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NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments [article]

Fei Meng, Liangliang Chen, Han Ma, Jiankun Wang, Max Q.-H. Meng
2022 arXiv   pre-print
In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT).  ...  Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty.  ...  Besides, it is interesting to investigate path planning for robots under motion uncertainties [10] . arXiv:2205.06951v1 [cs.RO] 14 May 2022 (a) NR-RRT (b) RRT-SOS Fig. 2 : 2 Fig. 2: Block diagram illustrating  ... 
arXiv:2205.06951v1 fatcat:bq6kwbip3vaiffo2wgnrqzh3zq

Robust Motion Planning in the Presence of Estimation Uncertainty [article]

Lars Lindemann, Matthew Cleaveland, Yiannis Kantaros, George J. Pappas
2021 arXiv   pre-print
We provide robustness guarantees and show, both in theory and simulations, that the induced robustness margins constitute a trade-off between conservatism and robustness for planning under estimation uncertainty  ...  This results in a novel motion planning problem where safety must be ensured in the presence of state estimation uncertainty.  ...  In particular, we proposed a novel sampling-based approach that introduces robustness margins into the offline planning to account for uncertainty in the state estimates based on a Kalman filter.  ... 
arXiv:2108.11983v1 fatcat:up4m6woz2vb3xj2agg3gdrgg4e

Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

Weria Khaksar, Md Zia Uddin, Jim Torresen
2019 International Journal of Applied Mathematics and Computer Science  
Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms.  ...  One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise.  ...  Acknowledgment This work is supported by the Research Council of Norway as part of the Multimodal Elderly Care Systems (MECS) project, under the grant agreement no. 247697.  ... 
doi:10.2478/amcs-2019-0047 fatcat:b3la7ggx5nafbn6w6staekhsfq

Risk-Aware Reasoning for Autonomous Vehicles [article]

Majid Khonji, Jorge Dias, Lakmal Seneviratne
2019 arXiv   pre-print
In this paper, we propose a system architecture for risk-aware AVs capable of reasoning about uncertainty and deliberately bounding the risk of collision below a given threshold.  ...  Third, a planning subsystem that takes into account the uncertainty, from perception and intention recognition subsystems, and propagates all the way to control policies that explicitly bound the risk  ...  Probabilistic predictions are beneficial in many safety-critical tasks such as collision checking and risk-aware motion planning.  ... 
arXiv:1910.02461v1 fatcat:cng3d2v6crdeflvy735yyl64di

Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs [article]

Brian Ichter and Edward Schmerling and Ali-akbar Agha-mohammadi and Marco Pavone
2017 arXiv   pre-print
In this paper we present the PUMP (Parallel Uncertainty-aware Multiobjective Planning) algorithm for addressing the stochastic kinodynamic motion planning problem, whereby one seeks a low-cost, dynamically-feasible  ...  The results show that this multiobjective search achieves a lower motion plan cost, for the same CP constraint, compared to a safety buffer-based search heuristic and repeated RRT trials.  ...  Our key contribution is to introduce the Parallel Uncertainty-aware Multiobjective Planning algorithm, a novel approach to planning under uncertainty that initially employs a multiobjective search to build  ... 
arXiv:1607.06886v3 fatcat:aot7skrfi5frzll3yok5mr5qoq

Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments [article]

Èric Pairet, Juan David Hernández, Marc Carreras, Yvan Petillot, Morteza Lahijanian
2020 arXiv   pre-print
In order to cope with these constraints, this manuscript proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees.  ...  The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: (i) incrementally mapping the surroundings to build an uncertainty-aware representation of the  ...  Acknowledgements The authors are grateful to Michael Mistry and Paola Ardón for all support and helpful discussions about this work.  ... 
arXiv:2004.12317v2 fatcat:5shpev4m3bfwlflzmobkr4t3xu

Efficient motion planning for problems lacking optimal substructure [article]

Oren Salzman and Brian Hou and Siddhartha Srinivasa
2017 arXiv   pre-print
We consider the motion-planning problem of planning a collision-free path of a robot in the presence of risk zones.  ...  of the risk zone.  ...  Acknowledgements The authors thank Chris Dellin and Shushman Choudhury for their contribution at the early stages of the this work.  ... 
arXiv:1703.02582v2 fatcat:q2xo76m6rfg2re6gmmoakbvm6u

Fast-reactive probabilistic motion planning for high-dimensional robots [article]

