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Motion planning under uncertainty for robotic tasks with long time horizons
2010
The international journal of robotics research
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By using probabilistic sampling, pointbased POMDP solvers have drastically improved the speed of POMDP planning, enabling POMDPs to handle moderately complex robotic tasks. ...
We tested MiGS in simulation on several difficult POMDPs modeling distinct robotic tasks with long time horizons; they are impossible with the fastest point-based POMDP solvers today. ...
This work is supported in part by AcRF grant R-252-000-327-112 from the Ministry of Education of Singapore. ...
doi:10.1177/0278364910386986
fatcat:wm2pkgpgcbewvc6gyjiycxo4tm
Identification of probabilistic approaches and map-based navigation in motion planning for mobile robots
2018
Sadhana (Bangalore)
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Future research prospects in multi-robot path planning based on probabilistic approaches are also discussed. ...
RMP is a complex task when it needs to be planned for a group of robots in a coordinated environment with leader-follower relationship. ...
Secondarily, to evaluate PRM actions efficiently in the context of uncertainty and then generate motion plans with minimal error. Further, PRM is a probability-based planning method. ...
doi:10.1007/s12046-017-0776-8
fatcat:hnek2eu7w5gv7ky3sr3uz5glrm
Path planning in belief space with pose SLAM
2011
2011 IEEE International Conference on Robotics and Automation
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We present a method that devises optimal navigation strategies by searching for the path in the pose graph with lowest accumulated robot pose uncertainty, independently of the map reference frame. ...
In contrast, we argue in this paper that Pose SLAM graphs can be directly used as belief roadmaps. ...
The most successful path planning methods are those based on randomized sampling such as the Probabilistic Roadmaps or the Rapidly-exploring Random Trees in which samples are stochastically drawn in the ...
doi:10.1109/icra.2011.5979742
dblp:conf/icra/ValenciaAP11
fatcat:2cce5pyryjbg7b6bbttawf4gle
Path Planning in Belief Space with Pose SLAM
[chapter]
2017
Springer Tracts in Advanced Robotics
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We present a method that devises optimal navigation strategies by searching for the path in the pose graph with lowest accumulated robot pose uncertainty, independently of the map reference frame. ...
In contrast, we argue in this paper that Pose SLAM graphs can be directly used as belief roadmaps. ...
The most successful path planning methods are those based on randomized sampling such as the Probabilistic Roadmaps or the Rapidly-exploring Random Trees in which samples are stochastically drawn in the ...
doi:10.1007/978-3-319-60603-3_4
fatcat:lujqzbyftrd77inbxrdm3pcwju
Motion Planning Under Uncertainty: Application to an Unmanned Helicopter
2007
Journal of Guidance Control and Dynamics
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The motion planner involves a high-level planner which plans against the uncertainty in the environment and issues its commands as a series of waypoints for a lowerlevel controller to track. ...
The optimal path for the unmanned helicopter is planned using a priori knowledge about the environment, and the sensor data received as the helicopter navigates the obstacles in the environment. ...
Probabilistic roadmaps (PRM) are a recent development in motion planning and are an efficient method for determining an optimal path. ...
doi:10.2514/1.25077
fatcat:dxo3beumgnhlrb5xo7ehjj6mhm
Stein Variational Probabilistic Roadmaps
[article]
2022
arXiv
pre-print
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We posit that such uncertainty in environment geometry can, in fact, help drive the sampling process in generating feasible, and probabilistically-safe planning graphs. ...
Our approach, Stein Variational Probabilistic Roadmap (SV-PRM), results in sample-efficient generation of planning-graphs and large improvements over traditional sampling approaches. ...
INTRODUCTION AND RELATED WORK Probabilistic roadmaps approximate a continuous configuration space with a discrete set of sampled configurations [1, 2] . ...
arXiv:2111.02972v2
fatcat:ojp6djjuxzdpbd27h2ifrbg2be
Task-Motion Planning for Navigation in Belief Space
[article]
2019
arXiv
pre-print
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We discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in indoor domains, returning a plan that is optimal at the task-level. ...
We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environment. ...
In contrast, we use a temporal task planner, POPF-TIF [9] with roadmap based sampling, incorporating robot state uncertainty. Srivastava et al. ...
arXiv:1910.11683v1
fatcat:krsoyt2ozvgajfdllagjekh4t4
Robust Belief Roadmap: Planning Under Intermittent Sensing
[article]
2013
arXiv
pre-print
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Computational results demonstrate the benefit of the approach and comparisons are made with the state of the art in path planning under state uncertainty. ...
