Planning safe cyclic motions under repetitive task constraints
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Citations
An analysis of RelaxedIK: an optimization-based framework for generating accurate and feasible robot arm motions
Generation of synchronized configuration space trajectories with workspace path constraints for an ensemble of robots
Path planning for robotic manipulators via bubbles of free configuration space: Evolutionary approach
Combining Method of Alternating Projections and Augmented Lagrangian for Task Constrained Trajectory Optimization
Concept and preliminary research of anthropomorphic manipulator with hydrostatic drive system for mobile robot
References
Randomized kinodynamic planning
Randomized kinodynamic planning
Automatic Supervisory Control of the Configuration and Behaviour of Multibody Mechanisms
Review of pseudoinverse control for use with kinematically redundant manipulators
Related Papers (5)
Frequently Asked Questions (18)
Q2. What future works have the authors mentioned in the paper "Planning safe cyclic motions under repetitive task constraints" ?
For redundant robotic systems subject to repetitive task constraints, the authors have presented a control-based approach for planning cyclic, collision-free configuration space paths. Future work will be aimed at several objectives, such as: • the use of specific metrics for Ctask ; • a formal proof of probabilistic completeness for the planner ; • the extension of the proposed approach to robotic systems subject to nonholonomic constraints ( e. g., wheeled mobile manipulators ). The authors believe that their contribution fills a void in the literature.
Q3. How many steps did the authors use to generate the motions?
The authors used4 N + 1 = 11 equispaced samples of the desired task path and generated motions using Euler’s method with integration step ∆s = 0.002.
Q4. What is the degree of redundancy in the drawing task?
Since the drawing task is 3-dimensional, the degree of redundancy is 6 − 3 = 3 (the wrist roll is frozen because it is irrelevant for the task).
Q5. How many vertices are there in the KUKA LWR-IV?
At the end, the forward and backward trees contain 29 and 34 vertices, respectively, while the mean and the maximum task errors are 0.0275 mm and 0.0418 mm.
Q6. What is the purpose of the loop closure procedure?
As customary in bidirectional search, extension steps are occasionally replaced (a parameter controls this event) by connection steps aimed at reducing the gap between the two trees.
Q7. What is the solution to the C-TCMP problem?
When closure is obtained, a solution to the C-TCMP problem is reconstructed by patching together the subpath from qstart to qfw on Tfw, the loop closure subpath from qfw to qbw, and the subpath from qbw to qstart on Tbw (in the reverse direction).
Q8. What is the basic idea of the algorithm?
The basic idea here is to identify a suitable subset of nq − nt ‘redundant’ configuration variables, and to perform the desired reconfiguration on these variables.
Q9. What is the cyclic task in the KUKA LWR-IV?
For redundant robotic systems subject to repetitive task constraints, the authors have presented a control-based approach for planning cyclic, collision-free configuration space paths.
Q10. What is the purpose of this paper?
Since the problem addressed in this paper is a special case of Task-Constrained Motion Planning (TCMP), the authors will adopt the terminology and framework introduced in [17].
Q11. What is the inverse kinematic solution for qrand?
At each iteration, a random configuration qrand is first generated3 in Ctask; this is done by picking a value for s ∈ {s1, . . . , sN−1}, and then choosing one of the inverse kinematic solutions for the corresponding sample of td(s).
Q12. What is the kinematic form of a tcp?
For path or motion planning purposes, it is appropriate to use the geometric form of such model, which specifies the admissible tangent vectors to a configuration space path q(s), where s is a path parameter, asq′ = ṽ, (1)with the notation ( )′ = d( )/ds.
Q13. How is the qrand generated in Ctask?
For each value of w̃, the motion generation scheme (3–4) is then integrated numerically starting from qnear at s = si and ending at s = si+1.
Q14. What is the mean and maximum value of the task error norm?
The mean and the maximum value of the task error norm over the whole path are, respectively, 0.0729 mm and 0.1354 mm, confirming that the proposed method guarantees continued satisfaction of the task constraint.
Q15. What is the difference between the two types of motion planners?
The authors emphasize that, unlike sampling-based constrained motion planners, the use of the motion generation scheme (3– 4) guarantees that the task variables move along the desired path throughout the motion.
Q16. What is the cyclicity of the configuration space?
Cyclicity in configuration space is confirmed by Fig. 5, which shows that the displacement with respect to the initial configuration returns to zero at the end of the motion.
Q17. How long did it take to compute the tree?
The solution shown in Fig. 3 took 83 s to compute, with a final size of the forward and backward trees of 116 and 33 vertices, respectively.
Q18. What are the main objectives of the proposed approach?
Future work will be aimed at several objectives, such as: • the use of specific metrics for Ctask; • a formal proof of probabilistic completeness for theplanner;• the extension of the proposed approach to robotic systems subject to nonholonomic constraints (e.g., wheeled mobile manipulators).