Showing papers on "Mobile robot published in 2016"

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Abstract: We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle.

Socially compliant mobile robot navigation via inverse reinforcement learning

Abstract: Mobile robots are increasingly populating our human environments. To interact with humans in a socially compliant way, these robots need to understand and comply with mutually accepted rules. In this paper, we present a novel approach to model the cooperative navigation behavior of humans. We model their behavior in terms of a mixture distribution that captures both the discrete navigation decisions, such as going left or going right, as well as the natural variance of human trajectories. Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features. To compute the feature expectations over the resulting high-dimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling. Furthermore, we rely on a Voronoi graph of the environment to efficiently explore the space of trajectories from the robot's current position to its target position. Using the proposed model, our method is able to imitate the behavior of pedestrians or, alternatively, to replicate a specific behavior that was taught by tele-operation in the target environment of the robot. We implemented our approach on a real mobile robot and demonstrated that it is able to successfully navigate in an office environment in the presence of humans. An extensive set of experiments suggests that our technique outperforms state-of-the-art methods to model the behavior of pedestrians, which also makes it applicable to fields such as behavioral science or computer graphics.

Robust Sound Source Localization Using a Microphone Array on a Mobile Robot

Abstract: The hearing sense on a mobile robot is important because it is omnidirectional and it does not require direct line-of-sight with the sound source. Such capabilities can nicely complement vision to help localize a person or an interesting event in the environment. To do so the robot auditory system must be able to work in noisy, unknown and diverse environmental conditions. In this paper we present a robust sound source localization method in three-dimensional space using an array of 8 microphones. The method is based on time delay of arrival estimation. Results show that a mobile robot can localize in real time different types of sound sources over a range of 3 meters and with a precision of 3 degrees.

Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach

Abstract: Mobile robots tracking a reference trajectory are constrained by the motion limits of their actuators, which impose the requirement for high autonomy driving capabilities in robots This paper presents a model predictive control (MPC) scheme incorporating neural-dynamic optimization to achieve trajectory tracking of nonholonomic mobile robots (NMRs) By using the derived tracking-error kinematics of nonholonomic robots, the proposed MPC approach is iteratively transformed as a constrained quadratic programming (QP) problem, and then a primal–dual neural network is used to solve this QP problem over a finite receding horizon The applied neural-dynamic optimization can make the cost function of MPC converge to the exact optimal values of the formulated constrained QP Compared with the existing fast MPC, which requires repeatedly calculating the Hessian matrix of the Langragian and then solves a quadratic program The computation complexity reaches ${O}({n}^{{3}})$ , while the proposed neural-dynamic optimization contains ${O}({n}^{{2}})$ operations Finally, extensive experiments are provided to illustrate that the MPC scheme has an effective performance on a real mobile robot system

A Generalized Extended Kalman Filter Implementation for the Robot Operating System

Abstract: Accurate state estimation for a mobile robot often requires the fusion of data from multiple sensors. Software that performs sensor fusion should therefore support the inclusion of a wide array of heterogeneous sensors. This paper presents a software package, robot_localization, for the robot operating system (ROS). The package currently contains an implementation of an extended Kalman filter (EKF). It can support an unlimited number of inputs from multiple sensor types, and allows users to customize which sensor data fields are fused with the current state estimate. In this work, we motivate our design decisions, discuss implementation details, and provide results from real-world tests.

Robot skills for manufacturing

Abstract: Due to a general shift in manufacturing paradigm from mass production towards mass customization, reconfigurable automation technologies, such as robots, are required. However, current industrial robot solutions are notoriously difficult to program, leading to high changeover times when new products are introduced by manufacturers. In order to compete on global markets, the factories of tomorrow need complete production lines, including automation technologies that can effortlessly be reconfigured or repurposed, when the need arises. In this paper we present the concept of general, self-asserting robot skills for manufacturing. We show how a relatively small set of skills are derived from current factory worker instructions, and how these can be transferred to industrial mobile manipulators. General robot skills can not only be implemented on these robots, but also be intuitively concatenated to program the robots to perform a variety of tasks, through the use of simple task-level programming methods. We demonstrate various approaches to this, extensively tested with several people inexperienced in robotics. We validate our findings through several deployments of the complete robot system in running production facilities at an industrial partner. It follows from these experiments that the use of robot skills, and associated task-level programming framework, is a viable solution to introducing robots that can intuitively and on the fly be programmed to perform new tasks by factory workers. HighlightsWe propose a conceptual model of robot skills and show how this differs from macros.We show how this approach can enable non-experts to utilize advanced robotic systems.Concrete industrial applications of the approach are presented, on advanced robot systems.

