Modification of Gesture-Determined-Dynamic Function with Consideration of Margins for Motion Planning of Humanoid Robots

The gesture-determined-dynamic function (GDDF) offers an effective way to handle the control problems of humanoid robots. Specifically, GDDF is utilized to constrain the movements of dual arms of humanoid robots and steer specific gestures to conduct demanding tasks under certain conditions. However, there is still a deficiency in this scheme. Through experiments, we found that the joints of the dual arms, which can be regarded as the redundant manipulators, could exceed their limits slightly at the joint angle level. The performance straightly depends on the parameters designed beforehand for the GDDF, which causes a lack of adaptability to the practical applications of this method. In this paper, a modified scheme of GDDF with consideration of margins (MGDDF) is proposed. This MGDDF scheme is based on quadratic programming (QP) framework, which is widely applied to solving the redundancy resolution problems of robot arms. Moreover, three margins are introduced in the proposed MGDDF scheme to avoid joint limits. With consideration of these margins, the joints of manipulators of the humanoid robots will not exceed their limits, and the potential damages which might be caused by exceeding limits will be completely avoided. Computer simulations conducted on MATLAB further verify the feasibility and superiority of the proposed MGDDF scheme

Task planning based on the interpretation of spatial structures

U ovom istraživanju razvijen je novi algoritam planiranja za transformaciju početnog neuređenog stanja objekata u uređeno konačno stanje. Zadatak algoritma planiranja je pronaći mogući niz djelovanja kojima se početno stanje okoline, kroz konačan broj diskretnih transformacija, može dovesti u zadano konačno stanje. Stanje okoline tumači se kroz položaj i orijentaciju objekata. Zadatak planiranja rješava se u dva koraka. Razvijena je konstruktivna heuristika pomoću koje se dobiva početni skup rješenja. Konstruktivna heuristika koristi mutacije za generiranje početne populacije. Genetski algoritam je razvijen za optimizaciju početnog skupa rješenja. Genetski algoritam karakteriziran je usporednom evolucijskom strategijom za pronalaženje rješenja, s ciljem prostorne pretvorbe neuređenog stanja objekata u uređeno, ograničen na dvodimenzionalnu interpretaciju radnog prostora. Verifikacija algoritma planiranja napravljena je u virtualnom okruženju.In this research, a new task planning algorithm is developed for building a desired object configuration from a given initial unordered object state. The task of the planning algorithm is to find a feasible set of actions, i.e. a finite number of discrete transformations, which can rearrange the objects into a desired ordered final state. The environment is interpreted through the position and orientation of the objects. The solution to the planning problem is proposed as a two-step method. First, a constructive heuristic generates an initial set of good solutions. The constructive heuristic uses only mutations for making an initial population of state transitions. A genetic algorithm is developed for optimizing the initial set of solutions. The genetic algorithm is characterized by a parallel evolutionary strategy, with the aim of spatial transformation of unordered object states into ordered object states. The algorithm can be used for solving the task planning problems represented in the two-dimensional space. Verification of the planning algorithm is done in a virtual environment

A Two-Arm Situated Artificial Communicator for Human–Robot Cooperative Assembly

Abstract—We present the development of a robot system with some cognitive capabilities, as well as experimental results. We focus on two topics: assembly by two hands and understanding human instructions in nonconstrained natural language. These two features distinguish human beings from animals, and are, thus, the means leading to high-level intelligence. A typical application of such a system is a human–robot cooperative assembly. A human communicator sharing a view of the assembly scenario with the robot instructs the latter by speaking to it in the same way that he would communicate with a child whose common-sense knowledge is limited. His instructions can be underspecified, incomplete, and/or context dependent. After introducing the general purpose of our research project, we present the hardware and software components of our robots needed for interactive assembly tasks. We then discuss the control architecture of the robot system with two stationary robot arms by describing the functionalities of perception, instruction understanding, and execution. To show how our robot learns from humans, the implementations of a layered learning methodology, memory, and monitoring functions are introduced. Finally, we outline a list of future research topics related to the enhancement of such systems. Index Terms—Cognitive science, cooperative systems, learning control systems, multiple manipulators, natural language interfaces

