This paper presents an overview of admittance control as a method of physical interaction control between machines and humans. We present an admittance controller framework and elaborate control ...scheme that can be used for controller design and development. Within this framework, we analyze the influence of feed-forward control, post-sensor inertia compensation, force signal filtering, additional phase lead on the motion reference, internal robot flexibility, which also relates to series elastic control, motion loop bandwidth, and the addition of virtual damping on the stability, passivity, and performance of minimal inertia rendering admittance control. We present seven design guidelines for achieving high-performance admittance controlled devices that can render low inertia, while aspiring coupled stability and proper disturbance rejection.
Dynamic control of soft robotic manipulators is an open problem yet to be well explored and analyzed. Most of the current applications of soft robotic manipulators utilize static or quasi-dynamic ...controllers based on kinematic models or linearity in the joint space. However, such approaches are not truly exploiting the rich dynamics of a soft-bodied system. In this paper, we present a model-based policy learning algorithm for closed-loop predictive control of a soft robotic manipulator. The forward dynamic model is represented using a recurrent neural network. The closed-loop policy is derived using trajectory optimization and supervised learning. The approach is verified first on a simulated piecewise constant strain model of a cable driven under-actuated soft manipulator. Furthermore, we experimentally demonstrate on a soft pneumatically actuated manipulator how closed-loop control policies can be derived that can accommodate variable frequency control and unmodeled external loads.
This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstractionDiscusses methods for optimization of task reproduction, ...such as reformulation of task planning as a constrained optimization problemFocuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regressionConcentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert
The proven efficacy of learning-based control schemes strongly motivates their application to robotic systems operating in the physical world. However, guaranteeing correct operation during the ...learning process is currently an unresolved issue, which is of vital importance in safety-critical systems. We propose a general safety framework based on Hamilton-Jacobi reachability methods that can work in conjunction with an arbitrary learning algorithm. The method exploits approximate knowledge of the system dynamics to guarantee constraint satisfaction while minimally interfering with the learning process. We further introduce a Bayesian mechanism that refines the safety analysis as the system acquires new evidence, reducing initial conservativeness when appropriate while strengthening guarantees through real-time validation. The result is a least-restrictive, safety-preserving control law that intervenes only when the computed safety guarantees require it, or confidence in the computed guarantees decays in light of new observations. We prove theoretical safety guarantees combining probabilistic and worst-case analysis and demonstrate the proposed framework experimentally on a quadrotor vehicle. Even though safety analysis is based on a simple point-mass model, the quadrotor successfully arrives at a suitable controller by policy-gradient reinforcement learning without ever crashing, and safely retracts away from a strong external disturbance introduced during flight.
Magnetically actuated small-scale robots have great potential for numerous applications in remote, confined, or enclosed environments. Multiple small-scale robots enable cooperation and increase the ...operating efficiency. However, independent control of multiple magnetic small-scale robots is a great challenge, because the robots receive identical control inputs from the same external magnetic field. In this article, we propose a novel strategy of completely decoupled independent control of magnetically actuated flexible swimming millirobots. A flexible millirobot shows a crawling motion on a flat plane within an oscillating magnetic field. Millirobots with different magnetization directions have the same velocity response curve to the oscillating magnetic field but with a difference of phase. We designed and fabricated a group of up to four heterogeneous millirobots with identical geometries and different magnetization directions. According to their velocity response curves, an optimal direction of oscillating magnetic field is calculated to induce a desired velocity vector for the millirobot group, one of which is nonzero and the others are approximately zero. The strategy is verified by experiments of independent position control of up to four millirobots and independent path following control of up to three millirobots with small errors. We further expect that with this independent control strategy, the millirobots will be able to cooperate to finish complicated tasks.
Highly stretchable strain sensors based on conducting polymer hydrogel are rapidly emerging as a promising candidate toward diverse wearable skins and sensing devices for soft machines. However, due ...to the intrinsic limitations of low stretchability and large hysteresis, existing strain sensors cannot fully exploit their potential when used in wearable or robotic systems. Here, a conducting polymer hydrogel strain sensor exhibiting both ultimate strain (300%) and negligible hysteresis (<1.5%) is presented. This is achieved through a unique microphase semiseparated network design by compositing poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) nanofibers with poly(vinyl alcohol) (PVA) and facile fabrication by combining 3D printing and successive freeze‐thawing. The overall superior performances of the strain sensor including stretchability, linearity, cyclic stability, and robustness against mechanical twisting and pressing are systematically characterized. The integration and application of such strain sensor with electronic skins are further demonstrated to measure various physiological signals, identify hand gestures, enable a soft gripper for objection recognition, and remote control of an industrial robot. This work may offer both promising conducting polymer hydrogels with enhanced sensing functionalities and technical platforms toward stretchable electronic skins and intelligent robotic systems.
A conducting‐polymer hydrogel strain sensor is proposed with both high stretchability (300% strain) and ultralow hysteresis (<1.5%). The hydrogel‐based sensor harnesses a unique microphase semiseparated network to achieve enhanced sensing properties. The fabricated sensor can be applied as electronic skins to monitor physiological signals, enable a soft gripper for object recognition and remote control of an industrial robot.
Soft robots, owing to their elastomeric material, ensure safe interaction with their surroundings. These robot compliance properties inevitably impose a tradeoff against precise motion control, as to ...which conventional model-based methods were proposed to approximate the robot kinematics. However, too many parameters, regarding robot deformation and external disturbance, are difficult to obtain, even if possible, which could be very nonlinear. Sensors self-contained in the robot are required to compensate modeling uncertainties and external disturbances. Camera (eye) integrated at the robot end-effector (hand) is a common setting. To this end, we propose an eye-in-hand visual servo that incorporates with learning-based controller to accomplish more precise robotic tasks. Local Gaussian process regression is used to initialize and refine the inverse mappings online, without prior knowledge of robot and camera parameters. Experimental validation is also conducted to demonstrate the hyperelastic robot can compensate an external variable loading during trajectory tracking.
In this study, the control problem of complex robot system with uncertainties and disturbances is addressed. Fuzzy system-fuzzy neural network-backstepping control (FS-FNN-BSC) system is proposed, ...which can guarantee the accurate, stable and efficient control. First, the general dynamics model of robot is introduced briefly. Then, the design procedure of backstepping control (BSC) technique is presented, to make the best of the advantages of fuzzy system (FS) and fuzzy neural network (FNN) and compromise the accuracy and efficiency, the FS is adopted to approximate the modeling information, and the FNN is utilized to approximate and predict the non-modeling information, and the FS-FNN-BSC system is constructed. Moreover, based on the Lyapunov stability theorem, the stability of the FS-FNN-BSC is proved. To illustrate the correctness, practicality and generality of the proposed control method, the FS-FNN-BSC system is applied to the series robot (KUKA robot) and the parallel robot (Delta robot). And the superiority of the proposed FS-FNN-BSC strategy is highlighted by quantitative comparison with the existing intelligent control methods.
Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with ...them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human-robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.
ntroduces a revolutionary, quadratic-programming based approach to solving long-standing problems in motion planning and control of redundant manipulators.This book describes a novel quadratic ...programming approach to solving redundancy resolutions problems with redundant manipulators. Known as “QP-unified motion planning and control of redundant manipulators” theory, it systematically solves difficult optimization problems of inequality-constrained motion planning and control of redundant manipulators that have plagued robotics engineers and systems designers for more than a quarter century.
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