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Controlling biomimetic underwater robots in unknown flow fields remains a challenge due to the strong nonlinearity of the fluid. This article investigates the attitude holding task of a robotic fish swimming in reality. Such a typical sensing-based control task requires the fish to keep a desired angle of attack in an unknown and even varied incoming flow. To this end, we propose a learning-based approach by using a deep neural network directly maps the raw data of sensors equipped on the robot to the continuous control signals in an end-to-end manner. First, based on experimental data of the physical robot, we construct a data-driven simulation environment including three modules of dynamic, sensor, and control. The dynamic and sensor modules are established to model the dynamics of the fish and to generate its sensors’ data, based on which a deep reinforcement learning (DRL) algorithm in the control module is trained to get a control policy. Then, we directly deploy the trained policy to a physical robotic fish for attitude holding task. Experimental results demonstrate the robustness and effectiveness of the DRL policy and, thus, verify the success of our approach to achieving sim-to-real transfer. [ABSTRACT FROM AUTHOR]

This article introduces the analysis and control of a meso-scale two degree-of-freedom robotic endoscopic tool body for minimally invasive surgeries. The design of the robotic tool uses two types of a tendon-driven joint known as a bending flexure joint that allows us to control each degree-of-freedom by minimizing interjoint coupling by design. Pure kinematic modeling and control for these robots may not provide precise control performance due to kinematic uncertainties arising from tendon elongation, tendon slacking, gear backlash, etc. We propose a static model for each of the joints of the robotic tool that avoids several of these problems. Depending on the direction of tendon tension application, the proximal joint displays considerable hysteresis due to the superelastic material characteristics and this is included in our static model. The statics of a highly compliant distal joint is also modeled and validated using finite element analysis and experimental data. Using these models, we develop a control system that comprises of a disturbance observer and the proposed static model to provide precise force control and compensate for joint hysteresis. [ABSTRACT FROM AUTHOR]

In this article, we consider the problem of mass transport cloaking using mobile robots. The robots move along a predefined curve that encloses a safe zone and carry sources that collectively counteract a chemical agent released in the environment. The goal is to steer the mass flux around a desired region so that it remains unaffected by the external concentration. We formulate the problem of controlling the robot positions and release rates as a partial differential equation (PDE)-constrained optimization, where the propagation of the chemical is modeled by the advection-diffusion (AD) PDE. We use a neural network (NN) to approximate the solution of the PDE. Particularly, we propose a novel loss function for the NN that utilizes the variational form of the AD-PDE and allows us to reformulate the planning problem as an unsupervised model-based learning problem. Our loss function is discretization-free and highly parallelizable. Unlike passive cloaking methods that use metamaterials to steer the mass flux, our method is the first to use mobile robots to actively control the concentration levels and create safe zones independent of environmental conditions. We demonstrate the performance of our method in simulations. [ABSTRACT FROM AUTHOR]

Controlling a robot astronaut to move in the same way as a human astronaut to realize a wide range of motion in a space station is an important requirement for the robot astronauts that are meant to assist or replace human astronauts. However, a robot astronaut is a nonlinear and strongly coupled multibody dynamic system with multiple degrees of freedom, whose dynamic characteristics are complex. Therefore, implementing a robot astronaut with wide-ranging motion control in a space station is a tremendous challenge for robotic technology. This article presents a wide-ranging stable motion control method for robot astronauts in space stations based on human dynamics. Focusing on the astronauts’ parking motion in a space station, a viscoelastic dynamic humanoid model of parking under microgravity environment was established using a mass–spring–damper system. The model was used as the expected model for stable parking control of a robot astronaut, and the complex dynamic characteristics were mapped into the robot astronaut system to control the stable parking of the robot astronaut in a manner similar to a human astronaut. This provides a critical basis for implementing robots that are capable of steady wide-ranging motion in space stations. The method was verified on a dynamic system of a robot astronaut that was constructed for this research. The experimental results showed that the method is feasible and effective and that it is a highly competitive solution for robot astronauts with human-like moving capabilities in space stations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control method, providing mobile robots with whole-body compliance capabilities, in response to multicontact physical interactions with their environment. The external forces applied to the robot, as well as their localization on its kinematic tree, are measured using a multimodal, self-configuring, and self-calibrating artificial skin. We formulate a compliance control law in Cartesian space, as a set of quadratic optimization problems, solved in parallel for each limb involved in the interaction process. This specific formulation makes it possible to determine the torque commands required to generate the desired reactive behaviors, while taking the robot kinematic and dynamic constraints into account. When a given limb fails to produce the desired compliant behavior, the generalized force residual at the considered contact points is propagated to a parent limb in order to be adequately compensated. Hence, the robot's compliance range can be extended in a both robust and easily adjustable manner. The experiments performed on a dual-arm velocity-controlled mobile manipulator show that our methodology is robust to nullspace interactions and robot physical constraints. [ABSTRACT FROM AUTHOR]

