90 research outputs found
Folding Assembly by Means of Dual-Arm Robotic Manipulation
In this paper, we consider folding assembly as an assembly primitive suitable
for dual-arm robotic assembly, that can be integrated in a higher level
assembly strategy. The system composed by two pieces in contact is modelled as
an articulated object, connected by a prismatic-revolute joint. Different
grasping scenarios were considered in order to model the system, and a simple
controller based on feedback linearisation is proposed, using force torque
measurements to compute the contact point kinematics. The folding assembly
controller has been experimentally tested with two sample parts, in order to
showcase folding assembly as a viable assembly primitive.Comment: 7 pages, accepted for ICRA 201
Asymmetric Dual-Arm Task Execution using an Extended Relative Jacobian
Coordinated dual-arm manipulation tasks can be broadly characterized as
possessing absolute and relative motion components. Relative motion tasks, in
particular, are inherently redundant in the way they can be distributed between
end-effectors. In this work, we analyse cooperative manipulation in terms of
the asymmetric resolution of relative motion tasks. We discuss how existing
approaches enable the asymmetric execution of a relative motion task, and show
how an asymmetric relative motion space can be defined. We leverage this result
to propose an extended relative Jacobian to model the cooperative system, which
allows a user to set a concrete degree of asymmetry in the task execution. This
is achieved without the need for prescribing an absolute motion target.
Instead, the absolute motion remains available as a functional redundancy to
the system. We illustrate the properties of our proposed Jacobian through
numerical simulations of a novel differential Inverse Kinematics algorithm.Comment: Accepted for presentation at ISRR19. 16 Page
Obstacle Avoidance in Dynamic Environments via Tunnel-following MPC with Adaptive Guiding Vector Fields
This paper proposes a motion control scheme for robots operating in a dynamic
environment with concave obstacles. A Model Predictive Controller (MPC) is
constructed to drive the robot towards a goal position while ensuring collision
avoidance without direct use of obstacle information in the optimization
problem. This is achieved by guaranteeing tracking performance of an
appropriately designed receding horizon path. The path is computed using a
guiding vector field defined in a subspace of the free workspace where each
point in the subspace satisfies a criteria for minimum distance to all
obstacles. The effectiveness of the control scheme is illustrated by means of
simulation
Asymmetric Dual-Arm Task Execution Using an\ua0Extended Relative Jacobian
Coordinated dual-arm manipulation tasks can be broadly characterized as possessing absolute and relative motion components. Relative motion tasks, in particular, are inherently redundant in the way they can be distributed between end-effectors. In this work, we analyse cooperative manipulation in terms of the asymmetric resolution of relative motion tasks. We discuss how existing approaches enable the asymmetric execution of a relative motion task, and show how an asymmetric relative motion space can be defined. We leverage this result to propose an extended relative Jacobian to model the cooperative system, which allows a user to set a concrete degree of asymmetry in the task execution. This is achieved without the need for prescribing an absolute motion target. Instead, the absolute motion remains available as a functional redundancy to the system. We illustrate the properties of our proposed Jacobian through numerical simulations of a novel differential Inverse\ua0Kinematics algorithm
Learning Shape Control of Elastoplastic Deformable Linear Objects
Deformable object manipulation tasks have long been regarded as challenging
robotic problems. However, until recently very little work has been done on the
subject, with most robotic manipulation methods being developed for rigid
objects. Deformable objects are more difficult to model and simulate, which has
limited the use of model-free Reinforcement Learning (RL) strategies, due to
their need for large amounts of data that can only be satisfied in simulation.
This paper proposes a new shape control task for Deformable Linear Objects
(DLOs). More notably, we present the first study on the effects of
elastoplastic properties on this type of problem. Objects with elastoplasticity
such as metal wires, are found in various applications and are challenging to
manipulate due to their nonlinear behavior. We first highlight the challenges
of solving such a manipulation task from an RL perspective, particularly in
defining the reward. Then, based on concepts from differential geometry, we
propose an intrinsic shape representation using discrete curvature and torsion.