Multiple Mobile Robot Systems

Abstract: Within the context of multiple mobile, and networked robot systems, this chapter explores the current state of the art. After a brief introduction, we first examine architectures for multirobot cooperation, exploring the alternative approaches that have been developed. Next, we explore communications issues and their impact on multirobot teams in Sect. 53.3, followed by a discussion of networked mobile robots in Sect. 53.4. Following this we discuss swarm robot systems in Sect. 53.5 and modular robot systems in Sect. 53.6. While swarm and modular systems typically assume large numbers of homogeneous robots, other types of multirobot systems include heterogeneous robots. We therefore next discuss heterogeneity in cooperative robot teams in Sect. 53.7. Once robot teams allow for individual heterogeneity, issues of task allocation become important; Sect. 53.8 therefore discusses common approaches to task allocation. Section 53.9 discusses the challenges of multirobot learning, and some representative approaches. We outline some of the typical application domains which serve as test beds for multirobot systems research in Sect. 53.10. Finally, we conclude in Sect. 53.11 with some summary remarks and suggestions for further reading.

Overtrust of Robots in Emergency Evacuation Scenarios

Abstract: Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we performed an experiment where a participant interacts with a robot in a non-emergency task to experience its behavior and then chooses whether to follow the robot's instructions in an emergency or not. Artificial smoke and fire alarms were used to add a sense of urgency. To our surprise, all 26 participants followed the robot in the emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to safely exit the way they entered.

Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking

Abstract: This paper presents a Robust Constrained Learning-based Nonlinear Model Predictive Control RC-LB-NMPC algorithm for path-tracking in off-road terrain. For mobile robots, constraints may represent solid obstacles or localization limits. As a result, constraint satisfaction is required for safety. Constraint satisfaction is typically guaranteed through the use of accurate, a priori models or robust control. However, accurate models are generally not available for off-road operation. Furthermore, robust controllers are often conservative, since model uncertainty is not updated online. In this work our goal is to use learning to generate low-uncertainty, non-parametric models in situ. Based on these models, the predictive controller computes both linear and angular velocities in real-time, such that the robot drives at or near its capabilities while respecting path and localization constraints. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, off-road environments. The paper presents experimental results, including over 5 km of travel by a 900 kg skid-steered robot at speeds of up to 2.0 m/s. The result is a robust, learning controller that provides safe, conservative control during initial trials when model uncertainty is high and converges to high-performance, optimal control during later trials when model uncertainty is reduced with experience.

Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning

Abstract: A key skill for mobile robots is the ability to navigate efficiently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially adaptive manner such as respecting comfort zones of the pedestrians. We propose a framework for socially adaptive path planning in dynamic environments, by generating human-like path trajectory. Our framework consists of three modules: a feature extraction module, inverse reinforcement learning (IRL) module, and a path planning module. The feature extraction module extracts features necessary to characterize the state information, such as density and velocity of surrounding obstacles, from a RGB-depth sensor. The inverse reinforcement learning module uses a set of demonstration trajectories generated by an expert to learn the expert’s behaviour when faced with different state features, and represent it as a cost function that respects social variables. Finally, the planning module integrates a three-layer architecture, where a global path is optimized according to a classical shortest-path objective using a global map known a priori, a local path is planned over a shorter distance using the features extracted from a RGB-D sensor and the cost function inferred from IRL module, and a low-level system handles avoidance of immediate obstacles. We evaluate our approach by deploying it on a real robotic wheelchair platform in various scenarios, and comparing the robot trajectories to human trajectories.