Planiranje robotskog djelovanja zasnovano na tumačenju prostornih struktura

Robot je programabilan mehanizam čije se djelovanje temelji na upravljačkim algoritmima. Prilikom rada u nestrukturiranoj okolini upravljački algoritmi postaju eksplicitne funkcije položaja i vremena u povratnoj vezi sa stanjem okoline. Obradu podataka iz okoline te zaključivanje o odgovarajućem djelovanju robota moguće je temeljiti na principima strojnoga učenja. Predloženo istraživanje bavi se razvojem modela učenja i planiranja djelovanja robota. Proces učenja temelji se na novoj umjetnoj neuronskoj mreži klasifikacijom prostornih struktura. Pojam prostorne strukture podrazumijeva interpretaciju rasporeda poznatih objekata u ravnini koje robot percipira vizijskim sustavom. Umjetna neuronska mreža za klasifikaciju i prepoznavanje prostornih struktura zasniva se na teoriji adaptivne rezonancije. Planiranje djelovanja robota temeljno je na usporednoj evoluciji rješenja razvojem novoga genetskoga algoritma. Genetski algoritam kao osnovni cilj ima prostornu pretvorbu neuređenoga stanja objekata u uređeno. Izvorni znanstveni doprinos rada očituje se u sljedećem: 1) Samoorganizirajuća umjetna neuronska mreža za klasifikaciju i prepoznavanje prostornih struktura zasnovana na teoriji adaptivne rezonancije, koju odlikuje nova dvorazinska klasifikacija po obliku i rasporedu objekata te mehanizam asocijativnoga povezivanja neuređenoga skupa objekata s uređenim i 2) Novi genetski algoritam za planiranje robotskoga djelovanja u nestrukturiranoj radnoj okolini karakteriziran usporednom evolucijskom strategijom za pronalaženje rješenja, s ciljem prostorne pretvorbe neuređenoga stanja objekata u uređeno

HUMAN ROBOT INTERACTION THROUGH SEMANTIC INTEGRATION OF MULTIPLE MODALITIES, DIALOG MANAGEMENT, AND CONTEXTS

The hypothesis for this research is that applying the Human Computer Interaction (HCI) concepts of using multiple modalities, dialog management, context, and semantics to Human Robot Interaction (HRI) will improve the performance of Instruction Based Learning (IBL) compared to only using speech. We tested the hypothesis by simulating a domestic robot that can be taught to clean a house using a multi-modal interface. We used a method of semantically integrating the inputs from multiple modalities and contexts that multiplies a confidence score for each input by a Fusion Weight, sums the products, and then uses the input with the highest product sum. We developed an algorithm for determining the Fusion Weights. We concluded that different modalities, contexts, and modes of dialog management impact human robot interaction; however, which combination is better depends on the importance of the accuracy of learning what is taught versus the succinctness of the dialog between the user and the robot

Representation and control of coordinated-motion tasks for human-robot systems

It is challenging for robots to perform various tasks in a human environment. This is because many human-centered tasks require coordination in both hands and may often involve cooperation with another human. Although human-centered tasks require different types of coordinated movements, most of the existing methodologies have focused only on specific types of coordination. This thesis aims at the description and control of coordinated-motion tasks for human-robot systems; i.e., humanoid robots as well as multi-robot and human-robot systems. First, for bimanually coordinated-motion tasks in dual-manipulator systems, we propose the Extended-Cooperative-Task-Space (ECTS) representation, which extends the existing Cooperative-Task-Space (CTS) representation based on the kinematic models for human bimanual movements in Biomechanics. The proposed ECTS representation can represent the whole spectrum of dual-arm motion/force coordination using two sets of ECTS motion/force variables in a unified manner. The type of coordination can be easily chosen by two meaningful coefficients, and during coordinated-motion tasks, each set of variables directly describes two different aspects of coordinated motion and force behaviors. Thus, the operator can specify coordinated-motion/force tasks more intuitively in high-level descriptions, and the specified tasks can be easily reused in other situations with greater flexibility. Moreover, we present consistent procedures of using the ECTS representation for task specifications in the upper-body and lower-body subsystems of humanoid robots in order to perform manipulation and locomotion tasks, respectively. Besides, we propose and discuss performance indices derived based on the ECTS representation, which can be used to evaluate and optimize the performance of any type of dual-arm manipulation tasks. We show that using the ECTS representation for specifying both dual-arm manipulation and biped locomotion tasks can greatly simplify the motion planning process, allowing the operator to focus on high-level descriptions of those tasks. Both upper-body and lower-body task specifications are demonstrated by specifying whole-body task examples on a Hubo II+ robot carrying out dual-arm manipulation as well as biped locomotion tasks in a simulation environment. We also present the results from experiments on a dual-arm robot (Baxter) for teleoperating various types of coordinated-motion tasks using a single 6D mouse interface. The specified upper- and lower-body tasks can be considered as coordinated motions with constraints. In order to express various constraints imposed across the whole-body, we discuss the modeling of whole-body structure and the computations for robotic systems having multiple kinematic chains. Then we present a whole-body controller formulated as a quadratic programming, which can take different types of constraints into account in a prioritized manner. We validate the whole-body controller based on the simulation results on a Hubo II+ robot performing specified whole-body task examples with a number of motion and force constraints as well as actuation limits. Lastly, we discuss an extension of the ECTS representation, called Hierarchical Extended-Cooperative-Task Space (H-ECTS) framework, which uses tree-structured graphical representations for coordinated-motion tasks of multi-robot and human-robot systems. The H-ECTS framework is validated by experimental results on two Baxter robots cooperating with each other as well as with an additional human partner