A soft, sensorized gripper for delicate harvesting of small fruits

Harvesting fruits and vegetables is a complex task worth to be fully automated with robotic systems. It involves several precision tasks that have to be performed with accuracy and the appropriate amount of force. Classical mechanical grippers, due to the complex control and stiffness, cannot always be used to harvest fruits and vegetables. Instead, the use of soft materials could provide a visible advancement. In this work, we propose a soft, sensorized gripper for harvesting applications. The sensing is performed by tracking a set of markers integrated into the soft part of the gripper. Different machine learning-based approaches have been used to map the markers’ position and dimensions into forces in order to perform a close-loop control of the gripper. Results show that force can be measured with an error of 2.6% in a range from 0 to 4 N. The gripper was integrated into a robotic arm having an external vision system used to detect plants and fruits (strawberries in our case scenario). As a proof of concept, we evaluated the performance of the robotic system in a laboratory scenario. Plant and fruit identification reached a positive rate of 98.2% and 92.4%, respectively, while the correct picking of the fruits, by removing it from the stalk without a direct cut, achieved an 82% of successful rate

Actuation Technologies for Soft Robot Grippers and Manipulators: A Review

Purpose of Review The new paradigm of soft robotics has been widely developed in the international robotics community. These robots being soft can be used in applications where delicate yet effective interaction is necessary. Soft grippers and manipulators are important, and their actuation is a fundamental area of study. The main purpose of this work is to provide readers with fast references to actuation technologies for soft robotic grippers in relation to their intended application. Recent Findings The authors have surveyed recent findings on actuation technologies for soft grippers. They presented six major kinds of technologies which are either used independently for actuation or in combination, e.g., pneumatic actuation combined with electro-adhesion, for certain applications. Summary A review on the latest actuation technologies for soft grippers and manipulators is presented. Readers will get a guide on the various methods of technology utilization based on the application

Pneumatic Hyperelastic Robotic End-Effector for Grasping Soft Curved Organic Objects

Pneumatically-driven soft robotic grippers can elastically deform to grasp delicate, curved organic objects with minimal surface damage. However, common actuators have complex geometries and are fabricated with ultra-soft hyperelastic elastomers not originally intended for scientific applications. The complexity of the actuator geometry and extreme nonlinearity of their material’s stress-strain behaviour make it difficult to predict the actuator’s deformation prior to experimentation. In this work, a compact soft pneumatic gripper made with polydimethylsiloxane (PDMS) is developed for grasping delicate organic objects, analyzed through computational modelling and experimentally validated. COMSOL Multiphysics is used to simulate the impact of geometrical parameters on the actuator’s behaviour, allowing for the refinement of the proposed geometry prior to fabrication. Optimal parameters are selected for fabrication, with experimental tests matching simulations within ± 1 mm. Gripper performance is evaluated for three actuator wall thicknesses in terms of contact area with target, contact force, and maximum payload before slippage. The comparative assessment between simulations and experiments demonstrate that the proposed soft actuators can be used in robotic grippers tailored for grasping delicate objects without damaging their surface. Furthermore, analysis of the actuators provides additional insight on how to design simple but effective soft systems

Innovative robot hand designs of reduced complexity for dexterous manipulation

This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects. Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension. Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty. In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness. Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm. In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces

DEVELOPMENT OF A SOFT PNEUMATIC ACTUATOR FOR MODULAR ROBOTIC MECHANISMS

Soft robotics is a widely and rapidly growing field of research today. Soft pneumatic actuators, as a fundamental element in soft robotics, have gained huge popularity and are being employed for the development of soft robots. During the last decade, a variety of hyper-elastic robotic systems have been realized. As the name suggests, such robots are made up of soft materials, and do not have any underlying rigid mechanical structure. These robots are actuated employing various methods like pneumatic, electroactive, jamming etc. Generally, in order to achieve a desired mechanical response to produce required actuation or manipulation, two or more materials having different stiffness are utilized to develop a soft robot. However, this method introduces complications in the fabrication process as well as in further design flexibility and modifications. The current work presents a design scheme of a soft robotic actuator adapting an easier fabrication approach, which is economical and environment friendly as well. The purpose is the realization of a soft pneumatic actuator having functional ability to produce effective actuation, and which is further employable to develop modular and scalable mechanisms. That infers to scrutinize the profile and orientation of the internal actuation cavity and the outer shape of viii the actuator. Utilization of a single material for this actuator has been considered to make this design scheme convenient. A commercial silicone rubber was selected which served for an economical process both in terms of the cost as well as its accommodating fabrication process through molding. In order to obtain the material behavior, \u2018Ansys Workbench 17.1 R \u2019 has been used. Cubic outline for the actuator aided towards the realization of a body shape which can easily be engaged for the development of modular mechanisms employing multiple units. This outer body shape further facilitates to achieve the stability and portability of the actuator. The soft actuator has been named \u2018Soft Cubic Module\u2019 based on its external cubic shape. For the internal actuation cavity design, various shapes, such as spherical, elliptical and cylindrical, were examined considering their different sizes and orientations within the cubic module. These internal cavities were simulated in order to achieve single degree of freedom actuation. That means, only one face of the cube is principally required to produce effective deformation. \u2018Creo Perametric 3.0 M 130\u2019 has been used to design the model and to evaluate the performance of actuation cavities in terms of effective deformation and the resulting von-mises stress. Out of the simulated profiles, cylindrical cavity with desired outcomes has been further considered to design the soft actuator. \u2018Ansys Workbench 17.1 R \u2019 environment was further used to assess the performance of cylindrical actuation cavity. Evaluation in two different simulation environments helped to validate the initially achieved results. The developed soft cubic actuator was then employed to develop different mechanisms in a single unit configuration as well as multi-unit robotic system developments. This design scheme is considered as the first tool to investigate its capacity to perform certain given tasks in various configurations. Alongside its application as a single unit gripper and a two unit bio-mimetic crawling mechanism, this soft actuator has been employed to realize a four degree ix of freedom robotic mechanism. The formation of this primitive soft robotic four axis mechanism is being further considered to develop an equivalent mechanism similar to the well known Stewart platform, with advantages of compactness, simpler kinematics design, easier control, and lesser cost. Overall, the accomplished results indicate that the design scheme of Soft Cubic Module is helpful in realizing a simple and cost-effective soft pneumatic actuator which is modular and scalable. Another favourable point of this scheme is the use of a single material with convenient fabrication and handling

