A path planning method of 6-DOF robot for mirror therapy based on A* algorithm

BACKGROUND: With the population aging, post-stroke patients suffering from hemiplegia are also rapidly increasing. It is essential to provide valid rehabilitation methods for hemiplegia patients. Mirror therapy is an effective rehabilitation method and is widely applied in many rehabilitation robots. OBJECTIVE: The aim of this paper is to present a path planning method to guarantee the robot’s motion performance during mirror therapy. METHODS: The kinematic framework of the proposed rehabilitation system is detailed, then the reference motion path of the manipulator is calculated according to kinematic transformation. The concept of manipulability is introduced to describe the motion performance of the manipulator. Based on the above work, a path planning method based on A* algorithm is proposed to quantitatively analyze and optimize the motion performance of the manipulator. RESULTS: Preliminary experiments with the proposed rehabilitation system are conducted to verify the proposed path planning method. The characteristics of the proposed method are analyzed through two typical situations. The results showed that the proposed method can build a new path for manipulator, which can ensure the robot’s motion performance and is highly consistent with the reference path. CONCLUSION: The results showed that the manipulator could achieve the task with acceptable error, which indicates the potential of the proposed path planning method for mirror therapy.

Simulation of the magnetic system of a linear motor for a delimber

The existing pruning shears and delimbers have many drawbacks that limit their widespread use in the production process. These are such disadvantages as large weight and dimensions, high power consumption, vibration and noise, low mobility due to being tied to an energy source. DC motors are used to drive the cutting blades. Their main disadvantage is the low operational reliability of such an element as electric brushes. The use of the kinematic transformation of the rotational motion of the electric motor into the reciprocating motion of the blades reduces the overall efficiency of the device and increases the consumption of electrical energy. The proposed linear electric motor for the delimber drive will increase the efficiency, operational reliability and reduce energy consumption for cutting tree branches. A feature of a linear electric motor is the use of two magnetizing coils, which are switched on alternately. The use of a thin element in the magnetic system makes it possible to redistribute the obtained magnetic flux towards the armature of the linear electric motor. The armature of the electric motor consists of magnetic and nonmagnetic bushings of a certain design. This allows you to obtain a magnetic flux, which, passing along the armature, creates an electromagnetic force that sets it in motion. The cutting blade is then anchored. This allows you to improve the characteristics of the proposed delimber. The main challenge in the design of the delimber is to create the maximum force on the cutting blade required to cut branches. For this, it is necessary to perform an improvement of the magnetic system of the linear electric motor. For this purpose, the simulation of the magnetic system was carried out in the ElCut program.

Mathematical Tools for Automating Digital Fixture Setups: Constructing T-Maps and Relating Metrological Data to Coordinates for T-Maps and Deviation Spaces

Mathematical tools underlie a method that has strong potential to lower the cost of fixture-setup when finishing large castings that have machined surfaces where other components are attached. One math tool, the kinematic transformation, is used for the first time to construct Tolerance-Map® (T-Map)® models of geometric and size tolerances that are applied to planar faces and to the axes of round shapes, such as pins or holes. For any polygonal planar shape, a generic T-Map primitive is constructed at each vertex of its convex hull, and each is sheared uniquely with the kinematic transformation. All are then intersected to form the T-Map of the given shape in a single frame of reference. For an axis, the generic T-Map primitive represents each circular limit to its tolerance-zone. Both are transformed to a central frame of reference and are intersected to form the T-Map. The paper also contains the construction for the first five-dimensional (5D) T-Map for controlling the minimum wall thickness between two concentric cylinders with a least-material-condition (LMC) tolerance specification on position. It is formed by adding the dimension of size to the T-Map for an axis. The T-Maps described are consistent with geometric dimensioning and tolerancing standards and industry practice. Finally, transformations are presented to translate between small displacement torsor (SDT) coordinates and the classical coordinates for lines and planes used in T-Maps.

