High-level Commands Research Articles

Semantic Mapping for Autonomous Subsea Intervention.

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based simultaneous localization and mapping (SLAM) and 3D object recognition using a database of a priori known objects. The robot uses Doppler velocity log (DVL), pressure, and attitude and heading reference system (AHRS) sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the SLAM, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The SLAM provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

An Over-Actuated Multi-Rotor Aerial Vehicle With Unconstrained Attitude Angles and High Thrust Efficiencies

Fully-actuated multi-rotor aerial platforms are receiving increasing research interests for the capability of six degree-of-freedom (DOF) motions such as hovering at non-horizontal attitude angles. Existing real world hardware and experiments have demonstrated such capability for a limited range of angles. This letter presents an aerial platform that achieves maneuvering at arbitrary attitudes with uniformly high thrust efficiencies. We propose a novel vectoring thrust force actuator by mounting a regular quadcopter on a passive mechanical gimbal mechanism. The UAV platform achieves full six-DOF motion with redundancies from four of these vectoring thrust actuators. We present the hierarchical controller, which generates high-level virtual wrench commands, allocations to actuators, and low-level actuator controls. We demonstrate the first real-world experiments of any pose maneuver over 360 degrees without requiring unwrapping rotations.

Task-Level Authoring for Remote Robot Teleoperation.

Remote teleoperation of robots can broaden the reach of domain specialists across a wide range of industries such as home maintenance, health care, light manufacturing, and construction. However, current direct control methods are impractical, and existing tools for programming robot remotely have focused on users with significant robotic experience. Extending robot remote programming to end users, i.e., users who are experts in a domain but novices in robotics, requires tools that balance the rich features necessary for complex teleoperation tasks with ease of use. The primary challenge to usability is that novice users are unable to specify complete and robust task plans to allow a robot to perform duties autonomously, particularly in highly variable environments. Our solution is to allow operators to specify shorter sequences of high-level commands, which we call task-level authoring, to create periods of variable robot autonomy. This approach allows inexperienced users to create robot behaviors in uncertain environments by interleaving exploration, specification of behaviors, and execution as separate steps. End users are able to break down the specification of tasks and adapt to the current needs of the interaction and environments, combining the reactivity of direct control to asynchronous operation. In this paper, we describe a prototype system contextualized in light manufacturing and its empirical validation in a user study where 18 participants with some programming experience were able to perform a variety of complex telemanipulation tasks with little training. Our results show that our approach allowed users to create flexible periods of autonomy and solve rich manipulation tasks. Furthermore, participants significantly preferred our system over comparative more direct interfaces, demonstrating the potential of our approach for enabling end users to effectively perform remote robot programming.

Automated Support for Battle Operational–Strategic Decision-Making

Battle casualties are the subject of study in military operations research, which applies mathematical models to quantify the probability of victory vs. loss. In particular, different approaches have been proposed to model the course of battles. However, none of them provide adequate decision-making support for high-level command. To overcome this situation, this paper presents an innovative high-level decision-making model, which uses an adaptive and predictive control architecture. The paper reports empirical evidence supporting our model by considering one of the greatest battles of World War II: the Battle of Crete.

Gesture commands for controlling high-level UAV behavior

Directing groups of unmanned air vehicles (UAVs) is a task that typically requires the full attention of several operators. This can be prohibitive in situations where an operator must pay attention to their surroundings. In this paper we present a gesture device that assists operators in commanding UAVs in focus-constrained environments. The operator influences the UAVs’ behavior by using intuitive hand gesture movements. Gestures are captured using an accelerometer and gyroscope and then classified using a logistic regression model. Ten gestures were chosen to provide behaviors for a group of fixed-wing UAVs. These behaviors specified various searching, following, and tracking patterns that could be used in a dynamic environment. A novel variant of the Monte Carlo Tree Search algorithm was developed to autonomously plan the paths of the cooperating UAVs. These autonomy algorithms were executed when their corresponding gesture was recognized by the gesture device. The gesture device was trained to classify the ten gestures and accurately identified them 95% of the time. Each of the behaviors associated with the gestures was tested in hardware-in-the-loop simulations and the ability to dynamically switch between them was demonstrated. The results show that the system can be used as a natural interface to assist an operator in directing a fleet of UAVs.Article highlightsA gesture device was created that enables operators to command a group of UAVs in focus-constrained environments.Each gesture triggers high-level commands that direct a UAV group to execute complex behaviors.Software simulations and hardware-in-the-loop testing shows the device is effective in directing UAV groups.

