Successful coordination of prehensile action depends upon the selection and control of appropriate reach and grasp movements. This thesis explores how prehensile actions are shaped and regulated by perceptual information. According to ecological psychology, behaviour is achieved through the detection of information specifying the opportunities the environment affords for action. A review of the literature identified that as this information evolves over time, a comprehensive understanding of prehension has to consider how affordance perception and continual guidance of action come together in the pursuit of goal-directed action. In a series of interlinking studies the initiation, hand transport and grasp components of prehension were investigated in order to determine how affordances are manifested in the emergence of, and guidance within, prehensile actions. Study 1 explored the effect of information specifying affordances on the time taken to initiate and perform a ball-posting action. Results indicated that affordance perception was reflected in initiation time, whilst affordance actualisation was reflected in movement time, demonstrating that effects of affordance perception extend beyond action preparation to directly influence the emergence of action. Study 2 investigated the selection and regulation of perceptual information during the guidance of hand transport. General tau theory (Lee, 1998) was applied to test i) whether the ratio of coupling between hand and object motion is held constant throughout the reach, and ii) whether this ratio, k, describes the kinematics of handobject contact under varying spatiotemporal task constraints. Results indicated a constant tau ratio during the middle, but not the end phase of the movement; moreover, although the summary ratio k was not sensitive to task manipulations, the time-dependent counterpart, K(t), did exhibit effects of task constraints. This indicates that the guidance of hand transport was a continuous process, where, dependent upon the task goals, the regulation of perceptual information changed throughout the action. The final study, Study 3, examined digit coordination during the grasp. Focusing on the transition from 2-digit to 3-digit grip configurations, the study addressed whether grip selection is made before or during the action. Results showed the transition between 2- and 3-digit grips occurs at a within trial level. The grip configuration utilised could only be distinguished as 2- or 3-digit during the second half of the movement, indicating that grip selection emerges online during the unfolding action. Together these studies provide evidence of continual guidance of prehensile actions and offer support for the consideration of prehensile action as a set of nested task goals. It was concluded that affordance perception and movement guidance are interrelated and evolve continuously throughout the unfolding prehensile action. In light of these findings, issues relating to action selection emerging online from the organism-environment interaction are discussed.

Grasping, the skill to hold objects and tools while doing in-hand manipulation, still is in many cases an unsolvable problem for robotics, but a natural act for humans. An efficient grasping requires not only human-like robotic hands with articulated fingers but also tactile, force, and kinesthetic sensors for the precise control of the forces and motions exerted during the manipulation. As a fully autonomous robotic dexterous manipulation is too difficult to develop for changing and unstructured environments, an alternative approach is to combine the low-level robot computer control with the higher-level perception and task planning abilities of a human operator equipped with an adequate human-computer interface (HCI). This thesis presents theoretical and experimental contributions to the development of an upgraded haptic-enabled anthropomorphic Ring Ada dexterous robotic hand and a biology-inspired synergistic real-time control system for teleoperated grasping of different objects using a CyberTouch HCI data glove. A fuzzy logic controller module was developed to efficiently control the underactuated Ring Ada’ robotic hand during grasping. A machine learning classification system was developed to recognize grasped objects. Experiments have convincingly demonstrated that our novel Ring Ada robotic hand equipped with kinematic position sensors and touch sensors is able to efficiently grasp different lightweight objects through teleoperation.

This thesis investigates the passive magnetic latching mechanism designs for autonomous aerial grasping and programmable self-assembly. The enormous latching potential of neodymium magnets is a well-established fact when it comes to their ability to interact with ferrous surfaces in particular. The force of attraction or repulsion among the magnets is strong enough to keep the levitation trains, and high speed transportation pods off the rails. But such utilization of these desirable magnetic properties in commercial applications, comes at a cost of high power consumption since the magnets used are usually electromagnets. On the other hand, we explore some useful robotic applications of passive (and hence low cost) magnetic latching; which are of vital importance in autonomous aerial transportation, automated drone-based package deliveries, and programmable self-assembly and self-reconfigurable systems. We propose, and implement a novel, attach/detach mechatronic mechanism, based on passive magnetic latching of permanent magnets for usBots; our indige- nously built programmable self-assembly robots, and show that it validates the game theoretic self-assembly algorithms. Another application addressed in this thesis is the utilization of permanent magnets in autonomous aerial grasping for Unmanned Aerial Vehicles (UAVs). We present a novel gripper design for ferrous objects with a passive magnetic pick up and an impulse based drop. For both the applications, we highlight the importance, simplicity and effectiveness of the proposed designs while providing a brief comparison with the other technologies out there.

