Cross-entropy motion planning

Kobilarov, Marin

doi:10.1177/0278364912444543

Cited by 112 publications

(102 citation statements)

References 42 publications

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“…The core of our solution to Problem 1 is the cross-entropy method, a stochastic optimization algorithm that has been used to successfully solve challenging point-to-point motion planning problems [14].…”

Section: Problem Statementmentioning

confidence: 99%

“…The basic idea behind applying the cross-entropy approach to motion planning [14] is to repeat the following two steps: 1) generate sample trajectories from a distribution and compute their costs, and 2) update the distribution using a subset of "good" samples, until the sampling distribution converges to a delta function over an optimal trajectory. Although convergence to a globally optimal solution cannot be guaranteed (as with nonconvex optimization in general), the approach does explore the entire state space.…”

Section: Cross-entropy Ltl Planningmentioning

confidence: 99%

“…This rare-event estimation is equivalent to the global optimization of J(z). Our development closely follows [14] and is based on [7] Rare-event estimation…”

Section: Cross-entropy Optimizationmentioning

confidence: 99%

“…Algorithm 2) could improve the original application of the cross-entropy method to point-to-point motion planning, which we briefly demonstrate by numerical experiments. Using the specification (6) but allowing satisfying trajectories to have finite length, we compared the method described in [14] against a modified version in which our Algorithm 2 is applied to re-use parts of promising trajectories. The workspace providing labels is shown in Figure 3.…”

Section: Comparison With Existing Workmentioning

confidence: 99%

“…The sampling distribution is then updated based on the quality of the sampled trajectories, and this procedure repeats until convergence. Our approach generalizes the cross-entropy motion planning method [14] by incorporating LTL task constraints.…”

Section: Introductionmentioning

confidence: 99%

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Cross-entropy temporal logic motion planning

Livingston

Wolff²,

Murray

2015

Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control

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This paper presents a method for optimal trajectory generation for discrete-time nonlinear systems with linear temporal logic (LTL) task specifications. Our approach is based on recent advances in stochastic optimization algorithms for optimal trajectory generation. These methods rely on estimation of the rare event of sampling optimal trajectories, which is achieved by incrementally improving a sampling distribution so as to minimize the cross-entropy. A key component of these stochastic optimization algorithms is determining whether or not a trajectory is collision-free. We generalize this collision checking to efficiently verify whether or not a trajectory satisfies a LTL formula. Interestingly, this verification can be done in time polynomial in the length of the LTL formula and the trajectory. We also propose a method for efficiently re-using parts of trajectories that only partially satisfy the specification, instead of simply discarding the entire sample. Our approach is demonstrated through numerical experiments involving Dubins car and a generic point-mass model subject to complex temporal logic task specifications.

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Section: Problem Statementmentioning

confidence: 99%

Section: Cross-entropy Ltl Planningmentioning

confidence: 99%

“…This rare-event estimation is equivalent to the global optimization of J(z). Our development closely follows [14] and is based on [7] Rare-event estimation…”

Section: Cross-entropy Optimizationmentioning

confidence: 99%

Section: Comparison With Existing Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-entropy temporal logic motion planning

Livingston

Wolff²,

Murray

2015

Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control

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High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Choudhury

Dugar

Maeta

et al. 2019

Journal of Field Robotics

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Autonomous flight of unmanned full‐size rotor‐craft has the potential to enable many new applications. However, the dynamics of these aircraft, prevailing wind conditions, the need to operate over a variety of speeds and stringent safety requirements make it difficult to generate safe plans for these systems. Prior work has shown results for only parts of the problem. Here we present the first comprehensive approach to planning safe trajectories for autonomous helicopters from takeoff to landing. Our approach is based on two key insights. First, we compose an approximate solution by cascading various modules that can efficiently solve different relaxations of the planning problem. Our framework invokes a long‐term route optimizer, which feeds a receding‐horizon planner which in turn feeds a high‐fidelity safety executive. Secondly, to deal with the diverse planning scenarios that may arise, we hedge our bets with an ensemble of planners. We use a data‐driven approach that maps a planning context to a diverse list of planning algorithms that maximize the likelihood of success. Our approach was extensively evaluated in simulation and in real‐world flight tests on three different helicopter systems for duration of more than 109 autonomous hours and 590 pilot‐in‐the‐loop hours. We provide an in‐depth analysis and discuss the various tradeoffs of decoupling the problem, using approximations and leveraging statistical techniques. We summarize the insights with the hope that it generalizes to other platforms and applications.

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Adaptive sampling with an autonomous underwater vehicle in static marine environments

Stankiewicz

Tan

Kobilarov

2020

Journal of Field Robotics

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This paper explores the use of autonomous underwater vehicles (AUVs) equipped with sensors to construct water quality models to aid in the assessment of important environmental hazards, for instance related to point-source pollutants or localized hypoxic regions. Our focus is on problems requiring the autonomous discovery and dense sampling of critical areas of interest in real-time, for which standard (e.g., grid-based) strategies are not practical due to AUV power and computing constraints that limit mission duration. To this end, we consider adaptive sampling strategies on Gaussian process (GP) stochastic models of the measured scalar field to focus sampling on the most promising and informative regions.

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Cross-entropy motion planning

Cited by 112 publications

References 42 publications

Cross-entropy temporal logic motion planning

Cross-entropy temporal logic motion planning

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Adaptive sampling with an autonomous underwater vehicle in static marine environments

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