This article presents a control strategy to carry out multiobject manipulation on a novelsoft robotictable (SoTa), which is a new form of the planar distributed manipulator. Manipulating multiple delicate objects simultaneously is an attractive feature of SoTa. The challenge here is to coordinate multiple objects in a confined planar space while avoiding interference with each other. The SoTa system adopts a manipulation strategy that includes a planning and a tracking stage for the purpose of sorting objects. The planning stage consists of two phases: 1) discrete path planning to find a path for each object on a grid map with respect to time; 2) trajectory generation to optimize and produce workable trajectories for SoTa. In the discrete path planning phase, a hierarchical searching method based on the time-varying potential field is proposed. Constraints of the SoTa system are modeled and incorporated into the path searching process. In the trajectory generation phase, a piecewise B-spline method is adopted to generate trajectories based on previously found discrete paths. Next, in the tracking stage, the objects are led to their goals along the trajectories ensuring safety and SoTa's capability. The performances of the proposed algorithm were simulated, analyzed, and compared with the conflict-based search method, which is optimal for multiagent path finding. A multiobject manipulation experiment of three objects on a$4\times 4$grid was conducted on the SoTa. The results demonstrated the effectiveness of the proposed control strategy in executing multiobject manipulations for sorting tasks on the SoTa.

This article presents the design, development, and validation of a closed-loop control system for a soft robotic table. The soft table can provide independent manipulation to multiple objects simultaneously, which is an advantage over traditional devices such as robotic arms and belt conveyor systems. The table also provides intrinsic soft handling for delicate objects. In order to execute automation tasks, a vision feedback system and an operation controller are developed and incorporated into the existing table prototype to form a closed-loop control. The vision feedback system implements image processing techniques to identify the object location from captured images. The operation controller is integrated from three sets of algorithms, including movement planning, actuator selection, and actuation planning. Two sets of experiments were conducted to validate the closed-loop system and the capability of the soft table. The first set validated the manipulation task of transporting an object to a desired location with autonomous planning and execution. The second set validated the table's ability to manipulate multiple objects simultaneously and independently. Videos of both experiments have been supplied.

Soft robots must be able to structure an automation problem into a sequence of actions that lead to a desired state, before they can fulfill a meaningful role in automation applications. This, however, can only be successful if the robot can predict the outcome of an action. The theory of rigid industrial robots is not applicable without major changes, because kinematic chains do not adequately describe the continuous deformation of the complex, often biologically inspired shapes of soft robots. Analytic solutions have not been found yet. Numerical solutions based on finite elements are slow, technically challenging, and only suitable for one specific robot. It is, however, possible to observe the outcome of an action, and use these observations to plan a sequence of actions that let the robot accomplish an automation task. In this paper, we analyze a probabilistic automaton that computes the optimal sequence of actions to bring the robot into a desired state. An earlier article explained the functioning of the method in a toy example. In this paper, we analyze if it is feasible to apply the method to a planning problem inspired by a real soft robot. We show the results and document the planning process. We identify the analog of an impulse response, although it is not closed form due to the nonparametric nature of the method.

A peristaltic, soft-bodied xy-sorting table manipulates objects by producing moving wave shapes on its surface. The waves exert forces on the objects which can be used for transportation, reorientation, and local repositioning. The control of such peristaltic robots is mostly unsolved, because important properties of the kinematics, dynamics, and effect of actuation are unknown. Fundamental and practical limitations in measuring the system state lead to numerical difficulties in the form of discontinuous signals in non-Euclidean spaces. To solve these problems, we introduce a probabilistic automaton that models the static and dynamic effect of actuation based on a discretized representation of the objects to be manipulated. The automaton only considers the qualitative input-output behavior of the system. It is therefore mostly independent of a particular hardware setup and controls the behavior of objects on the table without explicitly predicting mechanical forces. Our theoretical findings advance the field significantly: We show that the proposed class of automata is suited to model both static and dynamic movements. We introduce a cost function that enables the search and identification of optimal sequences of control patterns to bring the system into a desired state. We prove that the optimal control sequence can be found efficiently and give the respective algorithm.

This paper analyzes the application of machine learning techniques to the control of a soft, peristaltic, xy-sorting table. In particular, we address peristaltic tables made of a soft upper silicone layer and actuated by an array of integrated air-filled chambers. The chambers are pneumatically inflated in order to deform the table and move objects on the table. To control the robot precisely, it is necessary to model both the inverse mapping between the control signals of the actuators and the resulting surface deformation. There is currently no parametric model available. In this paper, we, therefore, study if nonparametric approaches are applicable. In these approaches, the mapping would be learned from a database of input signals and observed behaviors. From our analysis, we conclude that the most promising research direction consists in the nonparametric modeling of a limited set of peristaltic actuation patterns. However, the nonlinear hardware design that impedes a parametric model also affects the nonparametric optimization process. Our simulation suggests that the optimization is nonconvex, approximately monotonous, and feasible in terms of the number of observations of the physical robot.

Nearest neighbour searches in the image plane are among the most frequent problems in a variety of computer vision and image processing tasks. They can be used to replace missing values in image filtering, or to group close objects in image segmentation, or to access neighbouring points of interest in feature extraction. In particular, we address two nearest neighbour problems: The nearest neighbour problem is usually stated independently of the application as returning the point p ∈ S,S = {(x1,y1), . . . ,(xn,yn)} that minimises the Euclidean distance ||p− q||2 to a query point q = (x,y). The simple solution of a linear scan comprises a comparison of q to all elements of S, which is too time-consuming for most applications, especially those with real-time requirements. If the nearest neighbour p ∈ S must be found for every coordinate q ∈ I of an image I = {(0,0),(0,1),(0,2), . . . ,(W,H)} of width W and height H, we obtain the all nearest neighbours problem. This problem occurs frequently in modern saliency based approaches, where only robustly detectable image regions are processed (for example in SIFT). In this paper, we introduce an approximate solution to solve these problems that is based on using a space filling curve. The central idea of the proposed approach is to map the image plane to one dimension using the Hilbert curve (Fig. 1). The nearest-neighbour problem is then solved efficiently in one dimension and mapped back to the 2D-plane.

Document enhancement tools are a valuable help in the study of historic documents. Given proper filter settings, many effects that impair the legibility can be evened out (e.g. washed out ink, stained and yellowed paper). However, because of differing authors, languages, handwritings, fonts and paper conditions, no single filter parameter set fits all documents. Therefore, the parameters are usually tuned in a time-consuming manual process to every individual document. To simplify this procedure, this paper introduces a classifier for the legibility of an enhanced historic text document. Experiments on the binarisation of a set of documents from 1938 to 1946 show that the classifier can be used to automatically derive robust filter settings for a variety of documents.

3D environment reconstruction is a basic task, delivering the data for mapping, localization and navigation in mobile robotics. We present a new technique that combines a stereo-camera system with a PMD-camera. Both systems generate distance images of the environment but with different characteristics. It is shown that each system compensates effectively for the deficiencies of the other one. The combined system is real-time suited. Experimental data of an indoor scene including the calibration procedure are reported.