In recent years, the field of Soft Robotics has grown exponentially resulting in a variety of different soft robot designs. A majority of the current soft robots can easily be split into two distinct categories: Navigation and Grasping. Navigation robots alter their body orientation to navigate around an environment. Grasping robots are designed to grasp a variety of unknown objects without damaging said object. However, only a few robots are able to demonstrate both aspects and even fewer robots are able to do both simultaneously. As thus, the goal of this thesis is to create a soft robot that is able to pick up and support an additional payload. This thesis will explore the challenges and difficulties that come with designing such a robot. For this thesis, we chose to simplify the manufacturing process making it easy to create and test different designs. We primarily used Pneumatic Network actuators for the majority of the soft robot. This allowed us to use a layered manufacturing approach to create the full robot. Finally, we split the robot into two main components which have their own purpose, which made it easy to test and design each component. Attached to this thesis are three different supplementary videos. The first one labeled "Walking Gaits" demonstrate how the robot is capable of moving forward. This video is comprised of several sections showing the full robot moving, just the base moving, and the full robot briefly moving as it supports a payload. The second video is labeled "Additional Walking". This video shows how the base can effectively move around a given environment. The final video if called "Grasping Method" which demonstrates the different grasping methods that the full robot uses to pick up objects. / 2021-06-03T00:00:00Z

Automated grasping of objects of unknown geometry a priori has applications in many industries such as clearing a mine shaft after blasting, agricultural applications such as fruit and vegetable handling, and many roles in the service industry such as fetching items for a handicapped individual. In these roles the system environment is highly unstructured, and the robot must be able to react to different types of objects needing to be grasped. In this thesis a vision guided robotic grasp planner for unstructured environments is presented. An evaluation method for robotic grasping involving two distinct sets of objects is also presented. Both the grasp planner and evaluation metric are evaluated by experimentation using an articulated robotic arm with an eye-in-hand video camera, line laser, and pneumatic gripper. Multiple grasping experiments were performed with the objects in random poses on a modified tabletop deemed the playfield that did not allow objects to rest flat. The grasp planner focused on using a created model of the object from camera observations using silhouetting and line laser data. The object model and its computed convex hull were used to evaluate and select a single facet and point creating a grasping pair for the pneumatic gripper jaws. The grasp was attempted and then evaluated using a secondary camera and the developed evaluation method. iv Grasp success rates ranged from 80.30% (Rectangular Block on playfield 137 attempts) to 97.69% (Hexagonal Nut 173 attempts), with a mean grasp computation time for the hexagonal nut of 0.57s. / Master of Applied Science (MASc)

<p dir="ltr">This dissertation presents the findings and research conducted during my Ph.D. study, which focuses on developing grasp policies for modular end-effectors on mobile manipulation platforms operating in cluttered environments. The primary objective of this research is to enhance the performance and accuracy of robotic manipulation systems in complex, real-world scenarios. The work has potential implications for various domains, including the rapidly growing Industry 4.0 and the advancement of autonomous systems in space habitats.</p><p dir="ltr">The dissertation offers a comprehensive literature review, emphasizing the challenges faced by mobile manipulation platforms in cluttered environments and the state-of-the-art techniques for grasping and manipulation. It showcases the development and evaluation of a Modular End-Effector System (MEES) for mobile manipulation platforms, which includes the investigation of object 6D pose estimation techniques, the generation of a deep learning-based grasping dataset for MEES, the development of a suction cup gripper grasping policy (Sim-Suction), the development of a two-finger grasping policy (Sim-Grasp), and the integration of Modular End-Effector System grasping policy (Sim-MEES). The proposed methodology integrates hardware designs, control algorithms, data-driven methods, and large language models to facilitate adaptive grasping strategies that consider the unique constraints and requirements of cluttered environments.</p><p dir="ltr">Furthermore, the dissertation discusses future research directions, such as further investigating the Modular End-Effector System grasping policy. This Ph.D. study aims to contribute to the advancement of robotic manipulation technology, ultimately enabling more versatile and robust mobile manipulation platforms capable of effectively interacting with complex environments.</p>