Leader–Follower Formation and Tracking Control of Mobile Robots Along Straight Paths

Abstract: We address the problem of tracking control of multiple mobile robots advancing in formation along straight-line paths. We use a leader–follower approach, and hence, we assume that only one swarm leader robot has the information of the reference trajectory. Then, each robot receives information from one intermediary leader only. Therefore, the communications graph forms a simple spanning directed tree. As the existence of a spanning tree is necessary to achieve consensus, it is the minimal configuration possible to achieve the formation-tracking objective. From a technological viewpoint, this has a direct impact on the simplicity of its implementation; e.g., less sensors are needed. Our controllers are partially linear time-varying with a simple added nonlinearity satisfying a property of persistency of excitation, tailored for nonlinear systems. Structurally speaking, the controllers are designed with the aim of separating the tasks of position-tracking and orientation. Our main results ensure the uniform global asymptotic stabilization of the closed-loop system, and hence, they imply robustness with respect to perturbations. All these aspects make our approach highly attractive in diverse application domains of vehicles’ formations such as factory settings.

Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

Abstract: This paper presents a Learning-based Nonlinear Model Predictive Control LB-NMPC algorithm to achieve high-performance path tracking in challenging off-road terrain through learning The LB-NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model Disturbances are modeled as a Gaussian process GP as a function of system state, input, and other relevant variables The GP is updated based on experience collected during previous trials Localization for the controller is provided by an onboard, vision-based mapping and navigation system enabling operation in large-scale, GPS-denied environments The paper presents experimental results including over 3i¾?km of travel by three significantly different robot platforms with masses ranging from 50 to 600 kg and at speeds ranging from 035 to 12 m/s associated video at http://tinycc/RoverLearnsDisturbances Planned speeds are generated by a novel experience-based speed scheduler that balances overall travel time, path-tracking errors, and localization reliability The results show that the controller can start from a generic a priori vehicle model and subsequently learn to reduce vehicle- and trajectory-specific path-tracking errors based on experience

A deep-network solution towards model-less obstacle avoidance

Abstract: Obstacle avoidance is the core problem for mobile robots. Its objective is to allow mobile robots to explore an unknown environment without colliding into other objects. It is the basis for various tasks, e.g. surveillance and rescue, etc. Previous approaches mainly focused on geometric models (such as constructing local cost-maps) which could be regarded as low-level intelligence without any cognitive process. Recently, deep learning has made great breakthroughs in computer vision, especially for recognition and cognitive tasks. It takes advantage of the hierarchical models inspired by human brain structures. However, it is a fact that deep learning, up till now, has seldom been used for controlling and decision making. Inspired by the advantages of deep learning, we take indoor obstacle avoidance as example to show the effectiveness of a hierarchical structure that fuses a convolutional neural network (CNN) with a decision process. It is a highly compact network structure that takes raw depth images as input, and generates control commands as network output, by which a model-less obstacle avoidance behavior is achieved. We test our approach in real-world indoor environments. The new findings and results are reported at the end of the paper.

Review of visual odometry: types, approaches, challenges, and applications

Abstract: Accurate localization of a vehicle is a fundamental challenge and one of the most important tasks of mobile robots For autonomous navigation, motion tracking, and obstacle detection and avoidance, a robot must maintain knowledge of its position over time Vision-based odometry is a robust technique utilized for this purpose It allows a vehicle to localize itself robustly by using only a stream of images captured by a camera attached to the vehicle This paper presents a review of state-of-the-art visual odometry (VO) and its types, approaches, applications, and challenges VO is compared with the most common localization sensors and techniques, such as inertial navigation systems, global positioning systems, and laser sensors Several areas for future research are also highlighted

An information gain formulation for active volumetric 3D reconstruction

Abstract: We consider the problem of next-best view selection for volumetric reconstruction of an object by a mobile robot equipped with a camera. Based on a probabilistic volumetric map that is built in real time, the robot can quantify the expected information gain from a set of discrete candidate views. We propose and evaluate several formulations to quantify this information gain for the volumetric reconstruction task, including visibility likelihood and the likelihood of seeing new parts of the object. These metrics are combined with the cost of robot movement in utility functions. The next best view is selected by optimizing these functions, aiming to maximize the likelihood of discovering new parts of the object. We evaluate the functions with simulated and real world experiments within a modular software system that is adaptable to other robotic platforms and reconstruction problems. We release our implementation open source.