This paper presents a method for designing the gait of a snake robot that moves in a complicated environment. We propose a method for expressing the target form of a snake robot by connecting curve segments whose curvature and torsion are already known. Because the characteristics of each combined shape are clear, we can design the target form intuitively and approximate a snake robot configuration to this form with low computational cost. In addition, we propose two novel gaits for the snake robot as a design example of the proposed method. The first gait is aimed at moving over a flange on a pipe, while the other is the crawler gait aimed at moving over rough terrain. We demonstrated the effectiveness of the two gaits on a pipe and rough terrain in experiments. [ABSTRACT FROM AUTHOR]

We propose a framework to generate static robot configurations satisfying a set of physical and geometrical constraints. This is done by formulating nonlinear constrained optimization problems over non-Euclidean manifolds and solving them. To do so, we present a new sequential quadratic programming solver working natively on general manifolds and propose an interface to easily formulate the problems, with the tedious and error-prone work automated for the user. We also introduce several new types of constraints for having more complex contacts or working on forces/torques. Our approach allows an elegant mathematical description of the constraints and we exemplify it through formulation and computation examples in complex scenarios with humanoid robots. [ABSTRACT FROM AUTHOR]

As the application of probabilistic models in robotic applications increases, a systematic robot control approach considering the effects of uncertainty becomes indispensable. Inspired by human sensorimotor findings, in this paper, we study the stochastic optimal control problem with high-order cost statistics in order to synthesize uncertainty-dependent actions in robotic scenarios with multiple uncertainty sources. We present locally optimal risk-sensitive and cost-cumulant solutions for settings with nonlinear dynamics, multiple additive uncertainty sources, and nonquadratic costs. The influence of each uncertainty source on the cost can be individually parameterized offering additional flexibility in the control design. We further analyze the case in which the static uncertain parameters are involved. The simulations of several linear and nonlinear settings with nonquadratic costs and an experiment on a real robotic platform validate our approach and illustrate its peculiarities. [ABSTRACT FROM AUTHOR]

Grasping objects that are too large to envelop is traditionally achieved using friction that is activated by squeezing. We present a family of shear-activated grippers that can grasp such objects without the need to squeeze. When a shear force is applied to the gecko-inspired material in our grippers, adhesion is turned on; this adhesion in turn results in adhesion-controlled friction, a friction force that depends on adhesion rather than a squeezing normal force. Removal of the shear force eliminates adhesion, allowing easy release of an object. A compliant shear-activated gripper without active sensing and control can use the same light touch to lift objects that are soft, brittle, fragile, light, or very heavy. We present three grippers, the first two designed for curved objects, and the third for nearly any shape. Simple models describe the grasping process, and empirical results verify the models. The grippers are demonstrated on objects with a variety of shapes, materials, sizes, and weights. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed control policy to achieve rendezvous by a set of robots even when some robots in the system do not follow the prescribed policy. These nonconforming robots correspond to faults in the multirobot system, and our control policy is thus a fault-tolerant policy. Each robot has a limited sensing range and is able to directly estimate the state of only those robots within that sensing range, which induces a network topology for the multirobot system. We assume that it is not possible for the fault-free robots to identify the faulty robots, and thus our approach is robust even to undetected faults in the system. The main contribution of this paper is a fault-tolerant distributed control algorithm that is guaranteed to converge to consensus under certain reasonable connectivity conditions. We first present a general algorithm that exploits the notion of a Tverberg partition of a point set in \mathbb R^d, and give a proof of convergence. We then provide three instantiations of this algorithm, based on three different sensing models. For each case, we analyze performance via extensive simulations. The effectiveness and performance of our algorithms on real platforms are demonstrated through experiments on a multirobot testbed. [ABSTRACT FROM PUBLISHER]

Robotics have long sought an actuation technology comparable to or as capable as biological muscle tissue. Natural muscles exhibit a high power-to-weight ratio, inherent compliance and damping, fast action, and a high dynamic range. They also produce joint displacements and forces without the need for gearing or additional hardware. Recently, supercoiled commercially available polymer threads (sewing thread or nylon fishing lines) have been used to create significant mechanical power in a muscle-like form factor. Heating and cooling the polymer threads causes contraction and expansion, which can be utilized for actuation. In this paper, we describe the working principle of supercoiled polymer (SCP) actuation and explore the controllability and properties of these threads. We show that under appropriate environmental conditions, the threads are suitable as a building block for a controllable artificial muscle. We leverage off-the-shelf silver-coated threads to enable rapid electrical heating while the low thermal mass allows for rapid cooling. We utilize both thermal and thermomechanical models for feed-forward and feedback control. The resulting SCP actuator regulates to desired force levels in as little as 28 ms. Together with its inherent stiffness and damping, this is sufficient for a position controller to execute large step movements in under 100 ms. This controllability, high performance, the mechanical properties, and the extremely low material cost are indicative of a viable artificial muscle. [ABSTRACT FROM PUBLISHER]

