Teleoperation of heavy machinery in industry often requires operators to be in close proximity to the plant and issue commands on a per-actuator level using joystick input devices. However, this is non-intuitive and makes achieving desired job properties a challenging task requiring operators to complete extensive and costly training. Despite this, operator fatigue is common with implications for personal safety, project timeliness, cost, and quality. While full automation is not yet achievable due to unpredictability and the dynamic nature of the environment and task, shared control paradigms allow operators to issue high-level commands in an intuitive, task-informed control space while having the robot optimize for achieving desired job properties. In this paper, we compare a number of modes of teleoperation, exploring both the number of dimensions of the control input as well as the most intuitive control spaces. Our experimental evaluations of the performance metrics were based on quantifying the difficulty of tasks based on the well known Fitts' law as well as a measure of how well constraints affecting the task performance were met. Our experiments show that higher performance is achieved when humans submit commands in low-dimensional task spaces as opposed to joint space manipulations.
From the concrete spraying literature, a task space control framework was developed by Honegger and Codourey @cite_18 based on the inverse-kinematics method. The system allows the operator to control the end-effector of the concrete spraying unit using a 6-DoF space mouse. Later work by Girmscheid and Moser @cite_12 developed a sliding autonomy approach for a high-DoF concrete spraying unit. They define three levels of autonomy: (1) A manual mode, consisting of no autonomy and implementing common joint-level control, (2) A semi-automated mode that, using a pre-collected laser scan of a tunnel cavity and computed geometry, allows the operator to command only the position on the wall to spray whilst the system accounts for motion constraints, and (3) An automated mode that plans and executes an entire trajectory. The latter mode, they note, is only in development for tunnel-boring machine projects where the excavated tunnel surface is regular and very smooth. Modes of teleoperation compared in this work are inspired by these control techniques developed for concrete spraying applications.
@cite_18 @cite_12
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In tunneling and mining construction work, heavy and large manipulators are used to spray liquid concrete on the walls. These manipulators are usually operated manually with simple units allowing control of all actuators independently. In cooperation between industry and university, a novel control system has been developed for the redundant heavy manipulator Robojet(R) that supports the operator by calculating the inverse kinematic model and controlling all actuators automatically. The paper presents the solution of the redundant inverse kinematics of the Robojet using the null-space method and its implementation in a control system. Optimized shotcrete application techniques are required in particular tunneling projects. Today, any spraying can be done by hand or by manipulator. With the development in the material technologies, the range of possible operation was enlarged. With automation of the application, an important contribution to improve performance and quality and to reduce rebound may be achieved. At the Swiss Federal Institute of Technology, Zurich, systematic research is done to develop the fully automated process control, focusing on the wet shotcrete method. With the development of the fully automated shotcrete robot, the user will have a very effective tool at his or her disposal to spray concrete shells (fully automated). With the new robot, the user may choose from three different modes: manual, semiautomated, and fully automated spraying. Especially the fully automated mode facilitates higher performance with less danger to the worker's health. Quality control is inherent in the application process in regard to layer thickness, compaction, and homogeneity. We present the development of a full-scale “parallel autonomy” research platform including software and hardware. In the parallel autonomy paradigm, the control of the vehicle is shared; the human is still in control of the vehicle, but the autonomy system is always running in the background to prevent accidents. Our holistic approach includes: (1) a drive-by-wire conversion method only based on reverse engineering mounting of relatively inexpensive sensors onto the vehicle implementation of a localization and mapping system, (4) obstacle detection and (5) a shared controller as well as (6) integration with an advanced autonomy simulation system (Drake) for rapid development and testing. The system can operate in three modes: (a) manual driving, (b) full autonomy, where the system is in complete control of the vehicle and (c) parallel autonomy, where the shared controller is implemented. We present results from extensive testing of a full-scale vehicle on closed tracks that demonstrate these capabilities. This paper describes a strawberry-harvesting robot, a packing robot, and a movable bench system. The harvesting and packing operations in strawberry production require harder, more time-consuming work compared to other operations such as transplanting and chemical spraying, making automation of these tasks desirable. Since harvesting and packing operation account for half of total working hours, automation of these tasks are strongly desired. First of all, based on the findings of many studies on strawberry-harvesting robots for soil culture and elevated substrate culture, our institute of the Bio-oriented Technology Research Advancement Institution and Shibuya Seiki developed a commercial model of a strawberry-harvesting robot, which is chiefly composed a cylindrical manipulator, machine vision, an end-effector, and traveling platform. The results showed an average 54.9 harvesting success rate, 8.6 s cycle time of picking operation, and 102.5 m h work efficiency in hanging-type growing beds in an experimental greenhouse. Secondly, a prototype automatic packing robot consisting of a supply unit and a packing unit was developed. The supply unit picks up strawberries from a