503 research outputs found

    Closed-Form Inverse Kinematic Solution for Anthropomorphic Motion in Redundant Robot Arms

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    Human-like arm motion generation: a review

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    In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

    Using humanoid robots to study human behavior

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    Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans “program” behavior in-or train-each other

    Bio-inspired kinematical control of redundant robotic manipulators

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    Purpose – This paper aims to propose an innovative kinematic control algorithm for redundant robotic manipulators. The algorithm takes advantage of a bio-inspired approach. Design/methodology/approach – A simplified two-degree-of-freedom model is presented to handle kinematic redundancy in the x-y plane; an extension to three-dimensional tracking tasks is presented as well. A set of sample trajectories was used to evaluate the performances of the proposed algorithm. Findings – The results from the simulations confirm the continuity and accuracy of generated joint profiles for given end-effector trajectories as well as algorithm robustness, singularity and self-collision avoidance. Originality/value – This paper shows how to control a redundant robotic arm by applying human upper arm-inspired concept of inter-joint dependency

    A model-based approach to robot kinematics and control using discrete factor graphs with belief propagation

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    Much of recent researches in robotics have shifted the focus from traditionally-specific industrial tasks to investigations of new types of robots with alternative ways of controlling them. In this paper, we describe the development of a generic method based on factor graphs to model robot kinematics. We focused on the kinematics aspect of robot control because it provides a fast and systematic solution for the robot agent to move in a dynamic environment. We developed neurally-inspired factor graph models that can be applied on two different robotic systems: a mobile platform and a robotic arm. We also demonstrated that we can extend the static model of the robotic arm into a dynamic model useful for imitating natural movements of a human hand. We tested our methods in a simulation environment as well as in scenarios involving real robots. The experimental results proved the flexibility of our proposed methods in terms of remodeling and learning, which enabled the modeled robot to perform reliably during the execution of given tasks

    Biomimetic Manipulator Control Design for Bimanual Tasks in the Natural Environment

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    As robots become more prolific in the human environment, it is important that safe operational procedures are introduced at the same time; typical robot control methods are often very stiff to maintain good positional tracking, but this makes contact (purposeful or accidental) with the robot dangerous. In addition, if robots are to work cooperatively with humans, natural interaction between agents will make tasks easier to perform with less effort and learning time. Stability of the robot is particularly important in this situation, especially as outside forces are likely to affect the manipulator when in a close working environment; for example, a user leaning on the arm, or task-related disturbance at the end-effector. Recent research has discovered the mechanisms of how humans adapt the applied force and impedance during tasks. Studies have been performed to apply this adaptation to robots, with promising results showing an improvement in tracking and effort reduction over other adaptive methods. The basic algorithm is straightforward to implement, and allows the robot to be compliant most of the time and only stiff when required by the task. This allows the robot to work in an environment close to humans, but also suggests that it could create a natural work interaction with a human. In addition, no force sensor is needed, which means the algorithm can be implemented on almost any robot. This work develops a stable control method for bimanual robot tasks, which could also be applied to robot-human interactive tasks. A dynamic model of the Baxter robot is created and verified, which is then used for controller simulations. The biomimetic control algorithm forms the basis of the controller, which is developed into a hybrid control system to improve both task-space and joint-space control when the manipulator is disturbed in the natural environment. Fuzzy systems are implemented to remove the need for repetitive and time consuming parameter tuning, and also allows the controller to actively improve performance during the task. Experimental simulations are performed, and demonstrate how the hybrid task/joint-space controller performs better than either of the component parts under the same conditions. The fuzzy tuning method is then applied to the hybrid controller, which is shown to slightly improve performance as well as automating the gain tuning process. In summary, a novel biomimetic hybrid controller is presented, with a fuzzy mechanism to avoid the gain tuning process, finalised with a demonstration of task-suitability in a bimanual-type situation.EPSR

    Bio-inspired control of redundant robotic systems: Optimization approach

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    Osnovni cilj ovog rada je da promoviše pristup biološki inspirisanog sinergijskog upravljanja koji omogućava da se razreši redundansa datog robotizovanog sistema koji se može koristiti i za vojne svrhe. Pokazano je da je moguće razrešiti kinematički redundansu primenom metode lokalne optimizacije i bioloških analogona - sinergijsko upravljački pristup sa uvođenjem logičkog upravljanja i distribuiranog pozicioniranja. Takođe, mogućnost prebacivanja između sinegrija u okviru jedne trajektorije je razmatrano. Na kraju, problem aktuatorske redundanse je postavljen i rešen primenom Pontrjaginovog principa maksimuma. Upravljačka sinergija je ustanovljena primenom postupka optimizacije na koordinacionom nivou. Na kraju, efikasnost predložene biološki inspirisane optimalne upravljačke sinergije je demonstriran na pogodno usvojenom robotskom sistemu sa tri stepena slobode i četiri upravljačke promenljive, kao ilustrativnog primera.The major aim of this paper is to promote a biologically inspired control synergy approach that allows the resolution of redundancy of a given robotized system which can be used for military purposes. It is shown that it is possible to resolve kinematic redundancy using the local optimization method and biological analogues - control synergy approach, introducing hypothetical control and distributed positioning. Also, the possibility of switching synergies within a single trajectory is treated, where the control synergy approach applying logical control is used. The actuator redundancy control problem has been stated and solved using Pontryagin's maximum principle. Control synergy as a class of dynamic synergy is established by the optimization law at the coordination level. Finally, the effectiveness of the suggested biologically inspired optimal control synergy is demonstrated with a suitable robot with three degrees of freedom and four control variables, as an illustrative example.
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