To aid in real-world situations, soft robots need to be able to estimate their state and external interactions based on proprioceptive sensors. Estimating disturbances allows a soft robot to perform desirable force control. However, even in the case of rigid manipulators, force estimation at the end-effector is seen as a non-trivial problem. And indeed, current approaches to address this challenge have shortcomings that prevent their general application. They are often based on simplified soft dynamic models, such as the ones relying on a piece-wise constant curvature approximation or matched rigid-body models that do not represent enough details of the problem. This severely limits applications in complex human-robot interaction. Finite element method (FEM) based modeling allows for predictions of soft robot dynamics in a more generic fashion. Here, using the soft robot modeling capabilities of the frame-work SOFA, we built a detailed FEM model of a multi-segment soft continuum robotic arm composed of compliant deformable materials and fiber-reinforced pressurized actuation chambers. In addition, a model for sensors that provide orientation output is presented. This model is used to establish a state observer for the manipulator. The sensor model is adequate for representing the output of flexible bend sensors as well as orientations provided by IMUs or coming from tracking systems, all of which are popular choices in soft robotics. Model parameters were calibrated to match imperfections of the manual fabrication process using physical experiments. We then solve a quadratic programming inverse statics problem to compute the components of external force that explain the pose mismatch. Our experiments show an average force estimation error of around 1.2%. As the methods proposed are generic, these results are encouraging for the task of building soft robots exhibiting complex, reactive, sensor-based behavior that can be deployed in human-centered environments.

Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey article, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. In this domain, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Therefore, based on this observation, in the beginning of this article, we propose a categorization to organize the use cases of proximity sensors in human-centered robotics. Then, we devote effort to present the sensing technologies and different measuring principles that have been developed over the years, also providing a summary in form of a table. Following, we review the literature regarding the applications that have been proposed. Finally, we give an overview of the most important trends that will shape the future of this domain.

In this paper we present an approach for material recognition using capacitive tactile and proximity sensors. By variating the spatial resolution and the exciter frequency during the measurement in mutual capacitive mode, information about the dielectrical properties of different objects was captured and provided as data frames. For material recognition an artificial neural network was set up and fed with various data sets of different electrode combinations and exciter frequencies. The influence of the electrode combinations and shapes on the recognition accuracy was investigated. It is shown that seven objects of conductive and non-conductive dielectric materials have been ranged with an overall accuracy of about 71%-94%.

Many manipulation applications in industrial settings using robots depend on reliable detection and grasping capabilities of stationary or moving objects. The latter demand an extended use of intrinsic and extrinsic sensors being in a closed control-loop with a robotic system. In this paper we present an approach for tracking, reconstruction and grasping of unknown objects from a moving conveyor belt using an external depth sensor and an industrial robot. The approach consists of an algorithm for the estimation of the position, velocity and form of an object, an algorithm for estimation of the conveyor's path and an algorithm for calculating an interception trajectory for the robot. The presented scheme is designed for tracking and reconstruction of unknown rotationally symmetrical objects. Nevertheless, the reconstruction algorithm is able to detect asymmetrical parts of an object and recognize if an object is hollow. This is further used for calculation of a feasible grasp. From the reconstruction data a mesh of the object is created. Finally, robots interception and grasping trajectory are calculated on-line for any arbitrary position on the conveyor. The objects are sucessfully grasped from moving conveyor up to the speed of more than 10 [cm/s].

In this paper we've equipped a cylindrical end-effector with an array of capacitive sensors in order to implement 3D contour following. During the task, using proximity servoing, the sensors are aligned parallel to the surface and kept at a target distance. In addition, due to the spatial resolution, it's possible to estimate the surface's curvature in two dimensions along the rows and columns of the array. We show how a compound movement can be derived from both curvatures that pre-aligns the end-effector in each step, yielding a predictive component for the control scheme. We evaluate our approach with different geometries and show that the curvature information produces smooth contour following paths. We show that the system can handle speeds up to 150mms-1.

In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parameters relevant to the system. Furthermore, proximity sensors enable the autonomous alignment/centering of the gripper to objects in user-selected DoFs with the potential of aiding the user and lowering the workload. We evaluate our approach in a user study that shows that the telemanipulation system benefits from the supplementary proximity information and that the workload can indeed be reduced when the system operates with partial autonomy.

In this paper we present applications for a robot system whose gripper is equipped with distributed capacitive tactile proximity sensors (CTPS). Firstly, we introduce and evaluate a closed loop control scheme by which it is possible to align the gripper to objects or features of the environment using proximity values alone. We call this control method proximity servoing. It is implemented by equilibrating the sensor signals, resulting in a robust preshape in all 6DOF of the object pose. Objects can then be grasped with virtually no displacement. Secondly, also based on proximity servoing, we demonstrate and evaluate novel ideas for combined haptic and proximity-based exploration. Without any cues from an external camera the system is capable of detecting and exploring features such as curvatures, edges or corners of objects. It is also shown that tactile and proximity exploration steps can be used complementarily to increase efficiency in exploration while delivering accurate object measurements.

In this paper we base upon capacitive tactile proximity sensor modules developed in a previous work to demonstrate applications for safe human-robot-interaction. Arranged as a matrix, the modules can be used to model events in the near proximity of the robot surface, closing the near field perception gap in robotics. The central application investigated here is object tracking. Several results are shown: the tracking of two human hands as well as the handling of occlusions and the prediction of collision for object trajectories. These results are important for novel pretouch- and touch-based humanrobot interaction strategies and for assessing and implementing safety capabilities with these sensor systems.

This paper presents a novel approach for haptic object recognition with an anthropomorphic robot hand. Firstly, passive degrees of freedom are introduced to the tactile sensor system of the robot hand. This allows the planar tactile sensor patches to optimally adjust themselves to the object's surface and to acquire additional sensor information for shape reconstruction. Secondly, this paper presents an approach to classify an object directly from the haptic sensor data acquired by a palpation sequence with the robot hand - without building a 3d-model of the object. Therefore, a finite set of essential finger positions and tactile contact patterns are identified which can be used to describe a single palpation step. A palpation sequence can then be merged into a simple statistical description of the object and finally be classified. The proposed approach for haptic object recognition and the new tactile sensor system are evaluated with an anthropomorphic robot hand.