This study proposes a computational method for approximating the Minkowski sum of two ellipsoids in three-dimensional space using a superellipsoid. Computing the Minkowski sum using high-precision mesh generation is expensive. Therefore, methods for approximating the Minkowski sum using functional representations have been proposed. In particular, numerous approaches use ellipsoids for approximation but suffer from accuracy limitations. The shape matrix and exponents of the superellipsoid are derived through a closed-form computation with constant time complexity of O ( 1 ) , independent of any boundary sampling resolution. Moreover, because the superellipsoid is an implicit function, the inside–outside testing of points is straightforward and requires only the evaluation of an inequality. In our experiments, we evaluate various parameter combinations for two general ellipsoids. The results demonstrate that the superellipsoid approximates the Minkowski sum with small error. Additionally, a comparison with an existing method that approximates the Minkowski sum of rotational ellipsoids using superellipsoids shows comparable accuracy, while the proposed method generalizes the approach to arbitrary ellipsoids. Regarding execution time, the method exhibits consistently fast and stable performance, as it relies solely on closed-form computation. The theoretical framework of this method has the potential to be extended to N -dimensional spaces, and is expected to find applications not only in rigid body simulations in three dimensions but also in computational tasks, such as grasp stability evaluation for robotics in six dimensions.

Abstract In this paper, we propose a fast and accurate method for approximating the Minkowski sum of two rotational ellipsoids with a superellipsoid. The Minkowski sum is used in a variety of applications such as robot motion planning and particle flow simulation requiring collision detection. Many of them are computed based on Minkowski sum, whose accuracy and processing time depend on how the mesh is created. We approximate Minkowski sum with a superellipsoid function. The superellipsoid has various shapes with the value of the exponents, and the computation of the parameters including the exponents is an algebraic computation with the time complexity $$\varvec{O}(1)$$ O ( 1 ) . As a result, approximation error is small and computation is fast in all combinations of rotational ellipsoids. In addition, collision is quickly detected by solving the inequality of superellipsoid.

This study presents a method for estimating the three-dimensional (3D) shapes of transparent objects from an RGB-D image using a statistical shape model. Statistical shape models compress the dimensions of multiple shapes to represent shape variations using fewer parameters. It is difficult to measure the depth of a transparent object using sensors. Therefore, the statistical shape model is deformed to fit the contour extracted from an RGB image and estimate the shape of the object. The depth image is used only to detect the plane on which the transparent objects are placed. Unlike other estimation methods, the proposed method estimates the whole shape of a transparent object. To validate the proposed method, the obtained estimation accuracy is compared with that of a machine-learning-based method. In addition, the estimated whole shape is compared with the measured data from a 3D scanner.

Quickness, preciseness and robustness are required in manipulation tasks of robotic hands for automation of manufacturing sites. Previous researches have found that sensing from fingertips equipped with proximity sensors is available for the requirements, because it complements blind areas of vision sensors. In this paper, we develop a novel optical proximity sensor for robot fingertips which provides two levels of proximity information with different purposes, sampling rates, information quantity and quality. The lower-level information from the sensor is for high-speed feedback control of a robotic hand, and the higher-level information is for recognizing the shape and size of an object. A prototype of the sensor with 5 × 5 matrix of photo detectors is presented, and its availability is shown through basic characteristic tests. A motion experiment using a robotic hand equipped with the prototype sensors is also conducted. The result confirms that the robotic hand can adjust the position and orientation of the fingertips to various objects and then correct the grasping form according to the object size within 1s.

Unnoticed collisions compromise the success of remote exploratory tasks with mobile robots. We propose a vibrotactile stimulation method to represent frontal collisions that is based on the way people perceive impacts on a bar held with both hands. We observed that to estimate the impact point in a bimanual impact localization task people relied on amplitude and duration differences of the impact vibrations delivered to their hands. Then, to apply these results, we obtained a psychophysical function that relates impact points and vibration parameters. Finally, we used a differential drive mobile robot equipped with a high speed tactile sensor on the front bumper to evaluate our method in a simplified teleoperation task. We observed that participants required less time to complete the task when vibrotactile feedback was available.