Vision-Based Model Predictive Control for Steering of a Nonholonomic Mobile Robot

Abstract: In this paper, we have developed a novel visual servo-based model predictive control method to steer a wheeled mobile robot (WMR) moving in a polar coordinate toward a desired target. The proposed control scheme has been realized at both kinematics and dynamics levels. The kinematics predictive steering controller generates command of desired velocities that are achieved by employing a low-level motion controller, while the dynamics predictive controller directly generates torques used to steer the WMR to the target. In the presence of both kinematics and dynamics constraints, the control design is carried out using quadratic programming (QP) for optimal performance. The neurodynamic optimization technique, particularly the primal-dual neural network, is employed to solve the QP problems. Theoretical analysis has been first performed to show that the desired velocities can be achieved with the guaranteed stability, as well as with the global convergence to the optimal solutions of formulated convex programming problems. Experiments have then been carried out to validate the effectiveness of the proposed control scheme and illustrate its advantage over the conventional methods.

Enhanced Robot Audition Based on Microphone Array Source Separation with Post-Filter

Abstract: We propose a system that gives a mobile robot the ability to separate simultaneous sound sources. A microphone array is used along with a real-time dedicated implementation of Geometric Source Separation and a post-filter that gives us a further reduction of interferences from other sources. We present results and comparisons for separation of multiple non-stationary speech sources combined with noise sources. The main advantage of our approach for mobile robots resides in the fact that both the frequency-domain Geometric Source Separation algorithm and the post-filter are able to adapt rapidly to new sources and non-stationarity. Separation results are presented for three simultaneous interfering speakers in the presence of noise. A reduction of log spectral distortion (LSD) and increase of signal-to-noise ratio (SNR) of approximately 10 dB and 14 dB are observed.

Neural networks based reinforcement learning for mobile robots obstacle avoidance

Abstract: We propose a new path planning algorithm based on the use of Q-learning and artificial neural networks.We analyze and model in VR the mobile robot PowerBot.We implement and test the proposed algorithm in both VR and real workspaces.The solution converges to collision-free trajectories in dynamic environments. This study proposes a new approach for solving the problem of autonomous movement of robots in environments that contain both static and dynamic obstacles. The purpose of this research is to provide mobile robots a collision-free trajectory within an uncertain workspace which contains both stationary and moving entities. The developed solution uses Q-learning and a neural network planner to solve path planning problems. The algorithm presented proves to be effective in navigation scenarios where global information is available. The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. The solution is deployed in both Virtual Reality (VR) for easier visualization and safer testing activities, and on a real mobile robot for experimental validation. The algorithm is compared with Powerbot's ARNL proprietary navigation algorithm. Results show that the proposed solution has a good conversion rate computed at a satisfying speed.

Cooperative formation control of autonomous underwater vehicles: An overview

Abstract: Formation control is a cooperative control concept in which multiple autonomous underwater mobile robots are deployed for a group motion and/or control mission. This paper presents a brief review on various cooperative search and formation control strategies for multiple autonomous underwater vehicles (AUV) based on literature reported till date. Various cooperative and formation control schemes for collecting huge amount of data based on formation regulation control and formation tracking control are discussed. To address the challenge of detecting AUV failure in the fleet, communication issues, collision and obstacle avoidance are also taken into attention. Stability analysis of the feasible formation is also presented. This paper may be intended to serve as a convenient reference for the further research on formation control of multiple underwater mobile robots.

Two Time-Scale Tracking Control of Nonholonomic Wheeled Mobile Robots

Abstract: In this paper, a practical control law is proposed for wheeled mobile robots in order to both improve the transient performance and repress the tracking errors. In particular, a two time-scale filtering technique, which can derive a fast variable to compensate for the disturbance, is applied during the carbot’s moving process. The nominal system is governed using a controller derived under the back-stepping framework. Such a design can effectively realize the system’s tracking objective and enhance robustness via properly configured parameters. In the meantime, a two time-scale filter is applied to the system function to estimate the disturbances, essentially improving the system’s precision. By virtue of this innovative technique, the final performance of the system is satisfactory in terms of both transient response and tracking error rejection. A previous sliding mode based control law is compared with the propounded one with respect to transient behavior and steady-state errors, and two types of disturbances, respectively the constant and sinusoid are simulated to verify the filter’s effectiveness. Since, from the results, there is significant improvement in both transient and steady-state performance, the proposed method is confirmed to be practical for tracking control of the wheeled mobile robots.