Designing an actuator system for highly dynamic legged robots has been one of the grand challenges in robotics research. Conventional actuators for manufacturing applications have difficulty satisfying design requirements for high-speed locomotion, such as the need for high torque density and the ability to manage dynamic physical interactions. To address this challenge, this paper suggests a proprioceptive actuation paradigm that enables highly dynamic performance in legged machines. Proprioceptive actuation uses collocated force control at the joints to effectively control contact interactions at the feet under dynamic conditions. Modal analysis of a reduced leg model and dimensional analysis of DC motors address the main principles for implementation of this paradigm. In the realm of legged machines, this paradigm provides a unique combination of high torque density, high-bandwidth force control, and the ability to mitigate impacts through backdrivability. We introduce a new metric named the “impact mitigation factor” (IMF) to quantify backdrivability at impact, which enables design comparison across a wide class of robots. The MIT Cheetah leg is presented, and is shown to have an IMF that is comparable to other quadrupeds with series springs to handle impact. The design enables the Cheetah to control contact forces during dynamic bounding, with contact times down to 85 ms and peak forces over 450 N. The unique capabilities of the MIT Cheetah, achieving impact-robust force-controlled operation in high-speed three-dimensional running and jumping, suggest wider implementation of this holistic actuation approach. [ABSTRACT FROM PUBLISHER]

We present a unified framework for grasp planning and in-hand grasp adaptation using visual, tactile, and proprioceptive feedback. The main objective of the proposed framework is to enable fingertip grasping by addressing problems of changed weight of the object, slippage, and external disturbances. For this purpose we introduce the Hierarchical Fingertip Space as a representation enabling optimization for both efficient grasp synthesis and online finger gaiting. Grasp synthesis is followed by a grasp adaptation step that consists of both grasp force adaptation through impedance control and regrasping/finger gaiting when the former is not sufficient. Experimental evaluation is conducted on an Allegro hand mounted on a Kuka LWR arm. [ABSTRACT FROM PUBLISHER]

Partially observable Markov decision processes (POMDPs) are used in many robotic task classes from soccer to household chores. Determining an approximately optimal action policy for POMDPs is PSPACE-complete, and the exponential growth of computation time prohibits solving large tasks. This paper describes two techniques to extend the range of robotic tasks that can be solved using a POMDP. Our first technique reduces the motion constraints of a robot and, then, uses state-of-the-art robotic motion planning techniques to respect the true motion constraints at runtime. We then propose a novel task decomposition that can be applied to some indoor robotic tasks. This decomposition transforms a long time horizon task into a set of shorter tasks. We empirically demonstrate the performance gain provided by these two techniques through simulated execution in a variety of environments. Comparing a direct formulation of a POMDP to solving our proposed reductions, we conclude that the techniques proposed in this paper can provide significant enhancement to current POMDP solution techniques, extending the POMDP instances that can be solved to include large continuous-state robotic tasks. [ABSTRACT FROM PUBLISHER]

The use of formal methods for synthesis has recently enabled the automated construction of verifiable high-level robot control. Most approaches use a discrete abstraction of the underlying continuous domain, and make assumptions about the physical execution of actions given a discrete implementation; examples include when actions will complete relative to each other, and possible changes in the robot's environment while it is performing various actions. Relaxing these assumptions give rise to a number of challenges during the continuous implementation of automatically synthesized hybrid controllers. This paper presents several distinct timing semantics for controller synthesis, and compares them with respect to the assumptions they make on the execution of actions. It includes a discussion of when each set of assumptions is reasonable, and the computational tradeoffs inherent in relaxing them at synthesis time. [ABSTRACT FROM PUBLISHER]

A small but interesting result of Brockett is extended to the Euclidean group $SE(3)$ and is illustrated by several examples. The result concerns the explicit solution of an optimal control problem on Lie groups, where the control belongs to a Lie triple system in the Lie algebra. The extension allows for an objective function based on an indefinite quadratic form. Applying the result requires explicit knowledge of the Lie triple systems of the Lie algebra $se(3)$. Hence, a complete classification of the Lie triple systems of this Lie algebra is derived. Examples are considered for optimal trajectories in three cases. The first case concerns cars moving in the plane. The second looks at motions that rigidly follow the Bishop frame to a space curve. The final example does not have a particular name as it does not seem to have been studied before. The appendix gives a brief introduction to Screw theory. This is essentially the study of the Lie algebra $se(3)$. [ABSTRACT FROM PUBLISHER]

Reinforcement learning (RL) can be a tool for designing policies and controllers for robotic systems. However, the cost of real-world samples remains prohibitive as many RL algorithms require a large number of samples before learning useful policies. Simulators are one way to decrease the number of required real-world samples, but imperfect models make deciding when and how to trust samples from a simulator difficult. We present a framework for efficient RL in a scenario where multiple simulators of a target task are available, each with varying levels of fidelity. The framework is designed to limit the number of samples used in each successively higher-fidelity/cost simulator by allowing a learning agent to choose to run trajectories at the lowest level simulator that will still provide it with useful information. Theoretical proofs of the framework's sample complexity are given and empirical results are demonstrated on a remote-controlled car with multiple simulators. The approach enables RL algorithms to find near-optimal policies in a physical robot domain with fewer expensive real-world samples than previous transfer approaches or learning without simulators. [ABSTRACT FROM PUBLISHER]