Finally, we show through an empirical study that in order to successfully solve
the proposed task using Deep Deterministic Policy Gradient (DDPG), the reward
needs to include intrinsic information about the shape of the DLO
Planar Friction Modelling with LuGre Dynamics and Limit Surfaces
Contact surfaces in planar motion exhibit a coupling between tangential and
rotational friction forces. This paper proposes planar friction models grounded
in the LuGre model and limit surface theory. First, distributed planar extended
state models are proposed and the Elasto-Plastic model is extended for
multi-dimensional friction. Subsequently, we derive a reduced planar friction
model, coupled with a pre-calculated limit surface, that offers reduced
computational cost. The limit surface approximation through an ellipsoid is
discussed. The properties of the planar friction models are assessed in various
simulations, demonstrating that the reduced planar friction model achieves
comparable performance to the distributed model while exhibiting ~80 times
lower computational cost
Autonomous Navigation with Convergence Guarantees in Complex Dynamic Environments
This article addresses the obstacle avoidance problem for setpoint
stabilization and path-following tasks in complex dynamic 2D environments that
go beyond conventional scenes with isolated convex obstacles. A combined motion
planner and controller is proposed for setpoint stabilization that integrates
the favorable convergence characteristics of closed-form motion planning
techniques with the intuitive representation of system constraints through
Model Predictive Control (MPC). The method is analytically proven to accomplish
collision avoidance and convergence under certain conditions, and it is
extended to path-following control. Various simulation scenarios using a
non-holonomic unicycle robot are provided to showcase the efficacy of the
control scheme and its improved convergence results compared to standard
path-following MPC approaches with obstacle avoidance
A survey of robot manipulation in contact
In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks
ΠΠΎΠ²ΡΠ΅ ΠΏΠΎΠ΄Ρ ΠΎΠ΄Ρ ΠΊ ΡΠΈΠ½ΡΠ΅Π·Ρ Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΡ ΠΏΠΈΡΠΈΠ΄ΠΈΠ½ΠΊΠ°ΡΠ±ΠΎΠ½ΠΎΠ²ΡΡ ΠΊΠΈΡΠ»ΠΎΡ, Π°ΠΊΡΠΈΠ΄ΠΈΠ½Π° ΠΈ ΠΏΠΈΡΠ°Π·ΠΎΠ»ΠΎΠ½Π°
Π₯ΠΠΠΠ― Π€ΠΠ ΠΠΠ¦ΠΠΠ’ΠΠ§ΠΠ‘ΠΠΠ―ΠΠΠ’ΠΠ ΠΠ¦ΠΠΠΠΠ§ΠΠ‘ΠΠΠ Π‘ΠΠΠΠΠΠΠΠΠ― /Π₯ΠΠ Π‘ΠΠΠ’ΠΠΠ ΠΠΠΠΠΠΠ ΠΠΠΠΠΠ«Π ΠΠΠ‘ΠΠΠ’Π«ΠΠΠΠ¦ΠΠ /Π₯ΠΠΠΠΠΠΠ’ΠΠΠΠΠΠ― ΠΠΠ‘ΠΠΠ’Π /Π₯ΠΠΠΠΠΠΠΠΠΠ’ΠΠΠΠΠ«Π ΠΠΠ‘ΠΠΠ’Π« /Π₯ΠΠΠ₯ΠΠΠ ΠΠΠΠΠΠ ΠΠΠ«ΠΠ€ΠΠ Π« Π‘ΠΠΠΠΠ«Π /Π₯ΠΠΠΠΠΠΠΠ’ΠΠΠ«ΠΠΠ ΠΠΠΠΠ« /Π₯ΠΠΠ₯ΠΠΠΠΠΠΠ« /Π₯ΠΠΠΠΠ ΠΠΠΠΠΠΠ« /Π₯ΠΠΠΠΠΠΠΠΠΠ§ΠΠ‘ΠΠΠ― ΠΠΠ’ΠΠΠΠΠ‘Π’Π¬ΠΠΠΠΠ ΠΠΠ’ΠΠ ΠΠ’Π£Π Π«ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΡΠ°ΡΠΌΠ°ΡΠ΅Π²ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Ρ
ΠΈΠΌΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° Π½ΠΎΠ²ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΈΠ½ΡΠ΅Π·Π°, ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΠΎΠΉΡΡΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΠΈΡΠΊ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ Π°ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΡΡΠ΅Π΄ΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΡ
Π½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΈ ΠΈΠ·ΠΎΠ½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡ. Π ΠΎΠ±Π·ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΊ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΡΠΈΡΠΎΠ² ΠΊΠ°ΡΠ±ΠΎΠ½ΠΎΠ²ΡΡ
ΠΊΠΈΡΠ»ΠΎΡ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΈ ΠΈΠ·ΠΎΠ½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡ, ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΠΏΡΠΈΠΌΠ΅ΡΡ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΈ ΠΈΠ·ΠΎΠ½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡ ΠΈ ΠΈΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΡ
. ΠΠ±ΡΡΠΆΠ΄Π΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ ΡΠΈΠ½ΡΠ΅Π·Π° Π°Π·ΠΎΠΌΠ΅ΡΠΈΠ½ΠΎΠ², Π·Π°ΠΌΠ΅ΡΠ΅Π½Π½ΡΡ
Π°ΠΊΡΠΈΠ΄ΠΈΠ½ΠΎΠ² ΠΈ ΠΏΠΈΡΠ°Π·ΠΎΠ»ΠΎΠ½ΠΎΠ², ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΠΏΡΠΈΠΌΠ΅ΡΡ ΠΈΡ
Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½Π°Ρ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈΡ ΡΠΈΠ½ΡΠ΅Π·Π° Π½ΠΎΠ²ΡΡ
ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΡ
Π»Π΅ΠΊΠ°ΡΡΡΠ²Π΅Π½Π½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΡ
Π½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΈ ΠΈΠ·ΠΎΠ½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Π½ΡΠ΅ Π² Π΄Π°Π½Π½ΠΎΠΌ ΠΎΠ±Π·ΠΎΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΎΡΠ³Π°Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ ΡΠΈΠ½ΡΠ΅Π·Ρ Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΡ
Π½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΈ ΠΈΠ·ΠΎΠ½ΠΈΠΊΠΎΡΠΈΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΠΏΠΎΠ»ΡΡΠ°ΡΡ Π½ΠΎΠ²ΡΠ΅ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠ΅ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ, ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎ ΠΎΠ±Π»Π°Π΄Π°ΡΡΠΈΠ΅ Π°Π½ΡΠΈΠ±Π°ΠΊΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ, ΠΏΡΠΎΡΠΈΠ²ΠΎΠ²ΠΈΡΡΡΠ½ΠΎΠΉ, ΡΡΠ½Π³ΠΈΡΠΈΠ΄Π½ΠΎΠΉ ΠΈ ΠΏΡΠΎΡΠΈΠ²ΠΎΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΡ.The urgent task of modern pharmaceutical chemistry is the development of new methods of synthesis, the study of chemical properties, as well as the search for biologically active compounds among derivatives of nicotinic and isonicotinic acids. The review examines synthetic approaches to the production of carboxylic acid esters including nicotinic and isonicotinic acids, gives examples of the biological activity of nicotinic and isonicotinic acids and their derivatives. The methods for the synthesis of azomethines, substituted acridines and pyrazolones are discussed, examples of their biological activity are given. A promising concept for the synthesis of new potential drugs based on heterocyclic derivatives of nicotinic and isonicotinic acids is presented. The methods of functionalization of organic compounds considered in this review with regard to the synthesis of heterocyclic derivatives of nicotinic and isonicotinic acids make it possible to obtain new promising compounds potentially having antibacterial, antiviral, fungicidal and antitumor activity
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