Toward Optimal Sampling in the Space of Paths

Green, Colin J.; Kelly, Alonzo

doi:10.1007/978-3-642-14743-2_24

Cited by 45 publications

(56 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sub-sequence of trajectories is usually computed offline [11,9]. Such methods are widely used in modern, autonomous ground robots including the two highest placing teams for DARPA Urban Challenge and Grand Challenge [28,17,27,26], LAGR [12], UPI [1], and Perceptor [14] programs.…”

Section: B Mobile Robot Navigationmentioning

confidence: 99%

“…We simulated a robot traversing between various random starting and goal locations using the maximum-discrepancy trajectory [11] sequence as well as sequences generated by CONSEQOPT using Algorithm 1. A texton library [29] of 25 k-means cluster centers was computed for the whole overhead map.…”

Section: B Mobile Robot Navigationmentioning

confidence: 99%

“…In the case of control libraries, a sequence refers to a ranked list of control action choices rather than a series of actions to be taken. Examples of control libraries include a collection of feasible grasps for manipulation [5], a collection of feasible trajectories for mobile robot navigation [11], and a collection of expert-demonstrated trajectories for a walking robot (Stolle et. al.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Contextual Sequence Prediction with Application to Control Library Optimization

Dey¹,

Liu²,

Hebert³

et al. 2012

Robotics: Science and Systems VIII

View full text Add to dashboard Cite

Abstract-Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in robotics. Previous work in sequence optimization produces a static ordering that does not take any features of the item or context of the problem into account. In this work, we propose a general approach to order the items within the sequence based on the context (e.g., perceptual information, environment description, and goals). We take a simple, efficient, reduction-based approach where the choice and order of the items is established by repeatedly learning simple classifiers or regressors for each "slot" in the sequence. Our approach leverages recent work on submodular function maximization to provide a formal regret reduction from submodular sequence optimization to simple costsensitive prediction. We apply our contextual sequence prediction algorithm to optimize control libraries and demonstrate results on two robotics problems: manipulator trajectory prediction and mobile robot path planning.

show abstract

Section: B Mobile Robot Navigationmentioning

confidence: 99%

Section: B Mobile Robot Navigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Contextual Sequence Prediction with Application to Control Library Optimization

Dey¹,

Liu²,

Hebert³

et al. 2012

Robotics: Science and Systems VIII

View full text Add to dashboard Cite

show abstract

“…We first collisiontest some paths drawn from a low-dispersion sequence (Green and Kelly, 2007). After finding the first collision point, its location biases future action samples.…”

Section: Informed Path Sampling Approachmentioning

confidence: 99%

Realtime Informed Path Sampling for Motion Planning Search

Knepper

Mason

2016

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

Mobile robot motions often originate from an uninformed path sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive collision-testing process. Although all necessary information for collision-testing a path is known to the planner, that information is typically stored in a relatively unavailable form in a costmap or obstacle map. By summarizing the most salient data in a more accessible form, our process delivers a denser sampling of the free path space per unit time than open-loop sampling techniques. We obtain this result by probabilistically modeling-in real time and with minimal information-the locations of obstacles and free space, based on collision-test results. We present CALM, the combined adaptive locality model, along with an algorithm to bias path sampling based on the model's predictions. We provide experimental results in simulation for motion planning on mobile robots, demonstrating up to a 330% increase in paths surviving collision test.

show abstract

“…The Maneuver Automaton [5] attempts to capture the formal properties of trajectory libraries as a hybrid automaton, thus providing a nice unifying theoretical framework. Further theoretical investigations have focused on the offline generation of diverse but sparse trajectories that ensure the robot's ability to perform the necessary tasks online in an efficient manner [6]. More recently, tools from sub-modular sequence optimization have been leveraged in the optimization of the sequence and content of trajectories evaluated online [4].…”

Section: Related Workmentioning

confidence: 99%

Robust Online Motion Planning with Regions of Finite Time Invariance

Majumdar

Tedrake

2013

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

In this paper we consider the problem of generating motion plans for a nonlinear dynamical system that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, disturbances, and/or errors in state estimation. Furthermore, we consider the case where these plans must be generated online, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Previous work on feedback motion planning for nonlinear systems was limited to offline planning due to the computational cost of safety verification. Here we take a trajectory library approach by designing controllers that stabilize the nominal trajectories in the library and precomputing regions of finite time invariance ("funnels") for the resulting closed loop system. We leverage sums-of-squares programming in order to efficiently compute funnels which take into account bounded disturbances and uncertainty. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. We demonstrate our method on a simulation of a plane flying through a two dimensional forest of polygonal trees with parametric uncertainty and disturbances in the form of a bounded "cross-wind".

show abstract

Toward Optimal Sampling in the Space of Paths

Cited by 45 publications

References 14 publications

Contextual Sequence Prediction with Application to Control Library Optimization

Contextual Sequence Prediction with Application to Control Library Optimization

Realtime Informed Path Sampling for Motion Planning Search

Robust Online Motion Planning with Regions of Finite Time Invariance

Contact Info

Product

Resources

About