A self-reconfigurable modular robotic system has potential to carry out a wide variety of tasks in various fields and conditions. For realizing such versatileness and robustness, the compositional modules have to be designed so that the combined structure is equipped with high degree of freedom of the self-reconfiguration. We propose a new design of a modular robot using helically magnetized axes. The axes are arranged at all of the edges of the cubic-shaped module and contribute to both connection and transformation among the modules. The mechanism enables the modules to construct a 3-D orthogonal lattice structure, execute slide movement to neighboring vacant grid positions, and change the structural configuration by themselves. This paper firstly describes the conceptual design and fabrication method of the helically magnetized axis. Then, total design and specification of the module are explained. Finally, experimental results to evaluate the performance of connection and transformation are presented.

This paper presents methodologies for dexterous manipulation by an underactuated robotic soft gripper with a low DOF. While the softness and passive joints provide a stable grasp, the manipulations become challenging owing to the complexities of modeling and state estimation. To investigate the drawbacks, target manipulations using a support surface and gravity are defined in this study. By realizing the target manipulations, methodologies for utilizing both the support surface and gravity in manipulation are validated. For manipulation utilizing the support surface, the key states - at which the type of gripper motion primitive changes - are defined, and gripper motion at the key states is produced with a DNN-based motion generator. For manipulation utilizing gravity, the fall risk of the object is formulated as a constraint for a criterion function, and a parameters-online-exploration based methodology is adopted. The validity of the methodologies is demonstrated through several experiments.

In this study, we propose integrated control of a robotic hand and arm using only proximity sensing from the fingertips. An integrated control scheme for the fingers and for the arm enables quick control of the position and posture of the arm by placing the fingertips adjacent to the surface of an object to be grasped. The arm control scheme enables adjustments based on errors in hand position and posture that would be impossible to achieve by finger motions alone, thus allowing the fingers to grasp an object in a laterally symmetric grasp. This can prevent grasp failures such as a finger pushing the object out of the hand or knocking the object over. Proposed control of the arm and hand allowed correction of position errors on the order of several centimeters. For example, an object on a workbench that is in an uncertain positional relation with the robot, with an inexpensive optical sensor such as a Kinect, which only provides coarse image data, would be sufficient for grasping an object.

This study presents a robot-hand-arm system with high robustness and responsiveness by using a “Net-Structure Proximity Sensor.” The sensor, which we have developed and specially designed for a robot hand, directly detects an object being to be grasped and outputs analog voltage signals according to the position/posture error between the robot hand and the object. It has been confirmed that the robot hand is able to quickly adjust to and grasp an unknown object by applying a feed-back control method based on the sensor signals. This paper focuses on the integration of the proximity-based feedback control to a commonly-used vision-based control. These sensors work in complementary manner: a vision sensor is available for planning an approaching path of a robot hand by detecting large area, and a Net-Structure Proximity Sensor enables the robot hand to adjust the approaching error before grasping and to improve the certainty of the grasping. Two objective velocities are derived independently by the sensors. By adding the velocities with considering the reliability of the sensor information, the robot hand becomes to be able to perform approaching and adjustment to the target object simultaneously. Experimental results showed that the robot hand grasped a moving object with high success rate even in conditions where it was difficult to predict the trajectory of the object accurately.

Quick motion and soft touch control are important for autonomous grasping. To perform a grasping action, a hand must adjust its fingertips to match the object shape. It is also necessary to reduce the fingertip velocity on contact with the object. We propose a method of fingertip velocity control for fast approach and slow contact using proximity sensors installed on fingertips. The proposed control reduces the fingertip velocity at contact, decreasing the change of impulse force and thus making it appropriate for grasping soft or fragile objects. The proposed control uses time-to-contact (TTC), which is converted from sensor output. It is known that many animals, including humans, use TTC for collision avoidance. TTC represents the remaining time until collision considering the rate change of a control variable. We carried out grasping tests on various common objects using TTC. Experimental results show that the proposed control realizes fingertip alignment perpendicular to unknown shapes and surface objects and lowers the velocity at contact.

In this paper, we focus on unknown parameters such as the position and attitude of the object, and describe a short-range, high-speed and noncontact sensing method for obtaining the position and attitude of the object using IR Net-Structure Proximity Sensor (“IR-NSPS”) which complements the dead region of sensory information between visual and tactile sensing. To be more precise, we propose two effective control methods which are pre-shaping and object positioning using IR-NSPS for robust grasping by adjusting the gripper configuration in response to attitude error of up to ±45 deg and the position error of up to ±80 mm of the unknown object. The methods therefore can significantly increase the speed and effectiveness of grasping objects without requiring a specific approach that depends on a vision sensor. Furthermore, to demonstrate the advantages of pre-shaping and object positioning, object grasping experiments were performed using these two operations to grasp objects placed randomly on a tabletop.

