Time-optimal motion planning for n-DOF robot manipulators using a path-parametric system reformulation

Verschueren, Robin; Duijkeren, Niels van; Swevers, Jan; Diehl, Moritz

doi:10.1109/acc.2016.7525227

Cited by 24 publications

(9 citation statements)

References 12 publications

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“…In robotics research, applications include control of agricultural robots [125], remote sensing of icebergs with UAVs [15,70], time-optimal control of robots [132], motion templates for robot-human interaction [133], motion planning of robotic systems with contacts [8,99], and multi-objective control of complex robots [86].…”

Section: Optimization and Simulation In Science And Engineeringmentioning

confidence: 99%

CasADi: a software framework for nonlinear optimization and optimal control

Andersson

Gillis

Horn³

et al. 2018

Math. Prog. Comp.

Self Cite

2,404

976

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We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last seven years.

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Section: Optimization and Simulation In Science And Engineeringmentioning

confidence: 99%

CasADi: a software framework for nonlinear optimization and optimal control

Andersson

Gillis

Horn³

et al. 2018

Math. Prog. Comp.

Self Cite

2,404

976

View full text Add to dashboard Cite

show abstract

“…To test the performance of the simplified model for different initial and final conditions, additional simulations were carried out for four different machine base attitudes of 0,15, 20, and 30-degree roll, each with 3000 pairs of randomized initial and final cabin yaw angles from the set [−2π , 2π ] with other initial and final joint angles the same as in ( 27) and (28). The trajectory planning success rate is presented in Table III.…”

Section: Monte Carlo Simulations For Reconfiguration On Different Slopesmentioning

confidence: 99%

Stability-constrained mobile manipulation planning on rough terrain

Song¹,

Sharf²

2022

Robotica

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This paper presents a hierarchical framework that allows online point-to-point dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot and the zero moment point stability measure are presented as theoretical background. Then, a sampling-based quasi-static planning algorithm modified for stability guarantee and traction optimization in continuous dynamic motion is presented along with a mathematical proof. The robot’s quasi-static path is then used as an initial guess to warm start a nonlinear optimal control solver which may otherwise have difficulties finding a solution to the stability-constrained formulation efficiently. The performance and computational efficiency of the framework are demonstrated through an application to a simulated timber harvesting mobile manipulator machine working on varying terrain. The results demonstrate feasibility of online trajectory planning on varying terrain while satisfying the dynamic stability constraint. Qualitative and quantitative comparisons with existing methods are also presented.

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“…There is a variety of results on manipulator and mobile manipulator optimal trajectory planning [15]- [18] but none of the previous works address the problem of rollover and sliding prevention.…”

Section: B Mobile Manipulation Planningmentioning

confidence: 99%

“…By substituting ( 21) into (18), we obtain the following constraints on the variation of ZMP coordinates:…”

Section: Stability Guaranteed Sampling-based Path Search For Continuo...mentioning

confidence: 99%