5,161 research outputs found
Safety-related Tasks within the Set-Based Task-Priority Inverse Kinematics Framework
In this paper we present a framework that allows the motion control of a
robotic arm automatically handling different kinds of safety-related tasks. The
developed controller is based on a Task-Priority Inverse Kinematics algorithm
that allows the manipulator's motion while respecting constraints defined
either in the joint or in the operational space in the form of equality-based
or set-based tasks. This gives the possibility to define, among the others,
tasks as joint-limits, obstacle avoidance or limiting the workspace in the
operational space. Additionally, an algorithm for the real-time computation of
the minimum distance between the manipulator and other objects in the
environment using depth measurements has been implemented, effectively allowing
obstacle avoidance tasks. Experiments with a Jaco manipulator, operating in
an environment where an RGB-D sensor is used for the obstacles detection, show
the effectiveness of the developed system
Handling robot constraints within a Set-Based Multi-Task Priority Inverse Kinematics Framework
Set-Based Multi-Task Priority is a recent framework to handle inverse
kinematics for redundant structures. Both equality tasks, i.e., control
objectives to be driven to a desired value, and set-bases tasks, i.e., control
objectives to be satisfied with a set/range of values can be addressed in a
rigorous manner within a priority framework. In addition, optimization tasks,
driven by the gradient of a proper function, may be considered as well, usually
as lower priority tasks. In this paper the proper design of the tasks, their
priority and the use of a Set-Based Multi-Task Priority framework is proposed
in order to handle several constraints simultaneously in real-time. It is shown
that safety related tasks such as, e.g., joint limits or kinematic singularity,
may be properly handled by consider them both at an higher priority as
set-based task and at a lower within a proper optimization functional.
Experimental results on a 7DOF Jaco$^2
Hybrid Controller based on Null Space and Consensus Algorithms for Mobile Robot Formation
This work presents a novel hybrid control approach based on null space and consensus algorithms to solve the scalability problems of mobile robot formation and improve leader control through multiple control objectives. In previous works, the training of robots based on the null space requires a rigid training structure based on a geometric shape, which increases the number of agents in the formation. The scheme of the control algorithm, which does not make formation scalability possible, must be changed; therefore, seeking the scalability of training based on null space is a challenge that could be solved with the inclusion of consensus algorithms, which allow the control structure to be maintained despite increasing or decreasing the number of robot followers. Another advantage of this proposal is that the formation of the followers does not depend on any geometric figure compared to previous works based on the null space; this new proposal does not present singularities as if the structure is based on geometric shape, the latter one is crucial since the formation of agents can take forms that cannot be achieved with a geometric structure, such as collinear locations, that can occur in many environments. The proposed hybrid control approach presents three tasks: i) leader position task, ii) leader shape task, and iii) follower formation task. The proposed algorithm is validated through simulations, performing tests that use the kinematic model of non-holonomic mobile robots. In addition, linear algebra and Lyapunov theory are used to analyze the stability of the method. Doi: 10.28991/ESJ-2022-06-03-01 Full Text: PD
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