This paper proposes a framework called nonlinear robust internal-loop compensator that enables us to bring nonlinear \mathcal H_\infty optimality to robot controllers in a unified and simple way. Using the framework, a controller designed for the nominal plant can achieve additional robustness by simply adding PID-type auxiliary input to the original control law. Robust performance is guaranteed by the nonlinear \mathcal H_\infty optimality and robust stability is guaranteed by proving the extended disturbance input-to-state stability. Moreover, the framework preserves the passivity property of the original controller. Finally, the performance bound can be predicted and leads to the gain tuning rules. By virtue of the tuning rules, the performance can be tuned using only a single variable. The proposed method was validated through the simulations and experiments. [ABSTRACT FROM PUBLISHER]

We propose local magnetic actuation (LMA) as an approach to robotic actuation for surgical instruments. An LMA actuation unit consists of a pair of diametrically magnetized single-dipole cylindrical magnets, working as magnetic gears across the abdominal wall. In this study, we developed a dynamic model for an LMA actuation unit by extending the theory proposed for coaxial magnetic gears. The dynamic model was used for closed-loop control, and two alternative strategies—using either the angular velocity at the motor or at the load as feedback parameter—were compared. The amount of mechanical power that can be transferred across the abdominal wall at different intermagnetic distances was also investigated. The proposed dynamic model presented a relative error below 7.5% in estimating the load torque from the system parameters. Both the strategies proposed for closed-loop control were effective in regulating the load speed with a relative error below 2% of the desired steady-state value. However, the load-side closed-loop control approach was more precise and allowed the system to transmit larger values of torque, showing, at the same time, less dependence from the angular velocity. In particular, an average value of 1.5 mN$\cdot$m can be transferred at 7 cm, increasing up to 13.5 mN$\cdot$m as the separation distance is reduced down to 2 cm. Given the constraints in diameter and volume for a surgical instrument, the proposed approach allows for transferring a larger amount of mechanical power than what would be possible to achieve by embedding commercial dc motors. [ABSTRACT FROM PUBLISHER]

Intuitive and effective physical assistance is an essential requirement for robots sharing their workspace with humans. Application domains reach from manufacturing and service robotics via rehabilitation and mobility aids to education and training. In this context, assistance based on human behavior anticipation has shown superior performance in terms of human effort minimization. However, when a robot's expectations mismatch a human intentions, undesired interaction forces appear incurring safety risks and discomfort. Human behavior prediction is, therefore, a crucial issue: It enables effective anticipation but potentially produces disagreements when prediction errors occur. In this paper, we present a novel control scheme for anticipatory haptic assistance where robot behavior adapts to prediction uncertainty. Following a data-driven stochastic modeling approach, robot assistance is synthesized solving a risk-sensitive optimal control problem, where the cost function and plant dynamics are affected by model uncertainty. The proposed approach is objectively and subjectively evaluated in an experiment with human users. Results indicate that our method outperforms other assistive control approaches in terms of perceived helpfulness and human effort minimization. [ABSTRACT FROM PUBLISHER]

Helical microswimmers that are capable of propulsion at low Reynolds numbers have great potential for numerous applications. Several kinds of artificial magnetic-actuated helical microswimmers have been designed by researchers. However, they are primarily open-loop controlled. This paper aims to investigate methods of closed-loop control of a magnetic-actuated helical swimmer at low Reynolds number by using visual feedback. For many in-vitro applications, helical swimmers should pass through a defined path, for example along channels with no prerequisite on the velocity profile along the path. Therefore, the main objective of this paper is to achieve a velocity-independent planar path following task. Since the planar path following is based on 3-D steering control of the helical swimmer, a 3-D pose estimation of a helical swimmer is introduced based on the real-time visual tracking with a stereo vision system. The contribution of this paper is in two parts: The 3-D steering of a helical swimmer is demonstrated by visual servo control; and the path following of a straight line with visual servo control is achieved, then compared with open-loop control. We further expect that with this visual servo control method, the helical swimmers will be able to follow reference paths at the microscale. [ABSTRACT FROM PUBLISHER]

Sliding probe methods are designed for the in situ characterization of electrical properties of individual 1-D nanostructures. The key to achieving a high resolution is to keep the contact resistance constant by controlling the contact force and area between the specimen and the sliding probe. We have developed several techniques and tools including differential sliding, flexible probes, and specimen-shape-adaptable probes using nanorobotic manipulation. Compared with conventional methods, these sliding probe methods allow in situ characterization with a higher resolution than conventional methods. Furthermore, they are superior for local property characterization, which is of particular interest for heterostructured nanomaterials and defect detection. [ABSTRACT FROM PUBLISHER]

