Ioonsete elektromehaaniliselt aktiivsete polümeeride deformatsioonist sõltuv elektroodi impedants

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Elektromehaaniliselt aktiivsed materjalid on polümeeridel põhinevad mitmekihilised komposiitmaterjalid, mis muudavad oma välist kuju, kui neid elektriliselt stimuleerida; tihti nimetatakse neid ka tehislihasteks. Taolistest materjalidest valmistatud täiturid pakkuvad huvi nii mikrolaborseadmetes kui ka loodust matkivas robootikas, sest võimaldavad luua keerukaid ülipisikesi ajameid. Võrreldes tavapäraste elektrimootoritega võimaldavad EAP-d (elektromehaaniliselt aktiivsed polümeerid) helitut liigutust ning neid saab lõigata konkreetse rakenduse jaoks sobivasse suurusesse. EAP-d jagunevad kahte põhiklassi: elektron- ja ioon-EAP. Doktoritöös käsitletakse kahte erinevat ioon-EAP materjali, kus mehaaniline koste on tingitud ioonide ümberpaigutumisest kolmekihilises komposiitmaterjalis. Kuna EAP-de elektromehaanilised omadused sõltuvad lisaks sisendpinge amplituudile ja sagedusele ka tugevasti ümbritseva keskkonna parameetritest (nt niiskus ja temperatuur), siis on nendest materjalidest loodud täiturite juhtimiseks tarvilik kasutada tagasisidet. Täiendav tagasisideallikas võib oma omaduste tõttu aga vähendada EAP-de rakendusvõimalusi ning seetõttu on eesmärgiks luua n-ö isetundlik EAP ajam, mis funktsioneerib samaaegselt nii täituri kui ka liigutusandurina. Doktoritööd esitatakse uuritud materjalide elektroodi impedantsi ja deformatsiooni vaheline seos ning kirjeldatakse vastav elektriline mudel. Eraldamaks andursignaali täituri sisendpingest pakutakse välja elektroodikihi piires täituri ja anduri elektriline eraldamine. Loobudes ainult elektroodimaterjalist säilitab polümeerkarkass täituri ja anduri mehaanilise ühendatuse – seega taolises süsteemis järgib sensor täituri kuju, kuigi need on elektriliselt lahti sidestatud. Elektroodimaterjali valikuliseks eemaldamiseks kasutatakse mitmeid erinevaid meetodeid (freesimine, laserablatsioon jne) ning ühtlasi uuritakse nende kasutusmugavust ja protsessi mõju kogu komposiitmaterjalile.Electromechanically active materials are polymer-based composites exhibiting mechanical deformation under electrical stimulus, i.e. they can be implemented as soft actuators in variety of devices. In comparison to conventional electromechanical actuators, their key characteristics include easy customisation, noiseless operation, straightforward mechanical design, sophisticated motion patterns, etc. Ionic EAPs (electromechanically active polymers) are one of two primary classes of electroactive materials, where actuation is caused mostly by the displacement of ions inside polymer matrix. Mechanical response of ionic EAPs is, in addition to voltage and frequency, dependent on environmental variables such as humidity and temperature. Therefore a major challenge lies in achieving controlled actuation of these materials. Due to their size and added complexity, external feedback devices inhibit the application of micro-scale actuators. Hence, self-sensing EAP actuators—capable for simultaneous actuation and sensing—are desired. In this thesis, sensing based on deformation-dependent electrochemical impedance is demonstrated and modelled for two types of trilayer ionic EAPs—ionic polymer-metal composite and carbon-polymer composite. Separating sensing signal from the input signal of the actuator is achieved by patterning the electrode layers of an IEAP material in a way that different but mechanically coupled sections for actuation and sensing are created. A variety of concepts for pattering the electrode layers (machining, laser ablation, masking, etc.) are implemented and their applicability is discussed