Grasping Motion for Small Non-Rigid Food Using Instance Semantic Segmentation

The importance of food automation becomes a challenge in this era since food is an essential factor for humans. In this paper, we designed the robot framework to autonomously generate grasping motion for non-rigid food objects. The system could recognize and localize objects and target regions for pick-and-place rather than only the position. Assembling a lunch box with different food required advance in both hardware and software to create an efficient process. The robot platform based on a seven-axis industrial robot arm equipped with instance segmentation based on Cascade Mask R-CNN for Japanese food. A modular end effector was designed and prototyped to facilitate soft gripper and vacuum pad on the single unit, which allows the system to handle different food objects. In the experiment, we also evaluated the performance of pick-and-place process. The system can successfully pick-and-place food into a lunch box with an outcome successful rate of 90%.The Society of Instrument and Control Engineers (SICE) Annual Conference 2020 (SICE2020), September 23-26, 2020, Chiang Mai, Thailand （新型コロナ感染拡大に伴い、現地開催中止

Investigating pre-touch sensing to predict grip success in compliant grippers using machine learning techniques

This work explores the application of pre-touch sensing to a compliant gripper in order to navigate the last few centimeters while grasping fruit in an occluded, cluttered environment. Machine learning was used in conjunction with pre-touch sensors to provide qualitative feedback about the success of the gripper in picking the target fruit prior to contact. Three compliant grippers were each designed to pick a specific fruit (miracle berries, cherry tomatoes and small figs) without damaging them. These grippers were designed to be mounted on the hybrid soft-rigid arm of a mobile field robot. An IR reflectance, time of flight and color sensor were used as pre-touch sensors and arranged on the gripper in various combinations to explore the contribution of each sensor. The gripper-sensor system was trained by positioning it relative to a dummy fruit using a 6 DOF arm and gripping the target. Using the training data, five machine learning methods were explored: nearest neighbor, decision trees, support vector machines, multi-layer perceptrons and a naive Bayes classifier. The various sensor configuration-machine learning combinations were tested and evaluated based on their ability to predict grip success. Additional training was conducted to demonstrate the ability to differentiate fruit from foreign matter (e.g. leaves) that are in the gripper opening. Time of flight sensors using nearest neighbor and support vector machines along with the set of all three sensors using support vector machines and multi-layer perceptrons showed the highest prediction precision (= 90%) with the color sensor playing a key role in detecting foreign objects. The machine learning methods were similar in their ability to predict grip success with nearest neighbor showing the best overall results, while sensor ‘richness’ play an important role in differentiating the sensors with the three sensor combination showing the best results

The Jamming Donut: A Free-Space Gripper based on Granular Jamming

Fruit harvesting has recently experienced a shift towards soft grippers that possess compliance, adaptability, and delicacy. In this context, pneumatic grippers are popular, due to provision of high deformability and compliance, however they typically possess limited grip strength. Jamming possesses strong grip capability, however has limited deformability and often requires the object to be pushed onto a surface to attain a grip. This paper describes a hybrid gripper combining pneumatics (for deformation) and jamming (for grip strength). Our gripper utilises a torus (donut) structure with two chambers controlled by pneumatic and vacuum pressure respectively, to conform around a target object. The gripper displays good adaptability, exploiting pneumatics to mould to the shape of the target object where jamming can be successfully harnessed to grip. The main contribution of the paper is design, fabrication, and characterisation of the first hybrid gripper that can use granular jamming in free space, achieving significantly larger retention forces compared to pure pneumatics. We test our gripper on a range of different sizes and shapes, as well as picking a broad range of real fruit

Experimental Evaluation of Tactile Sensors for Compliant Robotic Hands

The sense of touch is a key aspect in the human capability to robustly grasp and manipulate a wide variety of objects. Despite many years of development, there is still no preferred solution for tactile sensing in robotic hands: multiple technologies are available, each one with different benefits depending on the application. This study compares the performance of different tactile sensors mounted on the variable stiffness gripper CLASH 2F, including three commercial sensors: a single taxel sensor from the companies Tacterion and Kinfinity, the Robotic Finger Sensor v2 from Sparkfun, plus a self-built resistive 3 × 3 sensor array, and two self-built magnetic 3-DoF touch sensors, one with four taxels and one with one taxel. We verify the minimal force detectable by the sensors, test if slip detection is possible with the available taxels on each sensor, and use the sensors for edge detection to obtain the orientation of the grasped object. To evaluate the benefits obtained with each technology and to assess which sensor fits better the control loop in a variable stiffness hand, we use the CLASH gripper to grasp fruits and vegetables following a published benchmark for pick and place operations. To facilitate the repetition of tests, the CLASH hand is endowed with tactile buttons that ease human–robot interactions, including execution of a predefined program, resetting errors, or commanding the full robot to move in gravity compensation mode