harvesting container, and the packing unit sucks each fruit from calyx side and locates its orientation into a tray. Performance testing showed that automatic packing had a task success rate of 97.3 , with a process time per fruit of 7.3 s. Thirdly, a movable bench system was developed, which makes planting beds rotate in longitudinal and lateral ways. This system brought high density production and labour saving operation at a fixed position, such as crop maintenance and harvesting. By setting up the main body of a strawberry-harvesting robot on working space, unmanned operation technique was developed and tested in an experimental greenhouse. Field experiments of these new automation technologies were conducted and gave a potential of practical use. Human-robot interaction is becoming an increasingly important research area. In this paper, we present a theoretical characterization of interaction efficiency with an aim towards designing a human-robot system with adjustable robot autonomy. In our approach, we analyze how modifying robot control schemes for a given autonomy mode can increase system performance and decrease workload demands on the human operator. We then perform a case study of the design of a shared-control teleoperation scheme and compare interaction efficiency against a traditional manual-control teleoperation scheme. Heap spraying is an attack technique commonly used in hijacking browsers to download and execute malicious code. In this attack, attackers first fill a large portion of the victim process's heap with malicious code. Then they exploit a vulnerability to redirect the victim process's control to attackers' code on the heap. Because the location of the injected code is not exactly predictable, traditional heap-spraying attacks need to inject a huge amount of executable code to increase the chance of success. Injected executable code usually includes lots of NOP-like instructions leading to attackers' shellcode. Targeting this attack characteristic, previous solutions detect heap-spraying attacks by searching for the existence of such large amount of NOP sled and other shellcode. In this paper, we analyze the implication of modern operating systems' memory allocation granularity and present Heap Taichi, a new heap spraying technique exploiting the weakness in memory alignment. We describe four new heap object structures that can evade existing detection tools, as well as proof-of-concept heap-spraying code implementing our technique. Our research reveals that a large amount of NOP sleds is not necessary for a reliable heap-spraying attack. In our experiments, we showed that our heap-spraying attacks are a realistic threat by evading existing detection mechanisms. To detect and prevent the new heap-spraying attacks, we propose enhancement to existing approaches and propose to use finer memory allocation granularity at memory managers of all levels. We also studied the impact of our solution on system performance. Flying in and around caves, tunnels, and buildings demands more than one sensing modality. This article presented an optic-flow- based approach inspired by flying insects for avoiding lateral collisions. However, there were a few real-world scenarios in which optic flow sensing failed. This occurred when obstacles on approach were directly in front of the aircraft. Here, a simple sonar or infrared sensor can be used to trigger a quick transition into the hovering mode to avoid the otherwise fatal collision. Toward this end, we have demonstrated a fixed-wing prototype capable of manually transitioning from conventional cruise flight into the hovering mode. The prototype was then equipped with an IMU and a flight control system to automate the hovering process. The next step in this research is to automate the transition from cruise to hover flight. We present a novel 6-DoF cable driven manipulator for handheld robotic tasks. Based on a coupled tendon approach, the arm is optimized to maximize movement speed and configuration space while reducing the total mass of the arm. We propose a space carving approach to design optimal link geometry maximizing structural strength and joint limits while minimizing link mass. The design improves on similar non-handheld tendon-driven manipulators and reduces the required number of actuators to one per DoF. As the manipulator has one redundant joint, we present a 5-DoF inverse kinematics solution for the end effector pose. The inverse kinematics is solved by splitting the 6-DoF problem into two coupled 3-DoF problems and merging their results. A method for gracefully degrading the output of the inverse kinematics is described for cases where the desired end effector pose is outside the configuration space. This is useful for settings where the user is in the control loop and can help the robot to get closer to the desired location. The design of the handheld robot is offered as open source. While our results and tools are aimed at handheld robotics, the design and approach is useful to non-handheld applications. Tracked vehicles are frequently used as search-and-rescue robots for exploring disaster areas. To enhance their ability to traverse rough terrain, some of these robots are equipped with swingable subtracks. However, manual control of such subtracks also increases the operator's workload, particularly in teleoperation with limited camera views. To eliminate this trade-off, we have developed a shared autonomy system using an autonomous controller for subtracks that is based on continuous three-dimensional terrain scanning. Using this system, the operator has only to specify the direction of travel to the robot, following which the robot traverses rough terrain using autonomously generated subtrack motions. In our system, real-time terrain slices near the robot are obtained using two or three LIDAR (laser imaging detection and ranging) sensors, and these terrain slices are integrated to generate three-dimensional terrain information. In this paper, we introduce an autonomous controller for subtracks and validate the reliability of a shared autonomy system on actual rough terrains through experimental results. © 2011 Wiley Periodicals, Inc. The excavation of foundations, general earthworks and earth removal tasks are activities which involve the machine operator in a series of repetitive operations, suggesting opportunities