can be socially integrated into animal groups. The designed system extends the current state of the art in the field of animal–robot interaction in general and the birds study in particular by combining such advantages as (1) the robot being a part of the group, (2) the possibility of mixed multi-robot, multi-animal groups, and (3) close-loop control of robots. It opens new opportunities in the study of behaviour in domestic fowl by using mobile robots; being socially integrated into the animal group, robots can profit from the positive feedback mechanism that plays key roles in animal collective behaviour. They have potential applications in various domains, from pure scientific research to applied areas such as control and ensuring welfare of poultry. [ABSTRACT FROM AUTHOR]

Petri nets (PNs) are capable of modeling nearly any conceivable system and can provide a better understanding of the idealized action sequence in which to most effectively describe or execute said system through their powerful analytical capabilities. However, because real world instances are rarely as consistent and ideal as simulated models, basic PN modeling and simulation properties may be insufficient in practical application. We remedy this through specialization in Fuzzy Petri nets (FPNs). Fuzzy logic is incorporated to better model a self-navigating robot algorithm, thanks to its versatile multi-valued logic reasoning. By using FPNs, it is possible to simulate, assess, and communicate the process and reasoning of the navigational algorithm and apply it to real world programming. In this paper, we propose a series of specific fuzzy algorithms intended to be implemented in concert on a mobile robot platform in order to optimize the sequence of actions needed for a given task, primarily the navigation of an unknown maze. A set of varied maze configurations were developed and simulated as PN and FPN models, providing a testing environment to examine the efficiency of several methodologies. Five methods, including an original proposal in this paper, were compared across 30,000 simulations, evaluating in particular performance in processing cost in time. Our experiments concluded with results suggesting a very competitive task completion time at a considerable fraction in processing cost compared to the closest performing alternatives. [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]

This study presents an autonomous guided vehicle (AGV) with simultaneous localization and map building (SLAM) based on matching method, and extended Kalman filter SLAM. In general, the AGV is a large mobile robot that is used in transportation to carry cargoes, and it is guided using wired or wireless guidance systems. The guidance system based AGV accounts for a majority of robots in the mobile robot industry. However, in semiconductor factories, landmarks are unavailable; hence, the existing system has not been used in the mentioned environments. Therefore, the SLAM technology is applied in the environments, and can guide the AGV without landmarks. However, the accuracy of the SLAM can be low owing to measurement error of sensors and a cumulative calculation caused by localization sensors. Therefore, the accuracy is frequently assumed to be incorrect; moreover, the accuracy of the built map is low. In order to solve the problems, this study proposes the AGV with the SLAM based on matching methods; two matching method; geometric matching method and iterative closest point algorithm. The performance of the proposed method is compared with typical methods such as singular value decomposition / RIGID transformation based technologies using feature-point-based SLAM and is compared with the aforementioned two methods using the extended Kalman filter SLAM. The proposed method is more efficient than the typical methods used in the comparison. [ABSTRACT FROM AUTHOR]

Cyber-security for robotic systems is a growing concern. Many mobile robots rely heavily on Real Time Location Systems to operate safely in different environments. As a result, Real Time Location Systems have become a vector of attack for robots and autonomous systems, a situation which has not been studied well. This article shows that cyber-attacks on Real Time Location Systems can be detected by a system built using supervised learning. Furthermore it shows that some type of cyber-attacks on Real Time Location Systems, specifically Denial of Service and Spoofing, can be detected by a system built using Machine Learning techniques. In order to construct models capable of detecting those attacks, different supervised learning algorithms have been tested and validated using a dataset of real data recorded by a wheeled robot and a commercial Real Time Location System, based on Ultra Wideband beacons. Experimental results with a cross-validation analysis have shown that Multi-Layer Perceptron classifiers get the highest test score and the lowest validation error. Moreover, it is the model with less overfitting and more sensitivity for detecting Denial of Service and Spoofing cyber-attacks on Real Time Location Systems. [ABSTRACT FROM AUTHOR]

