Parallel approach to motion planning in uncertain environments

2018 ◽  
Author(s):  
Mario Harper ◽  
Camilo Ordonez ◽  
Gordon Erlebacher ◽  
Emmanuel Collins
Keyword(s):  
Motion Planning ◽  
Uncertain Environments

Stochastic Optimal Motion Planning for the Attitude Kinematics of a Rigid Body With Non-Gaussian Uncertainties

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Taeyoung Lee
Keyword(s):  
Rigid Body ◽  
Motion Planning ◽  
Uncertainty Propagation ◽  
Planck Equation ◽  
Uncertain Environments ◽  
Stochastic Motion ◽  
Stochastic Optimal Control Problem ◽  
Optimal Motion Planning ◽  
Non Gaussian ◽  
Global Uncertainty

This paper investigates global uncertainty propagation and stochastic motion planning for the attitude kinematics of a rigid body. The Fokker–Planck equation on the special orthogonal group is numerically solved via noncommutative harmonic analysis to propagate a probability density function along flows of the attitude kinematics. Based on this, a stochastic optimal control problem is formulated to rotate a rigid body while avoiding obstacles within uncertain environments in an optimal fashion. The proposed intrinsic, geometric formulation does not require the common assumption that uncertainties are Gaussian or localized. It can be also applied to complex rotational maneuvers of a rigid body without singularities in a unified way. The desirable properties are illustrated by numerical examples.

On Receding Horizon Chance Constraint Motion Planning for Uncertain Multi-Agent Systems

2019 ◽  
Author(s):  
Yash Bagla ◽  
Vaibhav Srivastava
Keyword(s):  
Motion Planning ◽  
Autonomous Systems ◽  
Random Trees ◽  
Chance Constraint ◽  
Multi Agent Systems ◽  
Receding Horizon ◽  
Uncertain Environments ◽  
Uncertainty Model ◽  
Planning Algorithm ◽  
Multi Agent

Abstract We propose and study a motion planning algorithm for multi-agent autonomous systems to navigate through uncertain and dynamic environments. We use a receding horizon chance constraint framework that allows for tuning the trade-off between the risk of collision and the infeasibility of paths. We consider sampling-based incremental planning algorithms and extend them to the case of multiple agents and dynamic and uncertain environments. The receding horizon control framework is used to incorporate sensor measurements at a fixed interval of time to reduce uncertainty about agents’ state and environment. Our presentation focuses on rapidly-exploring random trees (RRTs) and the assumption of Gaussian noise in the uncertainty model. Our algorithm is illustrated using several examples.

scholarly journals Randomized Physics-Based Motion Planning for Grasping in Cluttered and Uncertain Environments

2018 ◽  
Vol 3 (2) ◽  
pp. 712-719 ◽  
Author(s):  
Muhayyuddin ◽  
Mark Moll ◽  
Lydia Kavraki ◽  
Jan Rosell
Keyword(s):  
Motion Planning ◽  
Uncertain Environments

scholarly journals I-Planner: Intention-aware motion planning using learning-based human motion prediction

2018 ◽  
Vol 38 (1) ◽  
pp. 23-39 ◽  
Author(s):  
Jae Sung Park ◽  
Chonhyon Park ◽  
Dinesh Manocha
Keyword(s):  
Motion Planning ◽  
Human Motion ◽  
Motion Prediction ◽  
Uncertain Environments ◽  
Human Actions ◽  
Online Planning ◽  
Human Motion Prediction ◽  
Offline Learning ◽  
Planning Algorithm ◽  
High Degree

We present a motion planning algorithm to compute collision-free and smooth trajectories for high-degree-of-freedom (high-DOF) robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real-world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.

scholarly journals Robot Motion Planning in Dynamic Uncertain Environments

2011 ◽  
Vol 25 (6-7) ◽  
pp. 849-870 ◽  
Author(s):  
Shu-Yun Chung ◽  
Han-Pang Huang
Keyword(s):  
Motion Planning ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Uncertain Environments
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