Siyu Dai, Andreas Hofmann, Brian C. Williams
2020 arXiv   pre-print
motion planning approaches developed for car-like robots can not be directly applied to high-dimensional robots.  ...  In this paper, we present probabilistic Chekov (p-Chekov), a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation  ...  Fast-reactive risk-aware motion planning for highdimensional robots like humanoid robots, however, is a very challenging task.  ... 
arXiv:2012.02118v1 fatcat:5ffjavngpfej3er3kiv5at6t5q

A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods [article]

Jonathan D. Gammell, Marlin P. Strub
2020 arXiv   pre-print
Recent results have proven that some sampling-based planning methods probabilistically converge towards the optimal solution as computational effort approaches infinity.  ...  Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot's limitations and constraints.  ...  Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms.  ... 
arXiv:2009.10484v1 fatcat:idhjoeu3snhljmmhvhodqd4cca

Mobile manipulator planning under uncertainty in unknown environments

Vinay Pilania, Kamal Gupta
2018 The international journal of robotics research  
We present a sampling-based mobile manipulator planner that considers the base pose uncertainty and the effects of this uncertainty on manipulator motions.  ...  We call this overall planner HAMP-BUA, where BUA stands for Base pose Uncertainty and its propagation to Arm motions.  ...  This is, we believe, one of the reasons, why planning under uncertainty for mobile manipulators largely ignores the manipulator and researchers simply choose to go with 2D planning for mobile base.  ... 
doi:10.1177/0278364918754677 fatcat:u7suacsyujfqdckgupfqprlrzu

A risk-aware architecture for resilient spacecraft operations

Catharine L. R. McGhan, Richard M. Murray, Romain Serra, Michel D. Ingham, Masahiro Ono, Tara Estlin, Brian C. Williams
2015 2015 IEEE Aerospace Conference  
In this paper we discuss a resilient, risk-aware software architecture for onboard, real-time autonomous operations that is intended to robustly handle uncertainty in spacecraft behavior within hazardous  ...  decisions without waiting for slow ground-based reactions.  ...  under a grant from the Keck Institute for Space Studies.  ... 
doi:10.1109/aero.2015.7119035 fatcat:vxg6cm36r5aajh6wu54nk3l6li

GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

Atul Thakur, Petr Svec, Satyandra K. Gupta
2012 Robotics and Autonomous Systems  
State transition model is a key component of Markov Decision Process (MDP), which is a natural framework to formulate the problem of trajectory planning under motion uncertainty.  ...  GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning.  ...  under the motion uncertainty.  ... 
doi:10.1016/j.robot.2012.07.009 fatcat:24u37ugwyfcivkhha5cnh7s5sq

Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

Georges S. Aoude, Brandon D. Luders, Joshua M. Joseph, Nicholas Roy, Jonathan P. How
2013 Autonomous Robots  
Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented.  ...  This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based  ...  While such optimizations have been demonstrated for real-time path planning, they lack the scalability with respect to problem complexity inherent to sampling-based algorithms, a crucial consideration  ... 
doi:10.1007/s10514-013-9334-3 fatcat:755kfakbongk7ieemi6f7xqqhi

Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion [article]

Amanda Bouman, Muhammad Fadhil Ginting, Nikhilesh Alatur, Matteo Palieri, David D. Fan, Thomas Touma, Torkom Pailevanian, Sung-Kyun Kim, Kyohei Otsu, Joel Burdick, Ali-akbar Agha-mohammadi
2020 arXiv   pre-print
In particular, we discuss the behaviors and capabilities which emerge from the integration of the autonomy architecture NeBula (Networked Belief-aware Perceptual Autonomy) with next-generation mobility  ...  ACKNOWLEDGMENT The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004  ...  (5) Risk-Aware/Perception-Aware Planning: Next we define a risk measure that takes perceptual capabilities and uncertainties into account when planning trajectories.  ... 
arXiv:2010.09259v2 fatcat:ofndllhsqbedpibb7uarv4p62q

Incorporating perception uncertainty in human-aware navigation: A comparative study

Zeynab Talebpour, Deepak Viswanathan, Rodrigo Ventura, Gwenn Englebienne, Alcherio Martinoli
2016 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)  
A social path planner based on the fast marching method has been augmented to account for the uncertainty in the positions of people.  ...  In this work, we present a novel approach to human-aware navigation by probabilistically modelling the uncertainty of perception for a social robotic system and investigating its effect on the overall  ...  The navigation system is based on FMM for motion planning, together with a Dynamic Window Approach (DWA) algorithm for guidance and obstacle avoidance [25] .  ... 
doi:10.1109/roman.2016.7745175 dblp:conf/ro-man/TalebpourVVEM16 fatcat:riummiuspbcl7al2aqv42ms2la
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