Geometric control of quadrotor UAVs using integral backstepping

The traditional quadcopter control systems should deal with two common problems. Namely, the singularities related to the inverse kinematics and the ambiguity linked to the quaternion representation of the dynamic model. Moreover, the stability problem due to the system nonlinearity and high degree of coupling. This paper provides a solution to the two issues by employing a geometrical integral-backstepping control system. The integral terms were added to improve system ability to track desired trajectories. The high-level control laws are considered as a virtual control and transmitted to the low-level to track the high-level commands. The proposed control system along with the quadcopter dynamic model were expressed in the special Euclidean group SE(3). Finally, the control system robustness against mismatching parameters was studied while tracking various paths.

Learning a State Representation and Navigation in Cluttered and Dynamic Environments

In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-level navigation commands, the robot is able to safely locomote to a target location based on frames from a depth camera without any explicit mapping of the environment. First, the sequence of images and the current trajectory of the camera are fused to form a model of the world using state representation learning. The output of this lightweight module is then directly fed into a target-reaching and obstacle-avoiding policy trained with reinforcement learning. We show that decoupling the pipeline into these components results in a sample efficient policy learning stage that can be fully trained in simulation in just a dozen minutes. The key part is the state representation, which is trained to not only estimate the hidden state of the world in an unsupervised fashion, but also helps bridging the reality gap, enabling successful sim-to-real transfer. In our experiments with the quadrupedal robot ANYmal in simulation and in reality, we show that our system can handle noisy depth images, avoid dynamic obstacles unseen during training, and is endowed with local spatial awareness.

On the Design of Outdoor Leader-Follower UAV-Formation Controllers From a Practical Point of View

This work presents a control structure designed to autonomously guide two of-the-shelf quadrotors navigating in outdoor environments, as a leader-follower formation. The objective is to show how simple is to design controllers to guide the two vehicles when taking into account practical assumptions and simplifications in their model. This way, the main contribution of the paper is to show how practical knowledge obtained from real experiments using of-the-shelf quadrotors, translated to the assumptions and simplifications applied to its model, can help in the design of a suitable controller. The basic premise is that the quadrotor is driven by high-level commands that excite its degrees of freedom. The control system here designed following such reasoning is implemented in a ground station that allows manual or automatic control of the leader vehicle, with the follower one always automatically controlled. Experimental results in real outdoor scenarios, subject to unpredicted disturbances, validate the assumptions and simplifications applied to the model of the quadrotor, and the proposed control structure as well.

Robust Control for a Class of Nonlinearly Coupled Hierarchical Systems with Actuator Faults

This paper proposes an approach to addresses the control challenges posed by a fault-induced uncertainty in both the dynamics and control input effectiveness of a class of hierarchical nonlinear systems in which the high-level dynamics is nonlinearly coupled with a multi-agent low-level dynamics. The high-level dynamics has a multiplicative uncertainty in the control input effectiveness and is subjected to an exogenous disturbance input. On the other hand, the low-level system is subjected to actuator faults causing a time-varying multiplicative uncertainty in the dynamical model and associated control effectiveness. Moreover, the nonlinear coupling between the high-level and the low-level dynamics makes the problem even more challenging. To address this problem, an online parameter estimation algorithm is designed, coupled with an adaptive splitting mechanism which automatically distributes the control action among low level multi-agent systems. A nonlinear $\mathcal{L}_2$-gain-based controller, and then a state-feedback controller are designed in the high-level, and the low-level, respectively, to recover the system from faults with high performance in the transient response, and reject the exogenous disturbance. The resulting analysis guarantees a robust tracking of the high-level reference command signal.

Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments

The paper presents a new approach and a related real-time parallel simulation tool for modeling and analyzing a swarm of more than 60 distributed autonomous mobile robots communicating over an unreliable and capacity restricted wireless communication network. It includes a physical simulation of static obstacles, dynamic obstacles with scriptable movement, soil condition, active jammers, static and dynamic link obstacles with configurable damping as well as thermal noise. The simulated ground based mobile robots use CPBP [12] as probabilistic model predictive closed loop controller in combination with gradient free cost functions to evaluate complex unsteady goodness aggregations. The goal of this approach is the development of connection aware swarm behavior for complex missions such as terrain exploration, formations, convoy escorting or creation of a mobile ad hoc network in dynamic disaster areas under realistic environmental conditions. The missions can be combined in any manner and the target extraction of the high-level commands for each agent is done implicitly. To validate the developed behavior, the independent software control kernel is able to be used on real robots as well.

Guided navigation from multiple viewpoints using qualitative spatial reasoning

ABSTRACT Navigation is an essential ability for mobile agents to be completely autonomous and able to perform complex actions. However, the problem of navigation for agents with limited (or no) perception of the world, or devoid of a fully defined motion model, has received little attention from research in AI and Robotics. One way to tackle this problem is to use guided navigation, in which other autonomous agents, endowed with perception, can combine their distinct viewpoints to infer the localization and the appropriate commands to guide a sensory deprived agent through a particular path. Due to the limited knowledge about the physical and perceptual characteristics of the guided agent, this task should be conducted on a level of abstraction allowing the use of a generic motion model, and high-level commands, that can be applied by any type of autonomous agents, including humans. The main task considered in this work is, given a group of autonomous agents perceiving their common environment with their independent, egocentric and local vision sensors, the development and evaluation of algorithms capable of producing a set of high-level commands (involving qualitative directions: e.g. move left, go straight ahead) capable of guiding a sensory deprived robot to a goal location. In order to accomplish this, the present paper assumes relations from the qualitative spatial reasoning formalism called StarVars, whose inference method is also used to build a model of the domain. This paper presents two qualitative-probabilistic algorithms for guided navigation using a particle filter and qualitative spatial relations. In the first algorithm, the particle filter is run upon a qualitative representation of the domain, whereas the second algorithm transforms the numerical output of a standard particle filter into qualitative relations to guide a sensory deprived robot. The proposed methods were evaluated with experiments carried out on a 2D humanoid robot simulator. A proof of concept executing the algorithms on a group of real humanoid robots is also presented. The results obtained demonstrate the success of the guided navigation models proposed in this work.

ATFuzzer

Application processors of modern smartphones use the AT interface for issuing high-level commands (or AT-commands) to the baseband processor for performing cellular network operations (e.g., placing a phone call). Vulnerabilities in this interface can be leveraged by malicious USB or Bluetooth peripherals to launch pernicious attacks. In this article, we propose ATFuzzer, which uses a grammar-guided evolutionary fuzzing approach that mutates production rules of the AT-command grammar instead of concrete AT commands to evaluate the correctness and robustness of the AT-command execution process. To automate each step of the analysis pipeline, ATFuzzer first takes as input the 3GPP and other vendor-specific standard documents and, following several heuristics, automatically extracts the seed AT command grammars for the fuzzer. ATFuzzer uses the seed to generate both valid and invalid grammars, following our cross-over and mutation strategies to evaluate both the integrity and execution of AT-commands. Empirical evaluation of ATFuzzer on 10 Android smartphones from 6 vendors revealed 4 invalid AT command grammars over Bluetooth and 14 over USB with implications ranging from DoS, downgrade of cellular protocol version, to severe privacy leaks. The vulnerabilities along with the invalid AT-command grammars were responsibly disclosed to affected vendors and assigned CVE’s.

A dual-arm mobile robot system performing assistive tasks operated via P300-based brain computer interface

PurposeIn the past decade, more than 700 million people are affected by some kind of disability or handicap. In this context, the research interest in assistive robotics is growing up. For people with mobility impairments, daily life operations, as dressing or feeding, require the assistance of dedicated people; thus, the use of devices providing independent mobility can have a large impact on improving their life quality. The purpose of this paper is to present the development of a robotic system aimed at assisting people with this kind of severe motion disabilities by providing a certain level of autonomy.Design/methodology/approachThe system is based on a hierarchical architecture where, at the top level, the user generates simple and high-level commands by resorting to a graphical user interface operated via a P300-based brain computer interface. These commands are ultimately converted into joint and Cartesian space tasks for the robotic system that are then handled by the robot motion control algorithm resorting to a set-based task priority inverse kinematic strategy. The overall architecture is realized by integrating control and perception software modules developed in the robots and systems environment with the BCI2000 framework, used to operate the brain–computer interfaces (BCI) device.FindingsThe effectiveness of the proposed architecture is validated through experiments where a user generates commands, via an Emotiv Epoc+ BCI, to perform assistive tasks that are executed by a Kinova MOVO robot, i.e. an omnidirectional mobile robotic platform equipped with two lightweight seven degrees of freedoms manipulators.Originality/valueThe P300 paradigm has been successfully integrated with a control architecture that allows us to command a complex robotic system to perform daily life operations. The user defines high-level commands via the BCI, letting all the low-level tasks, for example, safety-related tasks, to be handled by the system in a completely autonomous manner.