This paper proposes a new type of transformable wheel-legged mobile robot that could be applied on both flat and rugged terrains. It integrates stability and maneuverability of wheeled robot and obstacle climbing capability of legged robot by means of a wheel-legged transformable mechanism. These two modes can be switched easily with two spokes touching terrain. In this paper, the motion analysis of the proposed robot under wheeled mode, legged mode and transformable mode are carried out after briefly introducing the concept and control system design. Then, the obstacle climbing strategies under wheeled and legged modes are obtained. Finally, a prototype of the proposed robot is designed and manufactured based upon the simulation analysis. And the experiment results validate the effectiveness of the proposed transformable wheel-legged mobile robot. [ABSTRACT FROM AUTHOR]

To achieve a complete sense of a typical mobile robot moving on the rough and unknown surfaces, a new sensitive shield attached to the robotic wheel is developed with the principle of the total resistance of a circuit. Contact point detection using the new sensitive shield empowers the wheeled-robots to scan the surface, to find the spatial stability measure and to control itself in the absence of camera and laser. The resistive circuit, whereby the sensor is developed, includes a conductive wire, a resistive wire, an impulse voltage source and an additional resistor. All these elements are assembled as an exterior sensitive shield attached to the outside of the wheel. The main advantage of the resistive shield is to determine the exact locations of the spatial contact point on the thick wheel only by means of an input data. Using the data detected from the sensitive shield, the control process can produce the accurate rotational velocities of the wheels required for pursuing a desired path. Furthermore, the results of the new sensor enhance the accuracy of the spatial stability measure. The results of this research imply how the accurate contact point detection aided by the new sensor yields a reliable approach for surface scanning and stability measure calculation. [ABSTRACT FROM AUTHOR]

Mobile robot localization consists in estimation of robot pose by using real-time measurements. The Internet of Things (IoT) adds a new dimension to this process by enabling communications with smart objects at anytime and anywhere. Thus data used by localization process can come both from the robot on-board sensors and from environment objects, mobile or not, able to sense the robot. The paper considers localization problem as a nonlinear bounded-error estimation of the state vector whose components are the robot coordinates. The approach based on interval analysis is able to answer the constraints of IoT by easily taking account a heterogeneous set and a variable number of measurements. Bounded-error state estimation can be an alternative to other approaches, notably particle filtering which is sensible to non-consistent measures, large measure errors, and drift of robot evolution model. The theoretical formulation of the set-membership approach and the application to the estimation of the robot localization are addressed first. In order to meet more realistic conditions the way of reducing the effect of environment model inaccuracies, evolution model drift, outliers and disruptive events such as robot kidnapping is introduced. By integrating these additional treatments to the set-membership approach we propose a bounded-error estimator using multihypothesis tracking. Simulation results show the contribution of each step of the estimator. Real experiments focus on global localization and specific treatments for synchronizing measurements and processing outliers. [ABSTRACT FROM AUTHOR]

In multi agent mobile robotic systems, coordinating the gaze of each autonomous robot without the use of predetermined gaze attractors requires the sharing of either a gaze vector together with knowledge of the location of each robotic or sharing of a visual description of what the gaze should be directed at. In the latter case, this should consist of the least amount of data possible to reduce transmission delay and processing requirements. Sharing a single unique visual feature is sufficient to direct gaze. Unfortunately, typical feature detection methods are tuned to detect many hundreds of features to maximize successful matches in corresponding images. This paper presents an alternate feature detection method that is tuned to select only a few, high quality features. Not only does this method achieve the accuracy of state of the art methods over the target range, it does so quicker and results in a significantly smaller amount of information that needs to be shared between agents for directing gaze to the same point of interest. [ABSTRACT FROM AUTHOR]

Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time-constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set. [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]