Concepts of learning and knowledge among first year students in Estonia

Mitmed üliõpilaste seas läbi viidud uuringud näitavad seost epistemoloogiliste uskumuste ehk teadmuskäsituse ja õpikäsituse vahel. Need uskumused ise aga tingivad selle, kuidas üliõpilased võtavad vastu erinevaid õppejõu tegevusi ja kuidas nad õpivad. Viimasel ajal levivad ka Eestis üha enam aktiivõppe meetodid, mis eeldavad üliõpilastelt teistsugust valmisolekut kui passiivne loengu vormis õppimine. Käesoleva töö eesmärk oligi kirjeldada, millised on Eesti esimese kursuse üliõpilaste epistemoloogilised uskumused ja õpikäsitus ning kuidas need mõjutavad üliõpilaste õpieelistusi. Lähtuti hüpoteesist, et Eesti esmakursuslased on pigem passiivsed, fikseeritud laadi epistemoloogiliste uskumustega ning ootavad õppejõult kui autoriteedilt nn valmisteadmisi. Seetõttu eelistavad nad töötada pigem üksi, mitte rühmas. Uurimuse hüpotees leidis üldjoontes kinnitust. Enamik uuritud esimese kursuse üliõpilastest käsitab teadmist oskuse või kogemusena. Õppimine on uute teadmiste omandamine õppejõult, kaaslastel on eelkõige vaid motiveeriv roll. Esmakursuslased eelistavad õppejõudu, kes on autoriteet ja ekspert, mistõttu oodatakse, et õppejõud tooks elulisi näiteid ja selgitaks õpitavat samm-sammult. Suur osa küsimustiku täitnud üliõpilastest on pindmise õpihoiakuga: nad mõistavad teadmist eelkõige fakti, kogemuse või oskusena ja eelistavad õppida üksi. Samal ajal on neil pluralistlikku laadi epistemoloogilised uskumused. Uuringust selgus ka, et sügava õpihoiakuga üliõpilased näevad õppimist protsessina ning teadmisi sealjuures ajas muutuvate ja arenevatena. Ainsaks kõrvalekaldeks esialgsest hüpoteesist olid üliõpilaste valdavalt pluralistlikud epistemoloogilised uskumused, mis võivad viidata ühiskonnas üldiselt levivale arusaamale, et pole ühtset tõde, vaid on palju tõlgendusi. Summar

Multimodal Grounding for Embodied AI via Augmented Reality Headsets for Natural Language Driven Task Planning

Recent advances in generative modeling have spurred a resurgence in the field of Embodied Artificial Intelligence (EAI). EAI systems typically deploy large language models to physical systems capable of interacting with their environment. In our exploration of EAI for industrial domains, we successfully demonstrate the feasibility of co-located, human-robot teaming. Specifically, we construct an experiment where an Augmented Reality (AR) headset mediates information exchange between an EAI agent and human operator for a variety of inspection tasks. To our knowledge the use of an AR headset for multimodal grounding and the application of EAI to industrial tasks are novel contributions within Embodied AI research. In addition, we highlight potential pitfalls in EAI's construction by providing quantitative and qualitative analysis on prompt robustness.Comment: 18 pages, 15 figure

TeMoto: Intuitive Multi-Range Telerobotic System with Natural Gestural and Verbal Instruction Interface

Teleoperated mobile robots, equipped with object manipulation capabilities, provide safe means for executing dangerous tasks in hazardous environments without putting humans at risk. However, mainly due to a communication delay, complex operator interfaces and insufficient Situational Awareness (SA), the task productivity of telerobots remains inferior to human workers. This paper addresses the shortcomings of telerobots by proposing a combined approach of (i) a scalable and intuitive operator interface with gestural and verbal input, (ii) improved Situational Awareness (SA) through sensor fusion according to documented best practices, (iii) integrated virtual fixtures for task simplification and minimizing the operator’s cognitive burden and (iv) integrated semiautonomous behaviors that further reduce cognitive burden and negate the impact of communication delays, execution latency and/or failures. The proposed teleoperation system, TeMoto, is implemented using ROS (Robot Operating System) to ensure hardware agnosticism, extensibility and community access. The operator’s command interface consists of a Leap Motion Controller for hand tracking, Griffin PowerMate USB as turn knob for scaling and a microphone for speech input. TeMoto is evaluated on multiple robots including two mobile manipulator platforms. In addition to standard, task-specific evaluation techniques (completion time, user studies, number of steps, etc.)—which are platform and task dependent and thus difficult to scale—this paper presents additional metrics for evaluating the user interface including task-independent criteria for measuring generalized (i) task completion efficiency and (ii) operator context switching

Electrical Model of a Carbon-Polymer Composite (CPC) Collision Detector

sensor

Self-Sensing Ionic Polymer Actuators: A Review

Ionic electromechanically active polymers (IEAP) are laminar composites that can be considered attractive candidates for soft actuators. Their outstanding properties such as low operating voltage, easy miniaturization, and noiseless operation are, however, marred by issues related to the repeatability in the production and operation of these materials. Implementing closed-loop control for IEAP actuators is a viable option for overcoming these issues. Since IEAP laminates also behave as mechanoelectrical sensors, it is advantageous to combine the actuating and sensing functionalities of a single device to create a so-called self-sensing actuator. This review article systematizes the state of the art in producing self-sensing ionic polymer actuators. The IEAPs discussed in this paper are conducting (or conjugated) polymers actuators (CPA), ionic polymer-metal composite (IPMC), and carbonaceous polymer laminates

Fast Adaptation of Manipulator Trajectories to Task Perturbation by Differentiating through the Optimal Solution

Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. Most importantly, however, our algorithm achieves a worst-case speed-up of 160x over the latter approach