In this paper, we extend the recent body of work on planning under uncertainty to include the fact that sensors may not provide any measurement owing to misdetection. ...
Some algorithms assume uncertainty in the map used for navigation. ...
arXiv:1304.7256v2
fatcat:lyjm35otnbeljf7wb7ott2342m
Bootstrapping navigation and path planning using human positional traces
2013
2013 IEEE International Conference on Robotics and Automation
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Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. ...
as Probabilistic Roadmaps (PRM) can effectively utilise. ...
as Probabilistic Roadmaps (PRM) can effectively utilise. ...
doi:10.1109/icra.2013.6630730
dblp:conf/icra/AlempijevicFK13
fatcat:skotfnf56vdorb4pl2iiv3qfya
Motion Planning under Uncertainty for Robotic Tasks with Long Time Horizons
[chapter]
2011
Springer Tracts in Advanced Robotics
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Motion planning with imperfect state information is a crucial capability for autonomous robots to operate reliably in uncertain and dynamic environments. ...
Partially observable Markov decision processes (POMDPs) provide a principled general framework for planning under uncertainty. ...
We thank Sylvie Ong and Shao Wei Png for reading the first draft of this paper and helping with scripting a POMDP model. ...
doi:10.1007/978-3-642-19457-3_10
fatcat:3kyeqqx7yzhkpbkp5nppnh3aaa
An Overview of Planning Technology in Robotics
[chapter]
2004
Lecture Notes in Computer Science
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We present here an overview of several planning techniques in robotics. ...
We will not be concerned with the synthesis of abstract mission and task plans, using well known classical and other domain-independent planning techniques. ...
The algorithm Probabilistic-Roadmap ( Figure 2 ) starts initially with an empty roadmap. ...
doi:10.1007/978-3-540-30221-6_3
fatcat:6eyehtwszjaxjb2zenrygkitpi
Variational End-to-End Navigation and Localization
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1811.10119v1 fatcat:vb4qbifalzbx7ipb54nvogqwxm
In this paper, we extend end-to-end driving networks with the ability to perform point-to-point navigation as well as probabilistic localization using only noisy GPS data. ...
We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control ...
and uncertainty about the longer-term plan. ...
arXiv:1811.10119v2
fatcat:bsfle3yp6rejbok7ewpg5mssva
Construction and use of roadmaps that incorporate workspace modeling errors
2013
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
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In this paper, we present a method for roadmap construction that can be used in workspaces with uncertainty in the model. ...
Probabilistic Roadmap Methods (PRMs) have been shown to work well at solving high Degree of Freedom (DoF) motion planning problems. ...
Planning With Uncertainty The two main types of uncertainty are model uncertainty and motion uncertainty. ...
doi:10.1109/iros.2013.6696512
dblp:conf/iros/MaloneMWT13
fatcat:e7puc4fuwvadlfzseaqy6z4hjy
Generalized sampling based motion planners with application to nonholonomic systems
2009
2009 IEEE International Conference on Systems, Man and Cybernetics
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as uncertainties in the robot map/ workspace. ...
In this paper, generalized versions of the probabilistic sampling based planners, Probabilisitic Road Maps (PRM) and Rapidly exploring Random Tree (RRT), are presented. ...
Generalized Probabilistic Roadmap (GPRM) In motion planning, the objective is to plan the path of a robot from a start state to an end state. ...
doi:10.1109/icsmc.2009.5346705
dblp:conf/smc/ChakravortyK09
fatcat:xt6jv3qy2zbplcgrpazeynnbve
Study of Wrinkling and Thinning Behavior in The Stamping Process of Top Outer Hatchback Part on The SCGA and SPCC Materials
2020
Advances in Science, Technology and Engineering Systems
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This yield to a lot of improvement and suggestions in many areas related to mobile robot such as path planning. ...
, interact with objects and making quick decision. ...
Local navigational approaches are more intelligence since they need to interact with the dynamic environment and execute plan autonomously. These methods are classified as in Figure 1 . ...
doi:10.25046/aj050330
fatcat:5gulgqyuonbqvfb3vgw4e73fke
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