The focus of the present work is natural human like grasping, for realistic performance simulations in digital human modelling (DHM) environment. The performance simulation for grasping in DHM is typically done through high level commands to the digital human models (DHMs). This calls for a natural and unambiguous scheme to describe a grasp which would implicitly accommodate variations due to the hand form, object form and hand kinematics. A novel relational description scheme is developed towards this purpose. The grasp is modelled as a spatio-temporal relationship between the patches (a closed region on the surface) in the hand and the object. The task dependency of the grasp affects only the choice of the relevant patches. Thus, the present scheme of grasp description enables a human like grasp description possible. Grasping can be simulated either in an interactive command mode as discussed above or in an autonomous mode. In the autonomous mode the patches have to be computed. It is done using a psychological concept, of affordance. This scheme is employed to select a tool from a set of tools. Various types of grasps a user may adopt while grasping a spanner for manipulating a nut is simulated. Grasping of objects by human evolves through distinct naturally occurring phases, such as re-oreintation, transport and preshape. Hand is taken to the object ballpark using a novel concept of virtual object. Before contact establishment hand achieves the shape similar to the global shape of the object, called preshaping. Various hand preshape strategies are simulating using an optimization scheme. Since the focus of the present work is human like grasping, the mechanism which drives the DHMs should also be anatomically pertinent. A methodology is developed wherein the hand-object contact establishment is done based on the anatomical observation of logarithmic spiral pattern during finger flexion. The effect of slip in presence of friction has been studied for 2D and 3D object grasping endeavours and a computational generation of the slip locus is done. The in-grasp slip studies are also done which simulates the finger and object response to slip. It is desirable that the grasping performance simulations be validated for diverse hands that people have. In the absence of an available database of articulated bio-fidelic digital hands, this work develops a semi-automatic methodology for developing subject specific hand models from a single pose 3D laser scan of the subject's hand. The methodology is based on the clinical evidence that creases and joint locations on human hand are strongly correlated. The hand scan is segmented into palm, wrist and phalanges, both manually and computationally. The computational segmentation is based on the crease markings in the hand scan, which is identified by explicitly painting them using a mesh processing software by the user. Joint locations are computed on this segmented hand. A 24 dof kinematic structure is automatically embedded into the hand scan. The joint axes are computed using a novel palm plane normal concept. The computed joint axes are rectified using the convergence, and intra-finger constraints. The methodology is significantly tolerant to the noise in the scan and the pose of the hand. With the proposed methodology articulated, realistic, custom hand models can be generated. Thus, the reported work presents a geometric framework for comprehensive simulation of grasping performance in a DHM environment.

In daily activities, hand use is dominated by individuated thumb and finger movements, and by grasping. This thesis focused on the level of ???independence??? of the digits and its relationship to hand grasps, from the level of the motor units to the level of synergistic grasping forces. Four major studies were conducted in healthy adult volunteers. First, spike-triggered averages of forces produced by single motor units in flexor pollicis longus (FPL) in a grasp posture showed small but significant loading of the index, but not other fingers. This reflected a neural rather than anatomical coupling, as intramuscular stimulation produced minimal effect in any finger. Also, FPL had a surprisingly large number of low-force motor units and this may account for the thumb???s exceptional dexterity and force stability compared with the fingers. Second, independent control of extensor digitorum (ED) was more limited than flexor digitorum profundus (FDP), as more ED motor units of a ???test??? finger were recruited inadvertently by extension than by flexion of adjacent digits. Third, ???force enslavement??? in maximal voluntary tasks was greater in digit extension than flexion. The distribution of force enslavement (and deficits) matched the pattern of daily use of the digits (alone and in combination), and reveals a neural control system which preferentially lifts fingers together from an object by extension but allows an individual digit to flex to contact an object so the finger pads can engage in exploration and grasping. Finally, during grasping, irrespective of whether a digit had been lifted from the object, coherence among forces generated by the digits was similar. In addition, the coherence between finger forces was independent of any contraction of the thumb, was stable over 2 months, and required no learning. The pattern of coherence between digital grasping forces may be closely related to the level of digit independence and daily use. Overall, the grasp synergy was remarkably invariant over the various tasks and over time. In summary, this thesis demonstrates novel aspects of the properties of FPL, the lack of complete independence of the digits, and robustness in the production of flexion forces in hand grasps.

Master of Science / Department of Computing and Information Sciences / David A. Gustafson / Octant analysis, when combined with properties of the multivariate central limit theorem and multivariate normal distribution, allows finding a reasonable grasping point on an unknown novel object possible. This thesis’s original contribution is the ability to find progressively improving grasp points in a poor and/or sparse point cloud. It is shown how octant analysis was implemented using common consumer grade electronics to demonstrate the applicability to home and office robotics. Tests were carried out on three novel objects in multiple poses to determine the algorithm’s consistency and effectiveness at finding a grasp point on those objects. Results from the experiments bolster the idea that the application of octant analysis to the grasping point problem seems promising and deserving of further investigation. Other applications of the technique are also briefly considered.

The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced sensors (i.e. without relying on an inverse model) can enable the robots to solve such complex tasks. Another objective of these experiments is to investigate whether the Evolutionary Robotics approach can be successfully applied to scenarios that are significantly more complex than those to which it is typically applied (in terms of the complexity of the robot’s morphology, the size of the neural controller, and the complexity of the task). The obtained results indicate that skills such as reaching, grasping, and discriminating among objects can be accomplished without the need to learn precise inverse internal models of the arm/hand structure. This would also support the hypothesis that the human central nervous system (cns) does necessarily have internal models of the limbs (not excluding the fact that it might possess such models for other purposes), but can act by shifting the equilibrium points/cycles of the underlying musculoskeletal system. Consequently, the resulting controllers of such fundamental skills would be less complex. Thus, the learning of more complex behaviours will be easier to design because the underlying controller of the arm/hand structure is less complex. Moreover, the obtained results also show how evolved robots exploit sensory-motor coordination in order to accomplish their tasks.