Rather than merely dealing with robot localization and mapping in 3D environments, this paper tackles a special topic which could be denominated as-is 3D building modeling with robots. We present a method with which to carry out the automatic 3D scanning of furnished buildings with the aim of obtaining complete and in-depth information regarding the principal visible structural components of an indoor environment (walls, floors and ceilings). This is an essential stage in the automatic creation of as-built BIM models that greatly facilitates the detection of the structure of a building. Our system, which is essentially composed of a mobile robot and a 3D laser scanner, autonomously navigates indoor environments that are occluded and cluttered. The approach can be considered as a structural-component based 3D mapping in which the robot moves to certain positions that are calculated using a new Next Best Scan (NBS) algorithm. We justify the contributions of our work in this research field by means of a broad theoretical comparison with other related methods. Key issues such as the complexity of the scene, occlusion, hypotheses, limitations and autonomy are discussed in the paper. This system has been tested in real and simulated environments. Complex scenes composed of several adjacent non-regular rooms (i.e., concave/convex rooms) have been tested, with promising results. [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 work investigates the problem of robust vision-based human tracking by a human following robot using point-based features like SURF. The problem is challenging owing to failures arising because of variation in illumination, change in pose, size or scale, camera motion and partial or full occlusion. While point-based features provide robust detection against photometric and geometric distortions, the tracking of these features over subsequent frames becomes difficult as the number of matching points between a pair of images drops quickly with slight variation in target attribute owing to above mentioned variations. The problem of robust human tracking by the robot is solved by proposing a multi-tracker fusion framework that allows one to combine multiple tracker to ensure long term tracking of the target. This fusion framework also allows for creating a dynamic template pool of target features which gets updated over time. The interaction between the first two trackers is used to update the template pool of the target attribute while the last tracker is used to estimate the location of the target in case of full occlusion. The working of the framework is demonstrated by combining a SURF-based mean-shift tracker, an optical-flow tracker and a Kalman filter to provide robust tracking over a long time. The efficacy of the resulting tracker is demonstrated through rigorous testing on a variety of video datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for guaranteed integration of state equations. Within this framework, the variables of interest are trajectories submitted to both arithmetic and differential equations. The approach consists in formalizing a problem thanks to a constraint network and then apply these constraints to sets of trajectories. The contribution of the paper is to provide a reliable framework to enclose the solutions of these differential equations. Its use is shown to be simple, more general and more competitive than existing approaches dealing with guaranteed integration, especially when applied to mobile robotics. The flexibility of the developed framework allows to deal with non-linear differential equations or even differential inclusions built from datasets, while considering observations of the states of interest. An illustration of this method is given over several examples with mobile robots. [ABSTRACT FROM AUTHOR]

In this paper, we propose a system that stems from the integration of an autonomous mobile robot with an IoT-based monitoring system to provide monitoring, assistance, and stimulation to older adults living alone in their own houses. The creation of an Internet of Robotics Things (IoRT) based on the interplay between pervasive smart objects and autonomous robotic systems is claimed to enable the creation of innovative services conceived for assisting the final user, especially in elderly care. The synergy between IoT and a Socially Assistive Robot (SAR) was conceived to offer robustness, reconfiguration, heterogeneity, and scalability, by bringing a strong added value to both the current SAR and IoT technologies. First, we propose a method to achieve the synergy and integration between the IoT system and the robot; then, we show how our method increases the performance and effectiveness of both to provide long-term support to the older adults. To do so, we present a case-study, where we focus on the detection of signs of the frailty syndrome , a set of vulnerabilities typically conveyed by a cognitive and physical decline in older people that concur in amplifying the risks of major diseases hindering the capabilities of independent living. Experimental evaluation is performed in both controlled settings and in a long-term real-world pilot study with 9 older adults in their own apartments, where the system was deployed autonomously for, on average, 12 weeks. • We analyze the limitations of IoT and SARs within a home-based AAL setting. • The interplay between the IoT, the robot, and the user can overcome such limitations. • Each component both assists and is assisted by the other ones. • This creates a mutual-aid actor-network; we present it in a case-study. • We demonstrate in an extensive on-the-field trial with end-users our results. [ABSTRACT FROM AUTHOR]

