Showing papers on "Mobile robot published in 2017"

Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation

Abstract: We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.

Safety Barrier Certificates for Collisions-Free Multirobot Systems

Abstract: This paper presents safety barrier certificates that ensure scalable and provably collision-free behaviors in multirobot systems by modifying the nominal controllers to formally satisfy safety constraints. This is achieved by minimizing the difference between the actual and the nominal controllers subject to safety constraints. The resulting computation of the safety controllers is done through a quadratic programming problem that can be solved in real-time and in this paper, we describe a series of problems of increasing complexity. Starting with a centralized formulation, where the safety controller is computed across all agents simultaneously, we show how one can achieve a natural decentralization whereby individual robots only have to remain safe relative to nearby robots. Conservativeness and existence of solutions as well as deadlock-avoidance are then addressed using a mixture of relaxed control barrier functions, hybrid braking controllers, and consistent perturbations. The resulting control strategy is verified experimentally on a collection of wheeled mobile robots whose nominal controllers are explicitly designed to make the robots collide.

Robot Collisions: A Survey on Detection, Isolation, and Identification

Abstract: Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human–robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.

Uncertainty-Aware Reinforcement Learning for Collision Avoidance

Abstract: Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot. In this paper, we consider the specific case of a mobile robot learning to navigate an a priori unknown environment while avoiding collisions. In order to learn collision avoidance, the robot must experience collisions at training time. However, high-speed collisions, even at training time, could damage the robot. A successful learning method must therefore proceed cautiously, experiencing only low-speed collisions until it gains confidence. To this end, we present an uncertainty-aware model-based learning algorithm that estimates the probability of collision together with a statistical estimate of uncertainty. By formulating an uncertainty-dependent cost function, we show that the algorithm naturally chooses to proceed cautiously in unfamiliar environments, and increases the velocity of the robot in settings where it has high confidence. Our predictive model is based on bootstrapped neural networks using dropout, allowing it to process raw sensory inputs from high-bandwidth sensors such as cameras. Our experimental evaluation demonstrates that our method effectively minimizes dangerous collisions at training time in an obstacle avoidance task for a simulated and real-world quadrotor, and a real-world RC car. Videos of the experiments can be found at this https URL.

A survey of human-centered intelligent robots: issues and challenges

Abstract: Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.

ANYmal - toward legged robots for harsh environments

Abstract: This paper provides a system overview about ANYmal, a quadrupedal robot developed for operation in harsh environments. The 30 kg, 0.5 m tall robotic dog was built in a modular way for simple mainte...

Fundamentals of soft robot locomotion.

Abstract: Soft robotics and its related technologies enable robot abilities in several robotics domains including, but not exclusively related to, manipulation, manufacturing, human–robot interaction and locomotion. Although field applications have emerged for soft manipulation and human–robot interaction, mobile soft robots appear to remain in the research stage, involving the somehow conflictual goals of having a deformable body and exerting forces on the environment to achieve locomotion. This paper aims to provide a reference guide for researchers approaching mobile soft robotics, to describe the underlying principles of soft robot locomotion with its pros and cons, and to envisage applications and further developments for mobile soft robotics.

Design of the Hydraulically Actuated, Torque-Controlled Quadruped Robot HyQ2Max

Abstract: This paper presents the design of the hydraulically actuated quadruped robot HyQ2Max . HyQ2Max is an evolution of the 80 kg agile and versatile robot HyQ. Compared to HyQ, the new robot needs to be more rugged, more powerful and extend the existing locomotion skills with self-righting capability. Since the robot's actuation system has an impact on many aspects of the overall design/specifications of the robot (e.g., payload, speed, torque, overall mass, and compactness), this paper will pay special attention to the selection and sizing of the joint actuators. To obtain meaningful joint requirements for the new machine, we simulated seven characteristic motions that cover a wide range of required behaviors of an agile rough terrain robot, including trotting on rough terrain, stair climbing, push recovery, self-righting, etc. We will describe how to use the obtained joint requirements for the selection of the hydraulic actuator types, four-bar linkage parameters, and valve size. Poorly sized actuators may lead to limited robot capabilities or higher cost, weight, energy consumption, and cooling requirements. The main contributions of this paper are: 1) a novel design of an agile quadruped robot capable of performing trotting/crawling over flat/uneven terrain, balancing, and self-righting; 2) a detailed method to find suitable hydraulic cylinder/valve properties and linkage parameters with a specific focus on optimizing the actuator areas; and 3) to the best knowledge of the authors, the most complete review of hydraulic quadruped robots.