This paper presents “1STAR,” which is the first robot that is driven by a single actuator but can be directly commanded to move straight or turn clockwise or counterclockwise. The legged robot relies on a novel actuation gait, which exploits the compliance disparity between alternate stance tripods, to generate rotation by continuously accelerating and decelerating the legs. The direction of turning depends on the configuration of the legs—tripod left or right—and the timing of the acceleration and deceleration. Alternating leg acceleration in successive steps allows for continuous rotation in the desired direction. The turning radius can be varied by changing the timing of the leg acceleration and deceleration without changing the cycle frequency and linear speed. A simplified kinematic motion model of a robot is presented, and a dynamic simulation is performed to analyze the behavior and optimize robot parameters. The locomotion gait is verified experimentally using our newly designed “1STAR” robot. [ABSTRACT FROM PUBLISHER]

One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing, and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection in which the point of view of a mobile depth camera is controlled. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. Then, a sequence of views, which balances the amount of energy used to move the sensor with the chance of identifying the correct hypothesis, is planned. We formulate an active hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate partially observable Markov decision process algorithm. The validity of our approach is verified through simulation and real-world experiments with the PR2 robot. The results suggest that the approach outperforms the widely used greedy viewpoint selection and provides a significant improvement over static object detection. [ABSTRACT FROM AUTHOR]

This paper proposes an interval-based method for the validation of reliable and robust navigation rules for mobile robots. The main idea is to show that for all feasible perturbations, there exists a safe subset of the state space such that the system cannot escape. The methodology is illustrated on the line-following problem of a sailboat and then validated on an actual experiment where an actual sailboat robot, which is named Vaimos, sails autonomously from Brest to Douarnenez (i.e., more than 100 km). [ABSTRACT FROM AUTHOR]

In this paper, we propose a control method for stable grasp of dual-fingered robots with rigid hemispherical fingertips. The main contribution of the proposed approach is to lower the probability of the object slipping out of the fingertips as optimizing the angle of the grasping force. In addition, since the controller is allowed to use the tactile sensor, the shape of an object is not limited, as long as the contact point is positioned on the vicinity of smooth curvatures. We prove the stability analysis of the proposed controller via the Lyapunov direct method. Finally, we present simulation results to validate the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

We present a computational framework for automatic synthesis of control and communication strategies for a robotic team from task specifications that are given as regular expressions about servicing requests in an environment. We assume that the location of the requests in the environment and the robot capacities and cooperation requirements to service the requests are known. Our approach is based on two main ideas. First, we extend recent results from formal synthesis of distributed systems to check for the distributability of the task specification and to generate local specifications, while accounting for the service and communication capabilities of the robots. Second, by using a technique that is inspired by linear temporal logic model checking, we generate individual control and communication strategies. We illustrate the method with experimental results in our robotic urban-like environment. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the use of a spherical-projection model for visual servoing from three points. We propose a new set of six features to control a 6-degree-of-freedom (DOF) robotic system with good decoupling properties. The first part of the set consists of three invariants to camera rotations. These invariants are built using the Cartesian distances between the spherical projections of the three points. The second part of the set corresponds to the angle-axis representation of a rotation matrix measured from the image of two points. Regarding the theoretical comparison with the classical perspective coordinates of points, the new set does not present more singularities. In addition, using the new set inside its nonsingular domain, a classical control law is proven to be optimal for pure rotational motions. The theoretical results and the robustness to points range errors of the new control scheme are validated through simulations and experiments on a 6-DOF robot arm. [ABSTRACT FROM PUBLISHER]

Driving mobile robots to precise locations is of recognized interest, and using vision sensors in this context supplies many advantages. We propose a novel control law based on sliding-mode theory in order to drive mobile robots to a target location, which is specified by a previously acquired reference image. The control scheme exploits the piecewise epipolar geometry of three views on the basis of image-based visual servoing, in such a way that no 3-D scene information is required. The contribution of the paper is a new control law that achieves convergence to the target with no auxiliary images and without changing to any approach other than epipolar-based control. Additionally, the use of sliding-mode control deals with singularities, thus allowing the robot to move directly toward the target as well as avoiding the need of a precise camera calibration. The effectiveness of our approach is tested with simulations and real-world experiments. [ABSTRACT FROM AUTHOR]

For a compass-like biped robot, the problem of achieving stable walking on both ideal inclined surfaces and complex environments is studied. For the case of walking on ideal inclined surfaces, a feedback-control law is obtained via feedback-linearization techniques so that the trajectory of the robot converges to a desired passive walking gait. Simulations show that it leads to a larger basin of attraction. For the case of walking in complex environments, a scheme is developed with regulable step length and walking speed. Different reference trajectories are constructed for different steps, and corresponding feedback-control laws are updated at the beginning of each step. Then, it is shown that the errors that measure the difference of the response trajectory and the reference one asymptotically converge to zero. An example of walking over stairs is given to numerically verify and demonstrate our approach. [ABSTRACT FROM AUTHOR]