for the automation through the introduction of robotic technologies with subsequent improvements in machine utilisation and throughput. The automation of the earth removal process is also likely to provide a number of other benefits such as a reduced dependence on operator skills and a lower operator work load, both of which might be expected to contribute to improvements in quality and, in particular, the removal of the need for a local operator when working in hazardous environments. The Lancaster University Computerised Intelligent Excavator or LUCIE has demonstrated the achievement of automated and robotic excavation through the implementation of an integrated, real-time, artificial intelligence based control system utilising a novel form of motion control strategy for movement of the excavator bucket through ground. Having its origins in the systematic observation of a range of machine operators of differing levels of expertise, the control strategy as evolved enables the autonomous excavation of a high quality rectangular trench in a wide variety of types and conditions of ground and the autonomous removal of obstacles such as boulders along the line of that trench. The paper considers the development of the LUCIE programme since its inception and sets out in terms of the machine kinematics the evolution and development of the real-time control strategy from an implementation on a one-fifth scale model of a back-hoe arm to a full working system on a JCB801 360° tracked excavator. Robots for precision farming have the potential to reduce the reliance on herbicides and pesticides through selectively spraying individual plants or through manual weed removal. To achieve this, the value crops and the weeds must be identified by the robot's perception system to trigger the actuators for spraying or removal. In this paper, we address the problem of detecting the sugar beet plants as well as weeds using a camera installed on a mobile robot operating on a field. We propose a system that performs vegetation detection, feature extraction, random forest classification, and smoothing through a Markov random field to obtain an accurate estimate of the crops and weeds. We implemented and thoroughly evaluated our system on a real farm robot on different sugar beet fields and illustrate that our approach allows for accurately identifying the weed on the field. High-performance concurrent priority queues are essential for applications such as task scheduling and discrete event simulation. Unfortunately, even the best performing implementations do not scale past a number of threads in the single digits. This is because of the sequential bottleneck in accessing the elements at the head of the queue in order to perform a DeleteMin operation. In this paper, we present the SprayList, a scalable priority queue with relaxed ordering semantics. Starting from a non-blocking SkipList, the main innovation behind our design is that the DeleteMin operations avoid a sequential bottleneck by spraying'' themselves onto the head of the SkipList list in a coordinated fashion. The spraying is implemented using a carefully designed random walk, so that DeleteMin returns an element among the first O(p log^3 p) in the list, with high probability, where p is the number of threads. We prove that the running time of a DeleteMin operation is O(log^3 p), with high probability, independent of the size of the list. Our experiments show that the relaxed semantics allow the data structure to scale for high thread counts, comparable to a classic unordered SkipList. Furthermore, we observe that, for reasonably parallel workloads, the scalability benefits of relaxation considerably outweigh the additional work due to out-of-order execution. In this paper we describe a vision-based algorithm to control a vertical-takeoff-and-landing unmanned aerial vehicle while tracking and landing on a moving platform. Specifically, we use image-based visual servoing (IBVS) to track the platform in two-dimensional image space and generate a velocity reference command used as the input to an adaptive sliding mode controller. Compared with other vision-based control algorithms that reconstruct a full three-dimensional representation of the target, which requires precise depth estimation, IBVS is computationally cheaper since it is less sensitive to the depth estimation allowing for a faster method to obtain this estimate. To enhance velocity tracking of the sliding mode controller, an adaptive rule is described to account for the ground effect experienced during the maneuver. Finally, the IBVS algorithm integrated with the adaptive sliding mode controller for tracking and landing is validated in an experimental setup using a quadrotor. Real-time control of the end-effector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates inverse kinematics learning for resolved motion rate control (RMRC) employing an optimization criterion to resolve kinematic redundancies. Our learning approach is based on the key observations that learning an inverse of a nonuniquely invertible function can be accomplished by augmenting the input representation to the inverse model and by using a spatially localized learning approach. We apply this strategy to inverse kinematics learning and demonstrate how a recently developed statistical learning algorithm, locally weighted projection regression, allows efficient learning of inverse kinematic mappings in an incremental fashion even when input spaces become rather high dimensional. Our results are illustrated with a 30-DOF humanoid robot. Deep generative models have recently shown great promise in imitation learning for motor control. Given enough data, even supervised approaches can do one-shot imitation learning; however, they are vulnerable to cascading failures when the agent trajectory diverges from the demonstrations. Compared to purely supervised methods, Generative Adversarial Imitation Learning (GAIL) can learn more robust controllers from fewer demonstrations, but is inherently mode-seeking and more difficult to train. In this paper, we show how to combine the favourable aspects of these two approaches. The base of our model is a new type of variational autoencoder on demonstration trajectories that learns semantic policy embeddings. We show that these embeddings can be learned on a 9 DoF Jaco robot arm in