Real-Time Obstacle Avoidance Using Central Flow Divergence and Peripheral Flow

Abstract: The lure of using motion vision as a fundamental element in the perception of space drives this effort to use flow features as the sole cues for robot mobility. Real-time estimates of image flow and flow divergence provide the robot's sense of space. The robot steers down a conceptual corridor comparing left and right peripheral flows. Large central flow divergence warns the robot of impending collisions at "dead ends." When this occurs, the robot turns around and resumes wandering. Behavior is generated by directly using flow-based information in the 2D image sequence; no 3D reconstruction is attempted. Active mechanical gate stabilization simplifies the visual interpretation problems by reducing camera rotation. By combining corridor following and dead-end deflection, the robot has wandered around the lab at 30 cm/s for as long as 20 minutes without collision. The ability to support this behavior in real-time with current equipment promises expanded capabilities as computational power increases in the future. >

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Abstract: Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor actuator control techniques The applications of the autonomous mobile robot in many fields such as industry space defence and transportation and other social sectors are growing day by day The mobile robot performs many tasks such as rescue operation patrolling disaster relief planetary exploration and material handling etc Therefore an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance The present article focuses on the study of the intelligent navigation techniques which are capable of navigating a mobile robot autonomously in static as well as dynamic environments

A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization

Abstract: Display Omitted A novel hierarchical global path planning approach for mobile robots in a cluttered environment.A proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle.Providing optimal global robot paths with computational efficiency. In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach.

Soft Mobile Robots with On-Board Chemical Pressure Generation

Abstract: We wish to develop robot systems that are increasingly more elastic, as a step towards bridging the gap between man-made machines and their biological counterparts. To this end, we develop soft actuators fabricated from elastomer films with embedded fluidic channels. These actuators offer safety and adaptability and may potentially be utilized in robotics, wearable tactile interfaces, and active orthoses or prostheses. The expansion of fluidic channels under pressure creates a bending moment on the actuators and their displacement response follows theoretical predictions. Fluidic actuators require a pressure source, which limits their mobility and mainstream usage. This paper considers instances of mechanisms made from distributed elastomer actuators to generate motion using a chemical means of pressure generation. A mechanical feedback loop controls the chemical decomposition of hydrogen peroxide into oxygen gas in a closed container to self-regulate the actuation pressure. This on-demand pressure generator, called the pneumatic battery, bypasses the need for electrical energy by the direct conversion of chemical to mechanical energy. The portable pump can be operated in any orientation and is used to supply pressure to an elastomeric rolling mobile robot as a representative for a family of soft robots.

Resilient Flocking for Mobile Robot Teams

Abstract: We present a method that enables resilient formation control for mobile robot teams in the presence of noncooperative (defective or malicious) robots. Recent results in network science define graph topological properties that guarantee resilience against faults and attacks on individual nodes in static networks. We build on these results to propose a control policy that allows a team of mobile robots to achieve resilient consensus on the direction of motion. Our strategy relies on dynamic connectivity management that makes use of a metric that characterizes the robustness of the communication network topology. Our method distinguishes itself from prior work in that our connectivity management strategy ensures that the network lies above a critical resilience threshold , guaranteeing that the consensus algorithm always converges to a value within the range of the cooperative agents’ initial values. We demonstrate the use of our framework for resilient flocking, and show simulation results with groups of holonomic mobile robots.

Distributed Finite-Time Cooperative Control of Multiple High-Order Nonholonomic Mobile Robots

Abstract: The consensus problem of multiple nonholonomic mobile robots in the form of high-order chained structure is considered in this paper. Based on the model features and the finite-time control technique, a finite-time cooperative controller is explicitly constructed which guarantees that the states consensus is achieved in a finite time. As an application of the proposed results, finite-time formation control of multiple wheeled mobile robots is studied and a finite-time formation control algorithm is proposed. To show effectiveness of the proposed approach, a simulation example is given.

FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments

Abstract: We present a new approach to long-term mobile robot mapping in dynamic indoor environments. Unlike traditional world models that are tailored to represent static scenes, our approach explicitly models environmental dynamics. We assume that some of the hidden processes that influence the dynamic environment states are periodic and model the uncertainty of the estimated state variables by their frequency spectra. The spectral model can represent arbitrary timescales of environment dynamics with low memory requirements. Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot's long-term performance in changing environments. Experiments performed over time periods of months to years demonstrate that the approach can efficiently represent large numbers of observations and reliably predict future environment states. The experiments indicate that the model's predictive capabilities improve mobile robot localization and navigation in changing environments.

Correcting robot mistakes in real time using EEG signals

Abstract: Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink Robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction.

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter.

Abstract: Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

Disturbance Attenuation Tracking Control for Wheeled Mobile Robots With Skidding and Slipping

Abstract: In this paper, a robust tracking control scheme is proposed for wheeled mobile robots with skidding, slipping, and input disturbance. Considering the existing skidding and slipping, a desired disturbance-observer-based virtual velocity control law is first designed. Then, the robust tracking control scheme is developed by considering the prescribed tracking performance requirement and using the disturbance observer. In the tracking control scheme design, the prescribed performance function method is employed to guarantee the desired tracking performance. To handle the skidding, slipping, and input disturbance, the disturbance observer is developed in the control scheme design. Experiment results demonstrate the effectiveness of the proposed tracking control scheme for wheeled mobile robots with skidding, slipping, and input disturbance.

3D printed soft actuators for a legged robot capable of navigating unstructured terrain

Abstract: Soft robots have recently demonstrated impressive abilities to adapt to objects and their environment with limited sensing and actuation. However, mobile soft robots are typically fabricated using laborious molding processes that result in limited actuated degrees of freedom and hence limited locomotion capabilities. In this paper, we present a 3D printed robot with bellowed soft legs capable of rotation about two axes. This allows our robot to navigate rough terrain that previously posed a significant challenge to soft robots. We present models and FEM simulations for the soft leg modules and predict the robot locomotion capabilities. We use finite element analysis to simulate the actuation characteristics of these modules. We then compared the analytical and computational results to experimental results with a tethered prototype. The experimental soft robot is capable of lifting its legs 5.3 cm off the ground and is able to walk at speeds up to 20 mm/s (0.13 bl/s). This work represents a practical approach to the design and fabrication of functional mobile soft robots.

Effect of Robot Performance on Human–Robot Trust in Time-Critical Situations

Abstract: Robots have the potential to save lives in high-risk situations, such as emergency evacuations. To realize this potential, we must understand how factors such as the robot's performance, the riskiness of the situation, and the evacuee's motivation influence his or her decision to follow a robot. In this paper, we developed a set of experiments that tasked individuals with navigating a virtual maze using different methods to simulate an evacuation. Participants chose whether or not to use the robot for guidance in each of two separate navigation rounds. The robot performed poorly in two of the three conditions. The participant's decision to use the robot and self-reported trust in the robot served as dependent measures. A 53% drop in self-reported trust was found when the robot performs poorly. Self-reports of trust were strongly correlated with the decision to use the robot for guidance ( $\phi ({90}) = + 0.745$ ). We conclude that a mistake made by a robot will cause a person to have a significantly lower level of trust in it in later interactions.

Toward Socially Aware Robot Navigation in Dynamic and Crowded Environments: A Proactive Social Motion Model

