Electrostatic Brakes Enable Individual Joint Control of Underactuated, Highly Articulated Robots

Highly articulated organisms serve as blueprints for incredibly dexterous mechanisms, but building similarly capable robotic counterparts has been hindered by the difficulties of developing electromechanical actuators with both the high strength and compactness of biological muscle. We develop a stackable electrostatic brake that has comparable specific tension and weight to that of muscles and integrate it into a robotic joint. Compared to electromechanical motors, our brake-equipped joint is four times lighter and one thousand times more power efficient while exerting similar holding torques. Our joint design enables a ten degree-of-freedom robot equipped with only one motor to manipulate multiple objects simultaneously. We also show that the use of brakes allows a two-fingered robot to perform in-hand re-positioning of an object 45% more quickly and with 53% lower positioning error than without brakes. Relative to fully actuated robots, our findings suggest that robots equipped with such electrostatic brakes will have lower weight, volume, and power consumption yet retain the ability to reach arbitrary joint configurations.Comment: 17 pages, 15 figure

Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation

During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates.Comment: 8 pages, 6 figure

Motion Planning for Socially Competent Robot Navigation

Crowded human environments such as pedestrian scenes constitute challenging domains for mobile robot navigation, for a variety of reasons including the heterogeneity of pedestrians’ decision-making mechanisms, the lack of channels of explicit communication among them and the lack of universal rules or social conventions regulating traffic. Despite these complications, humans exhibit socially competent navigation through coordination, realized with implicit communication via a variety of modalities such as path shape and body posture. Sophisticated mechanisms of inference and decision-making allow them to understand subtle communication signals and encode them into their own actions. Although the problem of planning socially competent robot navigation has received significant attention over the past three decades, state-of-the-art approaches tend to explicitly focus on reproducing selected social norms or directly imitating observed human behaviors, while often lack of extensive and thorough validation procedures, thus raising questions about their generalization and reproducibility. This thesis introduces a family of planning algorithms, inspired by studies on human navigation. Our algorithms are designed to produce socially competent robot navigation behaviors by leveraging the existing mechanisms of implicit coordination in humans. We model multi-agent motion coordination through a series of data structures, based on mathematical abstractions from low-dimensional topology and physics, that capture fundamental properties of multi-agent collision avoidance. These models enable a robot to anticipate the effects of its actions on the inference and decision-making processes of nearby agents and allow for the generation of motion that is compliant with the unfolding evolution of the scene and consistent with the robot’s intentions. The introduced planning algorithms are supported by extensive simulated and experimental validation. Key findings include: (1) evidence extracted from a series of simulated studies, suggesting that the outlined planning architecture indeed results in effective coordination within groups of non-communicating agents in a variety of simulated scenarios; (2) evidence extracted from an online, video-based user study with more than 180 participants, indicating that humans perceive the motion generated by our framework as intent-expressive; (3) evidence extracted from an experimental study, conducted in a controlled lab environment with 105 human participants, suggesting that humans follow low-acceleration paths when navigating next to a robot running our framework