reaching tasks, and then smoothly interpolated with a resulting smooth interpolation of reaching behavior. Leveraging these policy representations, we develop a new version of GAIL that (1) is much more robust than the purely-supervised controller, especially with few demonstrations, and (2) avoids mode collapse, capturing many diverse behaviors when GAIL on its own does not. We demonstrate our approach on learning diverse gaits from demonstration on a 2D biped and a 62 DoF 3D humanoid in the MuJoCo physics environment. Shared control is a key technology for various robotic applications in which a robotic system and a human operator are meant to collaborate efficiently. In order to achieve efficient task execution in shared control, it is essential to predict the desired behavior for a given situation or context in order to simplify the control task for the human operator. This prediction is obtained by exploiting Learning from Demonstration (LfD), which is a popular approach for transferring human skills to robots. We encode the demonstrated behavior as trajectory distributions and generalize the learned distributions to new situations. The goal of this paper is to present a shared control framework that uses learned expert distributions to gain more autonomy. Our approach controls the balance between the controller's autonomy and the human preference based on the distributions of the demonstrated trajectories. Moreover, the learned distributions are autonomously refined from collaborative task executions, resulting in a master-slave system with increasing autonomy that requires less user input with an increasing number of task executions. We experimentally validated that our shared control approach enables efficient task executions. Moreover, the conducted experiments demonstrated that the developed system improves its performances through interactive task executions with our shared control. Stochastic switched systems are a relevant class of stochastic hybrid systems with probabilistic evolution over a continuous domain and control-dependent discrete dynamics over a finite set of modes. In the past few years several different techniques have been developed to assist in the stability analysis of stochastic switched systems. However, more complex and challenging objectives related to the verification of and the controller synthesis for logic specifications have not been formally investigated for this class of systems as of yet. With logic specifications we mean properties expressed as formulae in linear temporal logic or as automata on infinite strings. This paper addresses these complex objectives by constructively deriving approximately equivalent (bisimilar) symbolic models of stochastic switched systems. More precisely, this paper provides two different symbolic abstraction techniques: one requires state space discretization, but the other one does not require any space discretization which can be potentially more efficient than the first one when dealing with higher dimensional stochastic switched systems. Both techniques provide finite symbolic models that are approximately bisimilar to stochastic switched systems under some stability assumptions on the concrete model. This allows formally synthesizing controllers (switching signals) that are valid for the concrete system over the finite symbolic model, by means of mature automata-theoretic techniques in the literature. The effectiveness of the results are illustrated by synthesizing switching signals enforcing logic specifications for two case studies including temperature control of a six-room building. This paper describes geometric and kinematic modelling steps of a class of an hybrid hyper-redundant robot (HHRR). The main problem of such complex manipulator robot structures is the synthesis of an appropriate control of n number of degrees of freedom (DOF). Thus, it is important at the design step, to develop geometric and kinematic models allowing to reconstruct the kinematical behavior of n concatenated modules. The studied structure is considered as modular with multiple DOF and is built by connecting in serial many parallel modules. The coordinate frame distribution is based on space curve differential geometry. The inverse Geometric and kinematic models are calculated analytically for a n-DOF of HHRR manipulator while the final application is applied to the case of a 3-modules robot (18-DOF). The authors present an overview of a hierarchical framework for coordinating task- and motion-level operations in multirobot systems. Their framework is based on the idea of using simple temporal networks to simultaneously reason about precedence causal constraints required for task-level coordination and simple temporal constraints required to take some kinematic constraints of robots into account. In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account. In the plan-execution phase, the framework provides a method for absorbing an imperfect plan execution to avoid time-consuming re-planning in many cases. The authors use the multirobot path-planning problem as a case study to present the key ideas behind their framework for the long-term autonomy of multirobot systems. One of the guarantees that the designers of on-board satellite systems need to provide, so as to ensure their dependability, is that the mode transition scheme is implemented correctly, i.e. that the states of system components are consistent with the global system mode. There is still, however, a lack of scalable approaches to developing and verifying systems with complex mode transitions. This paper presents an approach to the formal development of mode-rich systems by refinement in Event-B. We formalise the concepts of modes and mode transitions as well as deriving specification and refinement patterns which support correct-by-construction system development. The proposed approach is validated by a formal development of the Attitude and Orbit Control System (AOCS) undertaken within the ICT DEPLOY project. The experience gained in the course of developing such a complex industrial system as AOCS, shows that Event-B refinement provides the engineers with a scalable formal technique. Moreover, the case study has demonstrated that Event-B can facilitate formal development of mode-rich systems and, in particular, proof-based verification of their mode consistency.