Abstract: Safe and social navigation is the key to deploying a mobile service robot in a human-centered environment. Widespread acceptability of mobile service robots in daily life is hindered by robot’s inability to navigate in crowded and dynamic human environments in a socially acceptable way that would guarantee human safety and comfort. In this paper, we propose an effective proactive social motion model (PSMM) that enables a mobile service robot to navigate safely and socially in crowded and dynamic environments. The proposed method considers not only human states (position, orientation, motion, field of view, and hand poses) relative to the robot but also social interactive information about human–object and human group interactions. This allows development of the PSMM that consists of elements of an extended social force model and a hybrid reciprocal velocity obstacle technique. The PSMM is then combined with a path planning technique to generate a motion planning system that drives a mobile robot in a socially acceptable manner and produces respectful and polite behaviors akin to human movements. Note to Practitioners —In this paper, we validated the effectiveness and feasibility of the proposed proactive social motion model (PSMM) through both simulation and real-world experiments under the newly proposed human comfortable safety indices. To do that, we first implemented the entire navigation system using the open-source robot operating system. We then installed it in a simulated robot model and conducted experiments in a simulated shopping mall-like environment to verify its effectiveness. We also installed the proposed algorithm on our mobile robot platform and conducted experiments in our office-like laboratory environment. Our results show that the developed socially aware navigation framework allows a mobile robot to navigate safely, socially, and proactively while guaranteeing human safety and comfort in crowded and dynamic environments. In this paper, we examined the proposed PSMM with a set of predefined parameters selected based on our empirical experiences about the robot mechanism and selected social environment. However, in fact a mobile robot might need to adapt to various contextual and cultural situations in different social environments. Thus, it should be equipped with an online adaptive interactive learning mechanism allowing the robot to learn to auto-adjust their parameters according to such embedded environments. Using machine learning techniques, e.g., inverse reinforcement learning [1] to optimize the parameter set for the PSMM could be a promising research direction to improve adaptability of mobile service robots in different social environments. In the future, we will evaluate the proposed framework based on a wider variety of scenarios, particularly those with different social interaction situations and dynamic environments. Furthermore, various kinds of social cues and signals introduced in [2] and [3] will be applied to extend the proposed framework in more complicated social situations and contexts. Last but not least, we will investigate different machine learning techniques and incorporate them in the PSMM in order to allow the robot to automatically adapt to diverse social environments.

Adaptive Vision-Based Leader–Follower Formation Control of Mobile Robots

Abstract: This paper focuses on the problem of vision-based leader–follower formation control of mobile robots. The proposed adaptive controller only requires the image information from an uncalibrated perspective camera mounted at any position and orientation (attitude) on the follower robot. Furthermore, the approach does not depend on the relative position measurement and communication between the leader and follower. First, a new real-time observer is developed to estimate the unknown intrinsic and extrinsic camera parameters as well as the unknown coefficients of the plane where the feature point moves relative to the camera frame. Second, the Lyapunov method is employed to prove the stability of the closed-loop system, where it is shown that convergence of the image error is guaranteed. Finally, the performance of the approach is demonstrated through physical experiments and experimental results.

Robot Operating System (ROS) The Complete Reference (Volume 2)

Abstract: Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using ROS -- Design of Fuzzy Logic Controllers to ROS-based UAVs -- Flying Multiple UAVs Using ROS -- SkiROS -- A skill-based robot control architecture on top of ROS -- Control of Mobile Robots using ActionLib -- Parametric Identification of the Dynamics of Mobile Robots and Its Application for the Tuning of Controllers in ROS -- ROSLink: Bridging ROS with the Internet-of-Things for Cloud Robotics -- A ROS Package for Dynamic Bandwidth Management in Multi-Robot Systems -- An autonomous companion UAV for the SpaceBot Cup competition 2015. .

DARP: Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning

Abstract: This paper deals with the path planning problem of a team of mobile robots, in order to cover an area of interest, with prior-defined obstacles. For the single robot case, also known as single robot coverage path planning (CPP), an źź(n) optimal methodology has already been proposed and evaluated in the literature, where n is the grid size. The majority of existing algorithms for the multi robot case (mCPP), utilize the aforementioned algorithm. Due to the complexity, however, of the mCPP, the best the existing mCPP algorithms can perform is at most 16 times the optimal solution, in terms of time needed for the robot team to accomplish the coverage task, while the time required for calculating the solution is polynomial. In the present paper, we propose a new algorithm which converges to the optimal solution, at least in cases where one exists. The proposed technique transforms the original integer programming problem (mCPP) into several single-robot problems (CPP), the solutions of which constitute the optimal mCPP solution, alleviating the original mCPP explosive combinatorial complexity. Although it is not possible to analytically derive bounds regarding the complexity of the proposed algorithm, extensive numerical analysis indicates that the complexity is bounded by polynomial curves for practical sized inputs. In the heart of the proposed approach lies the DARP algorithm, which divides the terrain into a number of equal areas each corresponding to a specific robot, so as to guarantee complete coverage, non-backtracking solution, minimum coverage path, while at the same time does not need any preparatory stage (video demonstration and standalone application are available on-line http://tinyurl.com/DARP-app).