We propose nonholonomic passive decomposition, which enables us to decompose the Lagrange–D’Alembert dynamics of multiple (or a single) nonholonomic mechanical systems with a formation-specifying (holonomic) map h into 1) shape system, describing the dynamics of h(q) (i.e., formation aspect), where q \in \Re^n is the systems’ configuration; 2) locked system, describing the systems’ motion on the level set of h with the formation aspect h(q) being fixed (i.e., maneuver aspect); 3) quotient system, whose nonzero motion perturbs both the formation and maneuver aspects simultaneously; and 4) energetically conservative inertia-induced coupling among them. All the locked, shape, and quotient systems individually inherit Lagrangian dynamics-like structure and passivity, which facilitate their control design/analysis. Canceling out the coupling, regulating the quotient system, and controlling the locked and shape systems individually, we can drive the formation and maneuver aspects simultaneously and separately. Notions of formation/maneuver decoupled controllability are introduced to address limitations imposed by the nonholonomic constraint, along with passivity-based formation/maneuver control design examples. Numerical simulations are performed to illustrate the theory. Extension to kinematic nonholonomic systems is also presented. [ABSTRACT FROM AUTHOR]

Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation, disturbances, and modeling errors, as well as stochastic mode transitions such as component failures. Chance-constrained control takes into account uncertainty to ensure that the probability of failure, due to collision with obstacles, for example, is below a given threshold. In this paper, we present a novel method for chance-constrained predictive stochastic control of dynamic systems. The method approximates the distribution of the system state using a finite number of particles. By expressing these particles in terms of the control variables, we are able to approximate the original stochastic control problem as a deterministic one; furthermore, the approximation becomes exact as the number of particles tends to infinity. This method applies to arbitrary noise distributions, and for systems with linear or jump Markov linear dynamics, we show that the approximate problem can be solved using efficient mixed-integer linear-programming techniques. We also introduce an important weighting extension that enables the method to deal with low-probability mode transitions such as failures. We demonstrate in simulation that the new method is able to control an aircraft in turbulence and can control a ground vehicle while being robust to brake failures. [ABSTRACT FROM AUTHOR]

To efficiently solve challenges related to motion-planning problems with dynamics, this paper proposes treating motion planning not just as a search problem in a continuous space but as a search problem in a hybrid space consisting of discrete and continuous components. A multilayered framework is presented which combines discrete search and sampling-based motion planning. This framework is called synergistic combination of layers of planning (SyCLoP) hereafter. Discrete search uses a workspace decomposition to compute leads, i.e., sequences of regions in the neighborhood that guide sampling-based motion planning during the state-space exploration. In return, information gathered by motion planning, such as progress made, is fed back to the discrete search. This combination allows SyCLoP to identify new directions to lead the exploration toward the goal, making it possible to efficiently find solutions, even when other planners get stuck. Simulation experiments with dynamical models of ground and flying vehicles demonstrate that the combination of discrete search and motion planning in SyCLoP offers significant advantages. In fact, speedups of up to two orders of magnitude were obtained for all the sampling-based motion planners used as the continuous layer of SyCLoP. [ABSTRACT FROM AUTHOR]

As robots come closer to humans, an efficient human-robot-control interface is an utmost necessity. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. A mathematical model is trained to decode upper limb motion from EMG recordings, using a dimensionality-reduction technique that represents muscle synergies and motion primitives. It is shown that a 2-D embedding of muscle activations can be decoded to a continuous profile of arm motion representation in the 3-D Cartesian space, embedded in a 2-D space. The system is used for the continuous control of a robot arm, using only EMG signals from the upper limb. The accuracy of the method is assessed through real-time experiments, including random arm motions. [ABSTRACT FROM AUTHOR]

In this paper, a physically interactive control scheme is developed for a manipulator robot arm. The human touches the robot and applies force in order to make it behave as he/she likes. The communication between the robot and the human is maintained by a physical contact with no sensors. The intent of the human is estimated by observing the change in control effort. The robot receives the estimated human intent and updates its position reference accordingly. The developed method uses the principle of conservation of zero momentum for position-controlled systems. A switching scheme is developed that goes between the modes of pure impedance control with a fixed-position reference and interactive control under human intent. The switching mechanism uses neither a physical switch nor a sensor; it observes the human intent and puts the robot into interactive mode, if there is any. When the human intent disappears, the robot goes into the pure-impedance-control mode, thus stabilizing in the left position. [ABSTRACT FROM AUTHOR]

A key problem in robot grasping is that of positioning the manipulator contacts so that an object can be grasped. In unstructured environments, contact positions are typically planned based on range or visual measurements that are used to reconstruct object geometry. However, because it is difficult to measure the complete object geometry precisely in common grasp scenarios, it is useful to employ additional techniques to adjust or refine the grasp using local information only. In particular, grasp control techniques can be used to improve a grasp by adjusting the contact configuration after making initial contact with an object by using measurements of local object geometry at the contacts. This paper proposes three variations on null-space grasp control: an approach that combines multiple grasp objectives to improve a grasp. Two of these variations are theoretically demonstrated to converge to force-closure configurations for arbitrary convex objects when grasping with two contacts. All variations are found to converge in simulation. Robot-grasping experiments are reported that show the approach to be useful in practice. [ABSTRACT FROM AUTHOR]