Hand posture comparison in Synergy Space

In studies of human movement control, an important question is how the central nervous system (CNS) controls movements in the presence of a large number of degrees of freedom (DoFs) at all levels of the control architecture. It is known that CNS groups the multiple DoFs into representative functional units also called synergies for simplification of the task. Hence, comparing two different hand postures in the synergy space, rather than joint angle space may provide insightful information about efforts needed (in terms of synergy components) to transform from one hand posture to another hand posture. Working with synergy space may also provide information about how CNS deals with system with multiple DoFs. We developed an index called posture similarity index (PSI) which measures the similarity of two postures by projecting hand posture from the joint angles into the synergy space. A large value of PSI represents high similarity between postures whereas a lower value represents less similarity between postures. This index uses principle of synergies and nicely captures effort required for kinematic transformation. Using this index as a feature, possible set of representative postures can be identified. The other hand postures can be derived from a possible set of representative postures with relatively less efforts for kinematic transformation.

Perceptual attraction in tool use: evidence for a reliability-based weighting mechanism

Humans are well able to operate tools whereby their hand movement is linked, via a kinematic transformation, to a spatially distant object moving in a separate plane of motion. An everyday example is controlling a cursor on a computer monitor. Despite these separate reference frames, the perceived positions of the hand and the object were found to be biased toward each other. We propose that this perceptual attraction is based on the principles by which the brain integrates redundant sensory information of single objects or events, known as optimal multisensory integration. That is, 1) sensory information about the hand and the tool are weighted according to their relative reliability (i.e., inverse variances), and 2) the unisensory reliabilities sum up in the integrated estimate. We assessed whether perceptual attraction is consistent with optimal multisensory integration model predictions. We used a cursor-control tool-use task in which we manipulated the relative reliability of the unisensory hand and cursor position estimates. The perceptual biases shifted according to these relative reliabilities, with an additional bias due to contextual factors that were present in experiment 1 but not in experiment 2. The biased position judgments’ variances were, however, systematically larger than the predicted optimal variances. Our findings suggest that the perceptual attraction in tool use results from a reliability-based weighting mechanism similar to optimal multisensory integration, but that certain boundary conditions for optimality might not be satisfied. NEW & NOTEWORTHY Kinematic tool use is associated with a perceptual attraction between the spatially separated hand and the effective part of the tool. We provide a formal account for this phenomenon, thereby showing that the process behind it is similar to optimal integration of sensory information relating to single objects.

Reference trajectory tracking for a multi-DOF robot arm

This paper presents the problem of tracking the generated reference trajectory by the simulation model of a multi-DOF robot arm. The kinematic transformation between task space and joint configuration coordinates is nonlinear and configuration dependent. To obtain the solution of the forward kinematics problem, the homogeneous transformation matrix is used. A solution to the inverse kinematics is a vector of joint configuration coordinates calculated using of pseudoinverse Jacobian technique. These coordinates correspond to a set of task space coordinates. The algorithm is presented which uses iterative solution and is simplified by considering stepper motors in robot arm joints. The reference trajectory in Cartesian coordinate system is generated on-line by the signal generator previously developed in MS Excel. Dynamic Data Exchange communication protocol allows sharing data with Matlab-Simulink. These data represent the reference tracking trajectory of the end effector. Matlab-Simulink software is used to calculate the representative joint rotations. The proposed algorithm is demonstrated experimentally on the model of 7-DOF robot arm system.

Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks

Nowadays, on the basis of significant work carried out, architectural adaption structures are considered to be intelligent entities, able to react to various internal or external influences. Their adaptive behavior can be examined in a digital or physical environment, generating a variety of alternative solutions or structural transformations. These are controlled through different computational approaches, ranging from interactive exploration ones, producing alternative emergent results, to automate optimization ones, resulting in acceptable fitting solutions. This paper examines the adaptive behavior of a kinetic structure, aiming to explore suitable solutions resulting in final appropriate shapes during the transformation process. A machine learning methodology that implements an artificial neural networks algorithm is integrated to the suggested structure. The latter is formed by units articulated together in a sequential composition consisting of primary soft mechanisms and secondary rigid components that are responsible for its reconfiguration and stiffness. A number of case studies that respond to unstructured environments are set as examples, to test the effectiveness of the proposed methodology to be used for handling a large number of input data and to optimize the complex and nonlinear transformation behavior of the kinetic system at the global level, as a result of the units’ local activation that influences nearby units in a chaotic and unpredictable manner.