Many complex vehicular systems, such as large marine vessels, contain confined spaces like water tanks, which are critical for the safe functioning of the vehicles. It is particularly hazardous for humans to inspect such spaces due to limited accessibility, poor visibility, and unstructured configuration. While robots provide a viable alternative, they encounter the same set of challenges in realizing robust autonomy. In this work, we specifically address the problem of detecting foreign object debris (FODs) left inside the confined spaces using a visual mapping-based system that relies on Mahalanobis distance-driven comparisons between the nominal and online maps for local outlier identification. The identified outliers, corresponding to candidate FODs, are used to generate waypoints that are fed to a mobile ground robot to take camera photos. The photos are subsequently labeled by humans for final identification of the presence and types of FODs, leading to high detection accuracy while mitigating the effect of recall–precision tradeoff. Preliminary simulation studies, followed by extensive physical trials on a prototype tank, demonstrate the capability and potential of our FOD detection system. • Foreign object debris (FODs) detection in confined spaces is a challenging problem. • Low-cost autonomous robots with remote human assistance offer a viable solution. • We present a purely vision-based mapping system for robotic FOD detection • We devise a new Mahalanobis distance-based local outlier detection method. • We demonstrate effective performance on a ground robot inside a water tank. [ABSTRACT FROM AUTHOR]

This paper presents a control and motion planning algorithm for a mobile vehicle-manipulator system such that the mobile vehicle and the manipulator mounted on it work in harmony to inspect unknown objects. Forward Dynamic Control method is used for the manipulator to accomplish a stable interaction with the environment and constrained particle swarm optimization is applied so that the vehicle can be localized at the estimated points maximizing the dexterity of the manipulator. Quartic splines are implemented to generate a smooth path for the vehicle in between the optimal locations. The proposed architecture is validated via an experimental setup consisting of a robotic arm with a force sensor at its end-effector mounted on a parallel manipulator. These experiments emulate an underwater vehicle-manipulator system, where the mobile base is subject to disturbances due to the physical interaction of the end-effector with the environment, typically a pipe. The advantage of the proposed approach is that it allows continuous and smooth movement of the base in harmony with the robotic manipulator while executing a task on a large surface (larger than the manipulator workspace can cover from a fixed position) and maintains a high level of dexterity index for the manipulator. [ABSTRACT FROM AUTHOR]

Accurate localization is crucial in the navigation of mobile robots. However, in other circumstances, single-sensor localization faces different challenges, including software and hardware problems or data outages. Sensor fusion is used in most autonomous vehicles (including aerial and ground vehicles) to overcome such challenges. In this paper, the localization of a mobile robot is studied in the presence of sensor faults. The mobile robot has two sensors: two Inertial Measurement Units (IMU) and wheel encoders. Regarding the fault-tolerant scheme, measurements of both sets of sensors are fused using an Interacting Multiple Model (IMM) Kalman filter based on both unscented and extended Kalman filters (UKF and EKF). UKF and EKF-based IMM are chosen for this study since the dynamic model of the localization is highly nonlinear. Regarding contributions, it should be noted that this scheme eliminates the need to model every single fault scenario and use an additional sensor to oversee the performance of the sensing system. Also, comparing this method with similar approaches adopted by other studies shows better performance regarding the cost of computations and RMSE. To evaluate performance, the outputs of the proposed filters are simulated and compared for different trajectories where the data of each sensor is intentionally corrupted to observe the fault detection capability. Simulations are performed for different trajectories and noises to demonstrate this method's efficiency in different situations. In addition, the results of unscented and extended Kalman filter-based IMM are compared in terms of error and computational costs to evaluate their performance. Overall, simulation and experiments indicate accurate 3D estimations in all cases. Moreover, designated weights vividly show that sensor fault detection is achieved by both unscented and extended IMM Kalman filters, which enable complete fault isolation consequently. This approach provides mobile robots with a reliable and straightforward sensor fault detection and localization solution. • Fault-tolerant sensor fusion scheme for mobile robots, when GPS is unavailable. • Simple, straightforward localization scheme in the presence of faults. • Reliable outlier detection and isolation scheme. • EKF and UKF-based approaches are implemented, and their response is compared. • Simulations and experiments validate the accuracy and reliability of this approach. [ABSTRACT FROM AUTHOR]

