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Mark Moll

Also published under: M. Moll

Publications
38
Citations
2,774
Publications by Year
20002024
Co-Authors:
Show All Co-Authors (64)
Image of author Mark Moll

Mark Moll

Also published under: M. Moll

Affiliation

Metron
Inc.
Reston, VA, USA

Publication Topics

Biography

Dr. Mark Moll is a Principal Research Scientist focused on planning and autonomy at Metron, a leading company in providing mathematical and scientific tools that aid in decision making. Dr. Moll has worked in robotics for more than 25 years, with a focus on motion planning. Previously, he was the Director of Research at PickNik, a robotics software development and consultancy company that is supporting the MoveIt motion planning framework. Prior to that, he was a senior research scientist in the Computer Science Department at Rice University, where he led the development of the Open Motion Planning Library (OMPL), which is widely used in industry and academic research (often via MoveIt / ROS). He has over 85 peer-reviewed publications with ...
Publications
38
Citations
2,774
Publications by Year
20002024
Co-Authors:
Show All Co-Authors (64)
Author's Published Works

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3D Dubins Paths for Underwater Vehicles

Mark Moll

OCEANS 2024 - Halifax
Year: 2024 | Conference Paper |
The dynamics of unmanned underwater vehicles (UUVs) impose significant constraints on the types of paths that can be followed. A number of motion models have been proposed that are inspired by the Dubins vehicle model. We have investigated two state-of-the-art models that ensure a bounded turning rate, bounded pitch angle, and (in one case) a bounded pitch change rate. We will show that these exis...Show More

3D Dubins Paths for Underwater Vehicles

Mark Moll

Year: 2024 | Conference Paper |

HyperPlan: A Framework for Motion Planning Algorithm Selection and Parameter Optimization

Mark Moll;Constantinos Chamzas;Zachary Kingston;Lydia E. Kavraki

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
Over the years, many motion planning algorithms have been proposed. It is often unclear which algorithm might be best suited for a particular class of problems. The problem is compounded by the fact that algorithm performance can be highly dependent on parameter settings. This paper shows that hyperparameter optimization is an effective tool in both algorithm selection and parameter tuning over a ...Show More

HyperPlan: A Framework for Motion Planning Algorithm Selection and Parameter Optimization

Mark Moll;Constantinos Chamzas;Zachary Kingston;Lydia E. Kavraki

Year: 2021 | Conference Paper |

Informing Multi-Modal Planning with Synergistic Discrete Leads

Zachary Kingston;Andrew M. Wells;Mark Moll;Lydia E. Kavraki

2020 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2020 | Conference Paper |
Cited by: Papers (16)
Robotic manipulation problems are inherently continuous, but typically have underlying discrete structure, e.g., whether or not an object is grasped. This means many problems are multi-modal and in particular have a continuous infinity of modes. For example, in a pick-and-place manipulation domain, every grasp and placement of an object is a mode. Usually manipulation problems require the robot to...Show More

Informing Multi-Modal Planning with Synergistic Discrete Leads

Zachary Kingston;Andrew M. Wells;Mark Moll;Lydia E. Kavraki

Year: 2020 | Conference Paper |

Increasing Robot Autonomy via Motion Planning and an Augmented Reality Interface

Juan David Hernández;Shlok Sobti;Anthony Sciola;Mark Moll;Lydia E. Kavraki

IEEE Robotics and Automation Letters
Year: 2020 | Volume: 5, Issue: 2 | Journal Article |
Cited by: Papers (17)
Recently, there has been a growing interest in robotic systems that are able to share workspaces and collaborate with humans. Such collaborative scenarios require efficient mechanisms to communicate human requests to a robot, as well as to transmit robot interpretations and intents to humans. Recent advances in augmented reality (AR) technologies have provided an alternative for such communication...
First Page of the Article
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Increasing Robot Autonomy via Motion Planning and an Augmented Reality Interface

Juan David Hernández;Shlok Sobti;Anthony Sciola;Mark Moll;Lydia E. Kavraki

Year: 2020 | Volume: 5, Issue: 2 | Journal Article |

Lazy Evaluation of Goal Specifications Guided by Motion Planning

Juan David Hernández;Mark Moll;Lydia E. Kavraki

2019 International Conference on Robotics and Automation (ICRA)
Year: 2019 | Conference Paper |
Cited by: Papers (6)
Nowadays robotic systems are expected to share workspaces and collaborate with humans. In such collaborative environments, an important challenge is to ground or establish the correct semantic interpretation of a human request. Once such an interpretation is available, the request must be translated into robot motion commands in order to complete the desired task. It is not unusual that a human re...Show More

Lazy Evaluation of Goal Specifications Guided by Motion Planning

Juan David Hernández;Mark Moll;Lydia E. Kavraki

Year: 2019 | Conference Paper |

Online Multilayered Motion Planning with Dynamic Constraints for Autonomous Underwater Vehicles

Eduard Vidal;Mark Moll;Narcís Palomeras;Juan David Hernández;Marc Carreras;Lydia E. Kavraki

