Lars Blackmore
California Institute of Technology
44 Papers
221 Citations
Lars Blackmore is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Probabilistic logic & Linear system. The author has an hindex of 22, co-authored 44 publications. Previous affiliations of Lars Blackmore include Analysis Group & Massachusetts Institute of Technology.
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Papers
A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control
TL;DR: In this paper, the authors present a method for chance-constrained predictive stochastic control of dynamic systems, which takes into account uncertainty to ensure that the probability of failure due to collision with obstacles, for example, is below a given threshold.
Minimum-Landing-Error Powered-Descent Guidance for Mars Landing Using Convex Optimization
TL;DR: It is shown that the minimum-landing-error trajectory generation problem can be posed as a convex optimization problem and solved to global optimality with known bounds on convergence time, which makes the approach amenable to onboard implementation for real-time applications.
374
Lossless Convexification of Nonconvex Control Bound and Pointing Constraints of the Soft Landing Optimal Control Problem
TL;DR: A convexification of the control constraints that is proven to be lossless enables the use of interior point methods of convex optimization to obtain optimal solutions of the original nonconvex optimal control problem.
281
Brief paper: Lossless convexification of a class of optimal control problems with non-convex control constraints
Behcet Acikmese,Lars Blackmore +1 more
TL;DR: The lossless convexification enables the use of interior point methods of convex optimization to obtain globally optimal solutions of the original non-convex optimal control problem.
232
A probabilistic approach to optimal robust path planning with obstacles
Lars Blackmore,Hui Li,Brian C. Williams +2 more
- 14 Jun 2006
TL;DR: The key idea behind the approach is that the probabilistic obstacle avoidance problem can be expressed as a disjunctive linear program using linear chance constraints, such that planning with uncertainty requires minimal additional computation.
224