A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the proposed scheme, which is assessed using a genetic algorithm (GA) as a baseline method. The results show that the game theoretical approach outperforms GA both in performance and computation time for a range of problem instances. • Cooperative game theory tools are considered to deal with MRTA problems. • Robots and tasks are defined and ranked in a game according to their Shapley value. • An algorithm is proposed to group the players into balanced clusters. • Randomized methods are applied to large problems to relieve the computational load. • The feasibility is assessed in a large scenario and contrasted with a genetic approach. [ABSTRACT FROM AUTHOR]

A Range Only Simultaneous Localization and Mapping (RO-SLAM) problem is considered in this paper. The robot is a unicycle like vehicle equipped with encoders on the actuated wheels, which measures the distance to a set of UWB landmarks located in unknown position in the surrounding. A Multi Hypotheses Extended Kalman Filter (MHEKF), one for each landmark, is designed to dynamically estimate the range and the bearing of the observed landmark. These estimates, regarded as measurements with a proper covariance matrix, are used in an EKF SLAM algorithm, endowed with a resilient module to discern and possibly to temporarily discard landmarks with an unreliable range and bearing estimate. This allows to cope with the initial uncertainty characterizing the bearing reconstruction, but also to resist the effects of outliers and to detect possible abnormal situations. Simulation and experimental results illustrate the effectiveness of the proposed approach compared to other methods available in the literature, especially in case of significant perturbations, like the sudden and unmodeled shift of the landmarks. • Range-Only SLAM problem for a unicycle robot using Ultra Wide Band (UWB) antennas. • A Multi Hypotheses EKF (MHEKF) for each landmark to estimate its range and bearing. • Robust EKF-SLAM solution using range and bearing provided by decoupled MHEKFs. • Resilience against strong perturbations (landmark shift and/or abnormal noise). [ABSTRACT FROM AUTHOR]

The development of lower limb exoskeletons has seen significant interest in recent times. Two types of them are more used that cover two types of needs: gait rehabilitation and human locomotion assistance. An essential subject in controlling the latter kind is trajectory generation, in which there are still challenges. For online controlling of the exoskeleton, gait parameters must have the ability to change at any moment during walking, taking into account human intention and particular conditions. In this paper, an online gait generation method is provided that is suitable for different walking modes. For this purpose, three trajectory generator blocks are proposed. The first block is for the center of mass (CoM) in the double support phase, where the trajectory is generated to make the patient feel more comfortable. The second block is for the support leg in the single support phase. The trajectory is generated using the center of pressure (CoP) criterion to secure backward balance and reduce the forces applied to the arms. The last block is for the swing leg in the single support phase, where a cost function is proposed to minimize the torques of the motors. The performance analysis of the proposed trajectory generator blocks was evaluated, and walking patterns were examined via simulations. Finally, three experimental tests were implemented with a healthy subject wearing Exoped® exoskeleton on level-ground with an obstacle, and stairs. • An online gait cycle generator for different walking modes is provided which combines three trajectory generators as follows. • The trajectory in the double support phase is generated according to patient comfort. • The trajectory of the support leg is generated based on the CoP criterion. • The trajectory of the swing leg is generated to minimize the torques of the joint motors. • The performance of the method is tested by simulations and three experimental tests. [ABSTRACT FROM AUTHOR]