Grasp Synthesis Algorithms for Multifingered Robot Hands

95 σ.Η ανάπτυξη σύνθετων, ανθρωπομορφικών, επιδέξιων ρομποτικών χεριών, με στόχο είτε την ενσωμάτωσή τους σε ρομπότ οικιακής και βιομηχανικής χρήσης, είτε ακόμα την τοποθέτησή τους σε ανθρώπους με αναπηρίες έχει φέρει το πρόβλημα της λαβής αντικειμένων από ρομποτικά χέρια στο προσκήνιο της σύγχρονης ρομποτικής. Πρόκειται για ένα πολυπαραμετρικό πρόβλημα κατά το οποίο το μηχανικό σύστημα (ρομποτικό χέρι) αλληλεπιδρά με το φυσικό περιβάλλον, προκειμένου να εκτελέσει μια επιθυμητή εργασία/χειρισμό. Συνεπώς, προκύπτει η ανάγκη για ανάπτυξη αλγορίθμων που δεδομένων όλων των απαραίτητων πληροφοριών, θα υπολογίζουν όλες τις παραμέτρους μιας επιτυχούς λαβής, λαμβάνοντας υπόψη τους περιορισμούς που επιβάλλονται από την κατασκευή του χεριού αλλά και από τον περιβάλλοντα χώρο, με τελικό στόχο την ικανοποίηση των προδιαγραφών που έχουν τεθεί για τη συγκεκριμένη λαβή. Σε αυτή την διπλωματική εργασία, ερευνάται το πρόβλημα του υπολογισμού της βέλτιστης λαβής ως προς διάφορες πτυχές, διασφαλίζοντας ότι ικανοποιούνται οι προαναφερθέντες περιορισμοί. Το πρόβλημα αντιμετωπίζεται για δύο διαφορετικούς τύπους ρομποτικού χεριού. Συγκεκριμένα, αλγόριθμοι βελτιστοποίησης έχουν αναπτυχθεί για την περίπτωση i) ενός ρομποτικού χεριού δεκαπέντε βαθμών ελευθερίας και ii) ένός συνεργιστικού υποεπενεργούμενου ρομποτικού χεριού που η κίνησή του διέπεται από τους ίδιους κανόνες με ένα ανθρώπινο χέρι. Και για τις δύο περιπτώσεις, ως κινηματικό μοντέλο έχει ληφθεί αυτό του ρομποτικού χεριού \textlatin{DLR}/\textlatin{HIT} ΙΙ. Έμφαση έχει δοθεί στην ελαχιστοποίηση των δυνάμεων επαφής των δακτύλων, προκειμένου να ελαχιστοποιηθεί η ενεργειακή κατανάλωση και επίσης να διασφαλιστεί η ακεραιότητα του αντικειμένου που μας ενδιαφέρει. Επιπλέον, κριτήρια που αφορούν την ικανότητα του μηχανισμού του χεριού να παράγει τις απαιτούμενες δυνάμεις, καθώς επίσης και τη συμβατότητα της λαβής με τον επιθυμητό επακόλουθο χειρισμό/εργασία έχουν επίσης συμπεριληφθεί στην παρούσα ανάλυση. Ειδικότερα, μετά τη μοντελοποίηση της προς εκτέλεση από το ρομποτικό χέρι εργασίας, οι αλγόριθμοι που προτείνονται οδηγούν σε υιοθέτηση από το χέρι συγκεκριμένης κινηματικής κατάστασης που ευνοεί την εκτέλεση της εργασίας. Τέλος, προτείνεται ένας αλγόριθμος που στοχεύει στη βελτίωση της ποιότητας της λαβής ενός αντικειμένου σε πραγματικό χρόνο, μετά από αξιολόγηση πληροφοριών που παρέχονται από αισθητήρες αφής, όρασης και μέτρησης δύναμης. Η αποτελεσματικότητα όλων των αναπτυχθέντων αλγορίθμων βελτιστοποίησης ελέγχεται και αποτυπώνεται μέσω μιας μελέτης προσομοίωσης. Τρισδιάστατες εικόνες που αναπαριστούν τα αποτελέσματα των διαφόρων περιπτώσεων που εξετάζονται, παρατίθενται μαζί με διαγράμματα που αφορούν τη σύγκλιση των υιοθετηθέντων κριτηρίων.The development of complex, human-like, multi-fingered robot hands, aiming at being incorporated in household robotics, prosthetics or even in industrial applications and space has brought the problem of grasping in the spotlight of modern robotics research. Grasping is a multiparametric problem during which the mechanical system (robot hand) interacts with the physical environment in order to perform a manipulation task. Therefore, there arises the need for the development of algorithms that, given sufficient information, produce successful grasps, taking into consideration the constraints imposed by the mechanical structure of the hand and also by the structure of the surrounding environment, aiming at satisfying the grasping task's requirements. In this diploma thesis, the problem of deriving optimal grasps with respect to different aspects of Grasp Quality is addressed, ensuring that the aforementioned constraints are satisfied. The study conducted involves different approaches of this problem. In particular, optimization schemes are developed for the case of i) a robot hand with 15 actuated DOFs and ii) a hypothetical synergistic underactuated hand. For both, the kinematic model of the DLR/HIT II five-fingered robot hand has been considered. Emphasis is given to the grasping force minimization in order to derive grasps consuming the least possible amount of power and also ensure that the grasped object does not break. Besides, criteria concerning the ability of the hand's mechanism to produce the required forces and the task compatibility of a certain grasp have also been considered. Upon modeling the desired task that needs to be executed by the robot hand, our optimization schemes lead to hand's postures that favor its execution. Finally, an algorithm that aims at improving the Grasp Quality in real-time mode, after assessing information provided by tactile/force/vision sensors, is developed. The efficiency of all the developed optimization schemes and algorithms is clarified through a simulation study for the model of the DLR/HIT II five-fingered robot hand. 3D plots representing the simulation results of various cases are provided, along with diagrams concerning the convergence of the criteria adopted.Χριστόφορος Ι. Μαυρογιάννη