Robotic motion planning in dynamic, cluttered, uncertain environments

Toit, Noel E. Du; Burdick, Joel W.

doi:10.1109/robot.2010.5509278

Cited by 72 publications

(45 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compare and analyze the performance and convergence characteristics of the approach presented in this paper to our preliminary approach based on stochastic differential dynamic programming (sDDP) [28]. We also analyze the effect of assuming maximum-likelihood observations [21,7,8] on the computed locally optimal trajectory and corresponding control policy.…”

Section: Resultsmentioning

confidence: 99%

“…We analyze the effect of assuming maximum-likelihood observations made in prior work [21,7,8] on the computed locally optimal trajectory and corresponding control policy. We reproduce this assumption in our method by ignoring all the terms in the value iteration that pertain to the matrix W [b, u], which determines the stochastic nature of the belief dynamics given by Eq.…”

Section: Effect Of Assuming Maximum-likelihood Observationsmentioning

confidence: 99%

“…Belief roadmaps [22] and icLQG [10] combine an iterative LQG approach with a roadmap, but this approach does not result in (locally) optimal solutions. The approaches of [21,7,8] are similar to ours and incorporate the variance into an augmented state and use the LQG framework to find a locally optimal control policy. The main difference is that these approaches assume maximumlikelihood observations to make the belief propagation deterministic.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Motion planning under uncertainty using iterative local optimization in belief space

Berg

Patil

Alterovitz

2012

The International Journal of Robotics Research

283

275

View full text Add to dashboard Cite

We present a new approach to motion planning under sensing and motion uncertainty by computing a locally optimal solution to a continuous partially observable Markov decision process (POMDP). Our approach represent beliefs (the distributions of the robot's state estimate) by Gaussian distributions and is applicable to robot systems with non-linear dynamics and observation models. The method follows the general POMDP solution framework in which we approximate the belief dynamics using an extended Kalman filter and represent the value function by a quadratic function that is valid in the vicinity of a nominal trajectory through belief space. Using a belief space variant of iterative LQG (iLQG), our approach iterates with secondorder convergence towards a linear control policy over the belief space that is locally optimal with respect to a user-defined cost function. Unlike previous work, our approach does not assume maximum-likelihood observations, does not assume fixed estimator or control gains, takes into account obstacles in the environment, and does not require discretization of the state and action spaces. The running time of the algorithm is polynomial (O[n 6 ]) in the dimension n of the state space. We demonstrate the potential of our approach in simulation for holonomic and nonholonomic robots maneuvering through environments with obstacles with noisy and partial sensing and with non-linear dynamics and observation models.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Effect Of Assuming Maximum-likelihood Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Motion planning under uncertainty using iterative local optimization in belief space

Berg

Patil

Alterovitz

2012

The International Journal of Robotics Research

283

275

View full text Add to dashboard Cite

show abstract

“…Recent research [Platt et al, 2010, Erez and Smart, 2010, du Toit and Burdick, 2010, Hauser, 2010 has established the value of control in belief space using simplified models and replanning. Our approach to belief space planning builds directly on this work.…”

Section: Related Workmentioning

confidence: 99%

Integrated task and motion planning in belief space

Kaelbling¹,

Lozano-Pérez²

2013

The International Journal of Robotics Research

293

223

View full text Add to dashboard Cite

We describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains based on planning in the belief space of probability distributions over states using hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of logical expressions that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators can give rise to task-oriented perception in support of the manipulation goals. An implementation of this method is demonstrated in simulation and on a real PR2 robot, showing robust, flexible solution of mobile manipulation problems with multiple objects and substantial uncertainty.

show abstract

“…POMDP solvers reason about uncertainty and can integrate observations, but do not easily generalize to continuous action spaces or non-additive reward functions. Most applications of POMDPs to manipulation rely on discretization [17,18,26] or receding horizon planning [34,41].…”

Section: Related Workmentioning

confidence: 99%

Robust trajectory selection for rearrangement planning as a multi-armed bandit problem

Koval

King

Pollard

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Abstract-We present an algorithm for generating openloop trajectories that solve the problem of rearrangement planning under uncertainty. We frame this as a selection problem where the goal is to choose the most robust trajectory from a finite set of candidates. We generate each candidate using a kinodynamic state space planner and evaluate it using noisy rollouts.Our key insight is we can formalize the selection problem as the "best arm" variant of the multi-armed bandit problem. We use the successive rejects algorithm to efficiently allocate rollouts between candidate trajectories given a rollout budget. We show that the successive rejects algorithm identifies the best candidate using fewer rollouts than a baseline algorithm in simulation. We also show that selecting a good candidate increases the likelihood of successful execution on a real robot. I. IntroductionWe explore the rearrangement planning problem [11] where a robot must rearrange several objects in a cluttered environment to achieve a goal ( Fig.1-Top). Recent work has used simple physics models, like quasistatic pushing [8,9,22,32,39,43], to quickly produce efficient and intricate plans. However, the intricacy of these plans makes them particularly sensitive to uncertainty in object pose, physics parameters, and trajectory execution.We address this problem by generating open-loop trajectories that are robust to uncertainty. However, this is particularly hard for rearrangement planning.First, the problem is set in a high-dimensional space with continuous actions. Second, contact causes physics to evolve in complex, non-linear ways and quickly leads to multi-modal and non-smooth distributions [25,26,34]. Third, finding good trajectories is inherently hard: most trajectories achieve success with zero probability.As a consequence, this problem lies outside the domains of standard conformant planning [19,37], POMDP [20] and policy search [7] algorithms.In response to these challenges, we propose a domain-agnostic algorithm that only requires: (1) a stochastic method of generating trajectories, (2) the ability to forward-simulate the system's dynamics, and (3) the capability of testing whether an execution is successful.Exploiting the fact that we can quickly generate feasible state space trajectories [22], we formulate rearrangement planning under uncertainty (sections III and IV)

show abstract

Robotic motion planning in dynamic, cluttered, uncertain environments

Cited by 72 publications

References 49 publications

Motion planning under uncertainty using iterative local optimization in belief space

Motion planning under uncertainty using iterative local optimization in belief space

Integrated task and motion planning in belief space

Robust trajectory selection for rearrangement planning as a multi-armed bandit problem

Contact Info

Product

Resources

About