2019 International Conference on Robotics and Automation (ICRA)
Year: 2019 | Conference Paper |
Cited by: Papers (21)
Underwater robots are subject to complex hydro-dynamic forces. These forces define how the vehicle moves, so it is important to consider them when planning trajectories. However, performing motion planning considering the dynamics on the robot’s onboard computer is challenging due to the limited computational resources available. In this paper an efficient motion planning framework for autonomous ...Show More

Online Multilayered Motion Planning with Dynamic Constraints for Autonomous Underwater Vehicles

Eduard Vidal;Mark Moll;Narcís Palomeras;Juan David Hernández;Marc Carreras;Lydia E. Kavraki

Year: 2019 | Conference Paper |

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

Muhayyuddin;Mark Moll;Lydia Kavraki;Jan Rosell

IEEE Robotics and Automation Letters
Year: 2018 | Volume: 3, Issue: 2 | Journal Article |
Cited by: Papers (49)
Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This letter takes a different approach and proposes to address this problem by using a randomized physics-based motion planner that permits robot–object and...Show More

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

Muhayyuddin;Mark Moll;Lydia Kavraki;Jan Rosell

Year: 2018 | Volume: 3, Issue: 2 | Journal Article |

Robonaut 2 and you: Specifying and executing complex operations

William Baker;Zachary Kingston;Mark Moll;Julia Badger;Lydia E. Kavraki

2017 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO)
Year: 2017 | Conference Paper |
Cited by: Papers (12)
Crew time is a precious resource due to the expense of trained human operators in space. Efficient caretaker robots could lessen the manual labor load required by frequent vehicular and life support maintenance tasks, freeing astronaut time for scientific mission objectives. Humanoid robots can fluidly exist alongside human counterparts due to their form, but they are complex and high-dimensional ...Show More

Robonaut 2 and you: Specifying and executing complex operations

William Baker;Zachary Kingston;Mark Moll;Julia Badger;Lydia E. Kavraki

Year: 2017 | Conference Paper |

Planning feasible and safe paths online for autonomous underwater vehicles in unknown environments

Juan David Hernández;Mark Moll;Eduard Vidal;Marc Carreras;Lydia E. Kavraki

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2016 | Conference Paper |
Cited by: Papers (32)
We present a framework for planning collision-free and safe paths online for autonomous underwater vehicles (AUVs) in unknown environments. We build up on our previous work and propose an improved approach. While preserving its main modules (mapping, planning and mission handler), the framework now considers motion constraints to plan feasible paths, i.e., those that meet vehicle's motion capabili...Show More

Planning feasible and safe paths online for autonomous underwater vehicles in unknown environments

Juan David Hernández;Mark Moll;Eduard Vidal;Marc Carreras;Lydia E. Kavraki

Year: 2016 | Conference Paper |

Improving protein conformational sampling by using guiding projections

Anastasia Novinskaya;Didier Devaurs;Mark Moll;Lydia E. Kavraki

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2015 | Conference Paper |
Cited by: Papers (6)
Sampling-based motion planning algorithms from the field of robotics have been very successful in exploring the conformational space of proteins. However, studying the flexibility of large proteins with hundreds or thousands of Degrees of Freedom (DoFs) remains a big challenge. Large proteins are also highly-constrained systems, which makes them more challenging for standard robotic approaches.Show More

Improving protein conformational sampling by using guiding projections

Anastasia Novinskaya;Didier Devaurs;Mark Moll;Lydia E. Kavraki

Year: 2015 | Conference Paper |

Structure-guided selection of Specificity Determining Positions in the human kinome

Mark Moll;Paul W. Finn;Lydia E. Kavraki

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2015 | Conference Paper |
It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. A better understanding of binding selectivity would enhance the design of drugs that target only a subfamily, thereby minimizing possible side-effects. The increased availability of protein 3D structures has made it possible to stud...Show More

Structure-guided selection of Specificity Determining Positions in the human kinome

Mark Moll;Paul W. Finn;Lydia E. Kavraki

Year: 2015 | Conference Paper |

Benchmarking Motion Planning Algorithms: An Extensible Infrastructure for Analysis and Visualization

Mark Moll;Ioan A. Sucan;Lydia E. Kavraki

IEEE Robotics & Automation Magazine
Year: 2015 | Volume: 22, Issue: 3 | Magazine Article |
Cited by: Papers (87)
Motion planning is a key problem in robotics that is concerned with finding a path that satisfies a goal specification subject to constraints. In its simplest form, the solution to this problem consists of finding a path connecting two states, and the only constraint is to avoid collisions. Even for this version of the motion planning problem, there is no efficient solution for the general case [1...Show More

Benchmarking Motion Planning Algorithms: An Extensible Infrastructure for Analysis and Visualization