An elbow exoskeleton for haptic feedback made with a direct drive hobby motor

A direct drive motor is one of the simplest mechanisms that can be used to move a mechanical joint. In particular, a brushless direct current (BLDC) motor with no gearing produces a low parasitic torque due to its backdrivability and low inertia, which is ideal for some applications such as wearable systems. While capable of operating with a higher power density than brushed motors, BLDC motors require accurate position feedback to be controlled via vector control at slow speeds. The MotorWare™ library from Texas Instruments (TI), which is designed to run with a C2000 microcontroller, is written to run BLDCs. However, the code was written to run the motor continuously with an incremental encoder and requires further engineering to be used at low speeds such as in an exoskeleton. In this paper, we present the design of an elbow exoskeleton that can be used for haptic feedback. We provide instructions to build the exoskeleton hardware, custom code to modify software provided by TI so that a motor can provide a controlled torque at low speeds, code to enable the microcontroller to communicate with a computer for high-level commands and data storage, and also provide an overview of how alternate motors could be used with this software setup.

ADOpy: a python package for adaptive design optimization.

Experimental design is fundamental to research, but formal methods to identify good designs are lacking. Advances in Bayesian statistics and machine learning offer algorithm-based ways to identify good experimental designs. Adaptive design optimization (ADO; Cavagnaro, Myung, Pitt, & Kujala, 2010; Myung, Cavagnaro, & Pitt, 2013) is one such method. It works by maximizing the informativeness and efficiency of data collection, thereby improving inference. ADO is a general-purpose method for conducting adaptive experiments on the fly and can lead to rapid accumulation of information about the phenomenon of interest with the fewest number of trials. The nontrivial technical skills required to use ADO have been a barrier to its wider adoption. To increase its accessibility to experimentalists at large, we introduce an open-source Python package, ADOpy, that implements ADO for optimizing experimental design. The package, available on GitHub, is written using high-level modular-based commands such that users do not have to understand the computational details of the ADO algorithm. In this paper, we first provide a tutorial introduction to ADOpy and ADO itself, and then illustrate its use in three walk-through examples: psychometric function estimation, delay discounting, and risky choice. Simulation data are also provided to demonstrate how ADO designs compare with other designs (random, staircase).

A surface metric and software toolbox for EEG electrode grids in the macaque

BackgroundThe past years have seen increased appreciation of electroencephalographic (EEG) recordings in non-human primates (NHP) as a tool for translational research. In humans, even large EEG electrode grids can easily and quickly be placed on standardized positions using commercially available EEG caps. In the NHP, the identification of standardized EEG electrode positions is more complicated and time-consuming. New methodHere we introduce a surface metric and software package (NHP1020) that automates the planning of large, approximately evenly spaced electrode grids for EEG recordings in the NHP. ResultsBased on one CT and one MRI image as well as two intracranial markers, the NHP1020 software defines electrode positions on the brain surface using a surface-based spherical metric similar to the one used by the international 10-20 system. Standardized electrode grids can be shared, imported or defined with few high-level commands. Existing methodsNHP EEG electrodes can be placed relative to extracranial markers and measurements or relative to underlying neural structures of interest. Both approaches are time-consuming and require manual intervention. Furthermore, the use of extracranial markers in this species may be more problematic than in humans, because cranial muscles and ridges are larger and keep maturing long into adulthood. ConclusionThe presented surface metric and the NHP1020 toolbox provide fast and automated identification of entire electrode grids in the non-human primate based on a two-dimensional metric on the brain surface.