Starting from human "sense of touch," this paper reviews the state of tactile sensing in robotics. The physiology, coding, and transferring tactile data and perceptual importance of the "sense of touch" in humans are discussed. Following this, a number of design hints derived for robotic tactile sensing are presented. Various technologies and transduction methods used to improve the touch sense capability of robots are presented. Tactile sensing, focused to fingertips and hands until past decade or so, has now been extended to whole body, even though many issues remain open. Trend and methods to develop tactile sensing arrays for various body sites are presented. Finally, various system issues that keep tactile sensing away from widespread utility are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a connection mechanism for autonomous self-assembly in mobile robots. Using this connection mechanism, mobile robots can be autonomously connected and disconnected. The purpose of self-assembly in mobile robotics is to add a new capability to mobile robots, thus, improving their performance to best fit the terrain conditions. Construction of a reconnectable joint is of primary concern in such systems. In this paper, first the geometric conditions and force equations of a general docking mechanism are studied. Then, we discuss the design details of our connection mechanism and present some experimental results that show that the proposed mechanism overcomes significant alignment errors and is considerably power efficient. [ABSTRACT FROM AUTHOR]

This paper introduces for the first time a metamorphic palm and presents a novel multifingered hand, known as Matahand, with a foldable and flexible palm that makes the hand adaptable and reconfigurable. The orientation and pose of the new robotic hand are enhanced by additional motion of the palm, and workspace of the robotic fingers is complemented with the palm motion. To analyze this enhanced workspace, this paper introduces finger-orientation planes to relate the finger orientation to palm various configurations. Normals of these orientation planes are used to construct a Gauss map. Adding an additional dimension, a 4-D ruled surface is generated to illustrate orientation and pose change of the hand, and an orientation-pose manifold is developed from the orientation-pose ruled surface. The orientation and workspace analysis are further developed by introducing a triangular palm workspace that evolves into a helical surface and is further developed into a 4-D representation. Simulations are presented to illustrate the characteristics of this new dexterous hand. [ABSTRACT FROM AUTHOR]

We present NIMS3D, a novel 3-D cabled robot for actuated sensing applications. We provide a brief overview of the main hardware components. Next, we describe installation procedures, including novel calibration methods, that enable rapid in-field deployability for nonexpert end users, and provide simulations and experimental results to highlight their effectiveness. Kinematic and dynamic analysis of the system are provided, followed by a description of control methods. We provide experimental results that illustrate tracking of linear and nonlinear paths by NIMS3D. Thereafter, we briefly present an example of an actuated sensing task performed by the system. Finally, we describe methods of improving energy efficiency by leveraging nonlinear trajectories and energy-optimal tension distributions. Experimental and simulated results show that energy efficiency can be improved significantly by using optimized parabolic trajectories. Furthermore, we provide simulation results that demonstrate improved efficiency enabled by optimal, least norm tension distributions. [ABSTRACT FROM AUTHOR]

This note presents a unified approach to nominal control generation and return map parameterization for yo-yo playing. The approach is based on choosing an intermediate state of the yo-yo as the parameter and formulating an optimization problem with appropriate boundary conditions that incorporate the corresponding parameter. By solving the optimization problems, both the return map and the nominal optimal control for the robot can be obtained. The return map is naturally parameterized with the intermediate state, which can be treated as a virtual control. The return map facilitates the application of standard analysis and design methods for discrete-time control systems. The approach is promising as it might be extended to control other cyclic dynamic systems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate comprehensive issues of modularized robot systems and present architecture for effective development of personal robots. The proposed architecture includes the interface and interaction methodology of the hardware modules and the software among the modules. Based on the architecture, we present a fully modularized personal robot dynamically reconfigurable personal robot I. Its hardware modules are easily added to or removed from the original system, and also the distributed software functions of the modules are rearrangeable accordingly. We focus on how to effectively build up the robot, and discuss the dynamically reconfigurable features of it for evaluating the proposed idea and approach. [ABSTRACT FROM AUTHOR]

One important category of parallel mechanisms is the translational parallel mechanism (TPM). This paper focuses on the structural shakiness of the nonoverconstrained TPM. Such a structural shakiness is due to the unavoidable lack of rigidity of the real bodies, which leads to uncheckable orientation changes of the moving platform of a TPM. Using algebraic properties of displacement subsets and, especially, displacement Lie subgroup theory, we show that the structural shakiness of the nonoverconstrained TPM is inherently determined by the structural type of its limb chains. A structural shakiness index (SSI) for a nonoverconstrained TPM is introduced. When the set of feasible displacements of the end body of a 5-degree-of-freedom (DOFs) limb chain contains two infinities of parallel axes of rotation, we have SSI = 2; when the displacement set of the end body of a 5-DOF limb chain contains only one infinity of parallel axes of rotation, we have SSI = 1. It is proven that nonoverconstained TPMs constructed with limb chains with SSI = 1 are much less prone to orientation changes than those constructed with limb chains with SSI = 2. Based on the SSI, we enumerate limb kinematic chains and construct 21 nonoverconstrained TPMs with less shakiness. [ABSTRACT FROM AUTHOR]