We have developed a net-structure proximity sensor that detects the azimuth and elevation to a nearby object. This information can be used by robots to avoid obstacles or to respond to human behavior. We propose detection principles where the azimuth is detected by arranging two one-dimensional net-structure proximity sensors along orthogonal axes, and the elevation is detected by arranging two one-dimensional net-structure proximity sensors in a stacked ring. We also experimentally demonstrate the feasibility of these detection principles. The experimental result shows the sensor can detect azimuth at all peripheral angles and elevation from side to top up.

In this paper, we demonstrate a preliminary motion before grasping by a robot hand, for adjusting the object-fingertip distance and 2-axis postures simultaneously, using a Resistor Network Structure Proximity sensor (RNSP sensor). Through this motion (called “pre-shaping”) and the grasping of an object, the surface of each fingertip is brought into contact with the object surface so that in the next stage grasping can be undertaken. In the next stage, a force can be applied from the fingertips onto the object surface directly. The pre-shaping enhances the reliability of the feedback control for the after-contact tactile sensors. To realize the pre-shaping, we use fingertips equipped with RNSP sensors, which can detect the distance between the fingertip and the object, to determine the relative position between fingertips and an object. The RNSP sensor has a fast response (<;1 [ms]) and simple connectivity (only 6 wires), and can be mounted easily. Additionally, a characteristics of the RNSP sensor output can be designed by the arrangement of the sensor elements. To perform the pre-shaping by simple sensor feedback control based on the configuration between the fingertip and object, we designed the RNSP sensor so that it had the appropriate characteristics for the pre-shaping.

Tactile sensors for slip detection are essential for implementing human-like gripping in a robot hand. In previous studies, we proposed flexible, thin and lightweight slip detection sensors utilizing the characteristics of pressure-sensitive conductive rubber. This was achieved by using the high-frequency vibration component generated in the process of slipping of the gripped object in order to distinguish between slipping of the object and changes in the normal force. In this paper, we design a slip detection sensor for a multi-fingered robot hand and examine the influence of noise caused by the operation of such a hand. Finally, we describe an experiment focusing on the adjustment of the gripping force of a multi-fingered robot hand equipped with the developed sensors.

This paper presents a proximity sensor for recognizing an object in 360 degrees all around with response time only 1[ms]. The sensor applies an unique design of analog net-structure circuit, which enables all-face mounting on various peripheries and fast-response/simplification of information processing. Thus, the sensor is especially available for avoiding sudden contact with rapidly approaching objects for mobile robots, robot arms, robot hands, and other robotic systems. In this paper, the structure and the detecting principle of the sensor are firstly described, then, its detection property is tested by experiments. The result shows that the sensor recognizes the angle of the nearby object with accuracy of 3[deg]. Furthermore, as practical use of the sensor, a large size model is produced for a mobile robot to achieve omni-directional sensing. By using the application model, two detection methods are discussed on situations where the number or the positions of the nearby objects differs.

This study deals with a control method of a modular robotic system ldquoCHOBIE II.rdquo Our purpose is to realize a function that CHOBIE II forms a self-reconfigurable structure adaptively to mechanical environments. In the previous paper, we introduced a method to express criteria for generating transformations. A criterion is regulated by 32 parameters corresponding to 32 kinds of local shapes on contour of the structure. According to the criterion, CHOBIE II can dissolve undesirable local shapes and transform to goal configurations. In this paper, we propose a method to realize load-adaptive motion extending the criterion. Specifically, we add two new criteria to induce desirable local shapes and to change priorities of motions depending on a load condition. We show the availability of the method by simulation results that the modular robots construct a cantilever structure avoiding overstressed states.

This study describes a group robot system “CHOBIE II”. The feature of the CHOBIE II is a function to form a reconfigurable structure. In the previous paper, we introduced a control method of the robots and realized transformation of the structure. In this paper, we focus on a motion planning method to obtain control algorithms. For this purpose, we introduce a numerical criterion for generating transformations. The criterion is expressed with matrix form of 32 parameters. This paper demonstrates the criteria generate various motions of the CHOBIE II, and proposes a new method to obtain the appropriate parameters for objective motions.