Efficient mixed-integer planning for UAVs in cluttered environments

Deits, Robin; Tedrake, Russ

doi:10.1109/icra.2015.7138978

Cited by 183 publications

(173 citation statements)

References 12 publications

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“…This allows the operator to provide high-level input about which surfaces are appropriate for walking, a task that is well-suited to the pilot's expertise but difficult to perform autonomously. We are also currently investigating methods to automate the selection of seed points, and have demonstrated autonomous seeding of regions using a simple heuristic in a 3D environment [20].…”

Section: Convex Decompositionmentioning

confidence: 99%

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

et al. 2015

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This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.

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Section: Convex Decompositionmentioning

confidence: 99%

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

et al. 2015

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“…This is usually handled by changing the constraints to take into account the future position of the agent with respect to its current position. There are a variety of ways of implementing these requirements but they all seem (to the best of the authors' knowledge) to be conservative in their description Richards and Turnbull [2015], Maia and Galvão [2009], Deits and Tedrake [2015].…”

Section: Introductionmentioning

confidence: 99%

On corner cutting in multi-obstacle avoidance problems

Stoican¹,

Grøtli

Prodan

et al. 2015

IFAC-PapersOnLine

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Abstract:One challenging and not extensively studied issue in obstacle avoidance is the corner cutting problem. Avoidance constraints are usually imposed at the sampling time without regards to the intra-sample behavior of the dynamics. This paper improves upon state of the art by describing a multi-obstacle environment over a hyperplane arrangement scaffolding, provides a piecewise description of the "shadow" regions and represents them into a combined mixed integer and predictive control formulation. Furthermore, over-approximation constraints which reduce to strictly binary formulations are discussed in detail. Illustrative proofs of concept, comparisons with the state of the art and simulation results over a classical multi-obstacle avoidance problem validate the benefits of the proposed approach.

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“…The video for this paper can be found at https://youtu.be/gu8Tb7XjU0o space has been done in [7] and [14] with mixed integer programming and quadratic programming respectively. [25] used a collision cost function with sequential convex programming.…”

Section: Fig 1: Example 2-dimensional Manifold Embedded In Rmentioning

confidence: 99%

“…Alternatively it can be seen as a generalization to arbitrary manifolds of the cost functional used in [23] [14] [15] [7].…”

Section: Manifold Problem Formulationmentioning

confidence: 99%

Trajectory Optimization On Manifolds with Applications to SO(3) and R3XS2

Watterson¹,

Liu²,

Sun³

et al. 2018

Robotics: Science and Systems XIV

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Abstract-Manifolds are used in almost all robotics applications even if they are not explicitly modeled. We propose a differential geometric approach for optimizing trajectories on a Riemannian manifold with obstacles. The optimization problem depends on a metric and collision function specific to a manifold. We then propose our Safe Corridor on Manifolds (SCM) method of computationally optimizing trajectories for robotics applications via a constrained optimization problem. Our method does not need equality constraints, which eliminates the need to project back to a feasible manifold during optimization. We then demonstrate how this algorithm works on an example problem on SO(3) and a perception-aware planning example for visualinertially guided robots navigating in 3 dimensions. Formulating field of view constraints naturally results in modeling with the manifold R 3 × S 2 which cannot be modeled as a Lie group.

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Efficient mixed-integer planning for UAVs in cluttered environments

Cited by 183 publications

References 12 publications

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

On corner cutting in multi-obstacle avoidance problems

Trajectory Optimization On Manifolds with Applications to SO(3) and R3XS2

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