Stability analysis of decentralized control mechanisms for networked coordinating systems has generally focused on specific controller implementations, such as nearest-neighbor and other types of proximity graph control laws. This approach often misses the need for the addition of other control structures to improve global characteristics of the network. An example of such a situation is the use of a Gabriel graph, which is essentially a nearest-neighbor rule modified to ensure global connectivity of the network if the agents are pairwise connected through their sensor inputs. We present a method of ensuring provable stability of decentralized switching systems by employing a hysteresis rule that uses a zero-sum consensus algorithm. We demonstrate the application of this result to several special cases, including nearest-neighbor control laws, Gabriel graph rules, diffuse target tracking, and hierarchical heterogeneous systems. [ABSTRACT FROM AUTHOR]

The admittance of a manipulator can be used to improve robotic assembly. If properly selected, the admittance will regulate a contact force and use it to guide the parts to proper positioning. In previous work, procedures for selecting the appropriate admittance for single principal contact (PC) cases were identified. This paper extends this research for some of the two PC cases—those for which each contact occurs at a single point. The conditions obtained ensure that the motion that results from frictionless contact always instantaneously reduces part misalignment. We show that, for bounded misalignments, if an admittance satisfies the misalignment. reducing conditions at a finite number of contact configurations, then the admittance will also satisfy the conditions at all intermediate configurations. [ABSTRACT FROM AUTHOR]

Consider a biped evolving in the sagittal plane. The unexpected rotation of the supporting foot can be avoided by controlling the zero moment point (ZMP). The objective of this study is to propose and analyze a control strategy for simultaneously regulating the position of the ZMP and the joints of the robot. If the tracking requirements were posed in the time domain, the problem would be underactuated in the sense that the number of inputs would be less than the number of outputs. To get around this issue, the proposed controller is based on a path-following control strategy, previously developed for dealing with the underactuation present in planar robots without actuated ankles. In particular, the control law is defined in such a way that only the kinematic evolution of the robot's state is regulated, but not its temporal evolution. The asymptotic temporal evolution of the robot is completely defined through a one degree-of-freedom subsystem of the closed-loop model. Since the ZMP is controlled, bipedal walking that includes a prescribed rotation of the foot about the toe can also be considered. Simple analytical conditions are deduced that guarantee the existence of a periodic motion and the convergence toward this motion. [ABSTRACT FROM AUTHOR]

We consider the problem of approximating a finite-length continuous curve by a piecewise linear one whose segments are assumed to be connected by 2 DOE joints. We solve the problem under the assumption that the endpoints of the line segments lie on the continuous curve. Analytical expressions for the relative orientations of each pair of line segments as a function of a single rotational DOE are found. This angle can be chosen arbitrarily or used to optimize a secondary task. The motivating application for this paper is the control of a snake-like robot using gaits designed from shape primitives. [ABSTRACT FROM AUTHOR]

In this paper, a novel type of impedance controllers for flexible joint robots is proposed. As a target impedance, a desired stiffness and damping are considered without inertia shaping. For this problem, two controllers of different complexity are proposed. Both have a cascaded structure with an inner torque feedback loop and an outer impedance controller. For the torque feedback, a physical interpretation as a scaling of the motor inertia is given, which allows to incorporate the torque feedback into a passivity-based analysis. The outer impedance control law is then designed differently for the two controllers. In the first approach, the stiffness and damping terms and the gravity compensation term are designed separately. This outer control loop uses only the motor position and velocity, but no noncollocated feedback of the joint torques or link side positions. In combination with the physical interpretation of torque feedback, this allows us to give a proof of the asymptotic stability of the closed-loop system based on the passivity proper- ties of the system. The second control law is a refinement of this approach, in which the gravity compensation and the stiffness implementation are designed in a combined way. Thereby, a desired static stiffness relationship is obtained exactly. Additionally, some extensions of the controller to viscoelastic joints and to Cartesian impedance control are given. Finally, some experiments with the German Aerospace Center (DLR) lightweight robots verify the developed controllers and show the efficiency of the proposed control approach. [ABSTRACT FROM AUTHOR]

Force feedback is necessary for accurate force control in robotic manipulators, and thus far, wrist force/torque (F/T) sensors have been used. But an important problem arises when only these types of sensors are used. In a dynamic situation where the manipulator moves in either free or constrained space, the interaction forces and moments at the contact point and also the noncontact ones are measured by the mentioned sensor. In this paper, an estimator based on a sensor fusion strategy integrating the measurements of three different sensors (a wrist FIT sensor, an inertial sensor, and joint sensors) was developed to determine the contact force and torque exerted by the manipulator to its environment. The resulting observer helps to overcome some difficulties of uncertain world models and unknown environments since it reduces the high-frequency and low-frequency spectral contents, i.e., the low-frequency component due to inertia of a heavy tool mass and the high-frequency component due to impacts. The new improvement was experimentally validated in a force/position impedance control loop applied to a Stäubli RX60 industrial robotic platform. [ABSTRACT FROM AUTHOR]