Formation Control of Leader–Follower Mobile Robots’ Systems Using Model Predictive Control Based on Neural-Dynamic Optimization

Abstract: In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for controlling leader–follower mobile robots formation. Consider obstacles in the environments, a control strategy is proposed for the formations which includes separation-bearing-orientation scheme (SBOS) for regular leader–follower formation and separation-distance scheme (SDS) for obstacle avoidance. During the formation motion, the leader robot shall track a desired trajectory and the desire leader–follower relationship can be maintained through SBOS method; meanwhile, the followers can avoid the collision by applying the SDS. The formation-error kinematics of both SBOS and SDS are derived and a constrained quadratic programming (QP) can be obtained by transforming the MPC method. Then, over a finite-receding horizon, the QP problem can be solved by utilizing the primal-dual neural network (PDNN) with parallel capability. The computation complexity can be greatly reduced by the implemented neural-dynamic optimization. Compared with other existing formation control approaches, the developed solution in this paper is rooted in NMPC techniques with input constraints and the novel QP problem formulation. Finally, experimental studies of the proposed formation control approach have been performed on several mobile robots to verify the effectiveness.

Optimal path planning for a mobile robot using cuckoo search algorithm

Abstract: The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.

Motion Planning and Obstacle Avoidance

Abstract: This chapter describes motion planning and obstacle avoidance for mobile robots. We will see how the two areas do not share the same modeling background. From the very beginning of motion planning, research has been dominated by computer sciences. Researchers aim at devising well-grounded algorithms with well-understood completeness and exactness properties.

Navigation planning for legged robots in challenging terrain

Abstract: This paper presents a framework for planning safe and efficient paths for a legged robot in rough and unstructured terrain. The proposed approach allows to exploit the distinctive obstacle negotiation capabilities of legged robots, while keeping the complexity low enough to enable planning over considerable distances in short time. We compute typical terrain characteristics such as slope, roughness, and steps to build a traversability map. This map is used to assess the costs of individual robot footprints as a function of the robot-specific obstacle negotiating capabilities for steps, gaps and stairs. Our sampling-based planner employs the RRT* algorithm to optimize path length and safety. The planning framework has a hierarchical architecture to frequently replan the path during execution as new terrain is perceived with onboard sensors. Furthermore, a cascaded planning structure makes use of different levels of simplification to allow for fast search in simple environments, while retaining the ability to find complex solutions, such as paths through narrow passages. The proposed navigation planning framework is integrated on the quadrupedal robot StarlETH and extensively tested in simulation as well as on the real platform.

Nonlinear Control for Tracking and Obstacle Avoidance of a Wheeled Mobile Robot With Nonholonomic Constraint

Abstract: This brief presents a novel control scheme for some problems on tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint. An extended state observer is introduced to estimate the unknown disturbances and velocity information of the wheeled mobile robot. A nonlinear controller is designed to achieve tracking target and obstacle avoidance in complex environments. Note that tracking errors converge to a residual set outside the obstacle detection region. Moreover, the obstacle avoidance is also guaranteed inside the obstacle detection region. Simulation results are given to verify the effectiveness and robustness of the proposed design scheme.

Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning

Abstract: Mobile robots that navigate in populated environments require the capacity to move efficiently, safely and in human-friendly ways. In this paper, we address this task using a learning approach that enables a mobile robot to acquire navigation behaviors from demonstrations of socially normative human behavior. In the past, such approaches have been typically used to learn only simple behaviors under relatively controlled conditions using rigid representations or with methods that scale poorly to large domains. We thus develop a flexible graph-based representation able to capture relevant task structure and extend Bayesian inverse reinforcement learning to use sampled trajectories from this representation. In experiments with a real robot and a large-scale pedestrian simulator, we are able to show that the approach enables a robot to learn complex navigation behaviors of varying degrees of social normativeness using the same set of simple features.

Localization of Simultaneous Moving Sound Sources for Mobile Robot Using a Frequency-Domain Steered Beamformer Approach

Abstract: Mobile robots in real-life settings would benefit from being able to localize sound sources. Such a capability can nicely complement vision to help localize a person or an interesting event in the environment, and also to provide enhanced processing for other capabilities such as speech recognition. In this paper we present a robust sound source localization method in three-dimensional space using an array of 8 microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a probabilistic post-processor. Results show that a mobile robot can localize in real time multiple moving sources of different types over a range of 5 meters with a response time of 200 ms.