Multi-Rate Control Design Leveraging Control Barrier Functions and Model Predictive Control Policies

In this letter we present a multi-rate control architecture for safety critical systems. We consider a high level planner and a low level controller which operate at different frequencies. This multi-rate behavior is described by a piecewise nonlinear model which evolves on a continuous and a discrete level. First, we present sufficient conditions which guarantee recursive constraint satisfaction for the closed-loop system. Afterwards, we propose a control design methodology which leverages Control Barrier Functions (CBFs) for low level control and Model Predictive Control (MPC) policies for high level planning. The control barrier function is designed using the full nonlinear dynamical model and the MPC is based on a simplified planning model. When the nonlinear system is control affine and the high level planning model is linear, the control actions are computed by solving convex optimization problems at each level of the hierarchy. Finally, we show the effectiveness of the proposed strategy on a simulation example, where the low level control action is updated at a higher frequency than the high level command.

BCI-Controlled Assistive Manipulator: Developed Architecture and Experimental Results

In this article, we present a control architecture for a robotic manipulator finally aimed at helping people with severe motion disabilities in performing daily life operations, such as manipulating objects or drinking. The proposed solution allows the user to focus the attention only on the operational tasks, while all the safety-related issues are automatically handled by the developed control architecture. The user commands the manipulator sending high-level commands via a P300-based brain–computer interface. A perception module, relying on an RGB-D sensor, continuously detects and localizes the objects in the scene, tracking the position of the user and monitoring the environment for identifying static and dynamic obstacles, e.g., a person entering in the scene. A lightweight manipulator is controlled relying on a task-priority inverse kinematics algorithm that handles task hierarchies composed of equality-based and set-based tasks, including obstacle avoidance and joint mechanical limits. This article describes the overall architecture and the integration of the implemented software modules, that are based on common frameworks and software libraries, such as the robotic operating system (ROS), BCI2000, OpenCV, and PCL. The experimental results on a use case scenario using a Kinova 7DOFs Jaco2 robot helping a user to perform drinking and manipulation tasks show the effectiveness of the developed control architecture.

Secure VM management with strong user binding in semi-trusted clouds

In Infrastructure-as-a-Service (IaaS) clouds, remote users access provided virtual machines (VMs) via the management server. The management server is managed by cloud operators, but not all the cloud operators are trusted in semi-trusted clouds. They can execute arbitrary management commands to users’ VMs and redirect users’ commands to malicious VMs. We call the latter attack the VM redirection attack. The root cause is that the binding of remote users to their VMs is weak. In other words, it is difficult to enforce the execution of only users’ management commands to their VMs. In this paper, we propose UVBond for strongly binding users to their VMs to address this issue. UVBond boots user’s VM by decrypting its encrypted disk inside the trusted hypervisor. Then it issues a VM descriptor to securely identify that VM. To bridge the semantic gap between high-level management commands and low-level hypercalls, UVBond uses hypercall automata, which accept the sequences of hypercalls issued by commands. We have implemented UVBond in Xen and created hypercall automata for various management commands. Using UVBond, we confirmed that a VM descriptor and hypercall automata prevented insider attacks and that the overhead was not large in remote VM management.

An embodied, platform-invariant architecture for connecting high-level spatial commands to platform articulation

In contexts such as teleoperation, robot reprogramming, human–robot-interaction, and neural prosthetics, conveying movement commands to a robotic platform is often a limiting factor. Currently, many applications rely on joint-angle-by-joint-angle prescriptions. This inherently requires a large number of parameters to be specified by the user that scales with the number of degrees of freedom on a platform, creating high bandwidth requirements for interfaces. This paper presents an efficient representation of high-level, spatial commands that specifies many joint angles with relatively few parameters based on a spatial architecture that is judged favorably by human viewers. In particular, a general method for labeling connected platform linkages, generating a databank of user-specified poses, and mapping between high-level spatial commands and specific platform static configurations are presented. Thus, this architecture is “platform-invariant” where the same high-level, spatial command can be executed on any platform. This has the advantage that our commands have meaning for human movers as well. In order to achieve this, we draw inspiration from Laban/Bartenieff Movement Studies, an embodied taxonomy for movement description. The architecture is demonstrated through implementation on 26 spatial directions for a Rethink Robotics Baxter, an Aldebaran NAO, and a KUKA youBot. User studies are conducted to validate the claims of the proposed framework.