Mark Moll;Ioan A. Sucan;Lydia E. Kavraki

Year: 2015 | Volume: 22, Issue: 3 | Magazine Article |

A heuristic approach to finding diverse short paths

Caleb Voss;Mark Moll;Lydia E. Kavraki

2015 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2015 | Conference Paper |
Cited by: Papers (21)
We present an algorithm that seeks to find a set of diverse, short paths through a roadmap graph. The usefulness of a such a set is illustrated in robotic motion planning and routing applications wherein a precomputed roadmap of the environment is partially invalidated by some change, for example, relocation of obstacles or reconfiguration of the robot. Our algorithm employs the heuristic that nea...Show More

A heuristic approach to finding diverse short paths

Caleb Voss;Mark Moll;Lydia E. Kavraki

Year: 2015 | Conference Paper |

Experience-based planning with sparse roadmap spanners

David Coleman;Ioan A. Şucan;Mark Moll;Kei Okada;Nikolaus Correll

2015 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2015 | Conference Paper |
Cited by: Papers (47)
We present an experience-based planning framework called Thunder that learns to reduce computation time required to solve high-dimensional planning problems in varying environments. The approach is especially suited for large configuration spaces that include many invariant constraints, such as those found with whole body humanoid motion planning. Experiences are generated using probabilistic samp...Show More

Experience-based planning with sparse roadmap spanners

David Coleman;Ioan A. Şucan;Mark Moll;Kei Okada;Nikolaus Correll

Year: 2015 | Conference Paper |

Extending the Applicability of POMDP Solutions to Robotic Tasks

Devin K. Grady;Mark Moll;Lydia E. Kavraki

IEEE Transactions on Robotics
Year: 2015 | Volume: 31, Issue: 4 | Journal Article |
Cited by: Papers (30)
Partially observable Markov decision processes (POMDPs) are used in many robotic task classes from soccer to household chores. Determining an approximately optimal action policy for POMDPs is PSPACE-complete, and the exponential growth of computation time prohibits solving large tasks. This paper describes two techniques to extend the range of robotic tasks that can be solved using a POMDP. Our fi...Show More

Extending the Applicability of POMDP Solutions to Robotic Tasks

Devin K. Grady;Mark Moll;Lydia E. Kavraki

Year: 2015 | Volume: 31, Issue: 4 | Journal Article |

Fast stochastic motion planning with optimality guarantees using local policy reconfiguration

Ryan Luna;Morteza Lahijanian;Mark Moll;Lydia E. Kavraki

2014 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2014 | Conference Paper |
Cited by: Papers (5)
This work presents a framework for fast reconfiguration of local control policies for a stochastic system to satisfy a high-level task specification. The motion of the system is abstracted to a class of uncertain Markov models known as bounded-parameter Markov decision processes (BMDPs). During the abstraction, an efficient sampling-based method for stochastic optimal control is used to construct ...Show More

Fast stochastic motion planning with optimality guarantees using local policy reconfiguration

Ryan Luna;Morteza Lahijanian;Mark Moll;Lydia E. Kavraki

Year: 2014 | Conference Paper |

SMT-based synthesis of integrated task and motion plans from plan outlines

Srinivas Nedunuri;Sailesh Prabhu;Mark Moll;Swarat Chaudhuri;Lydia E. Kavraki

2014 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2014 | Conference Paper |
Cited by: Papers (40)
We present a new approach to integrated task and motion planning (ITMP) for robots performing mobile manipulation. In our approach, the user writes a high-level specification that captures partial knowledge about a mobile manipulation setting. In particular, this specification includes a plan outline that syntactically defines a space of plausible integrated plans, a set of logical requirements th...Show More

SMT-based synthesis of integrated task and motion plans from plan outlines

Srinivas Nedunuri;Sailesh Prabhu;Mark Moll;Swarat Chaudhuri;Lydia E. Kavraki

Year: 2014 | Conference Paper |

Active Planning, Sensing, and Recognition Using a Resource-Constrained Discriminant POMDP

Zhaowen Wang;Zhangyang Wang;Mark Moll;Po-Sen Huang;Devin Grady;Nasser Nasrabadi;Thomas Huang;Lydia Kavraki;Mark Hasegawa-Johnson

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops
Year: 2014 | Conference Paper |
Cited by: Papers (1)
In this paper, we address the problem of object class recognition via observations from actively selected views/modalities/features under limited resource budgets. A Partially Observable Markov Decision Process (POMDP) is employed to find optimal sensing and recognition actions with the goal of long-term classification accuracy. Heterogeneous resource constraints -- such as motion, number of measu...Show More

Active Planning, Sensing, and Recognition Using a Resource-Constrained Discriminant POMDP

Zhaowen Wang;Zhangyang Wang;Mark Moll;Po-Sen Huang;Devin Grady;Nasser Nasrabadi;Thomas Huang;Lydia Kavraki;Mark Hasegawa-Johnson

Year: 2014 | Conference Paper |

Automated model approximation for robotic navigation with POMDPs

Devin Grady;Mark Moll;Lydia E. Kavraki

2013 IEEE International Conference on Robotics and Automation
Year: 2013 | Conference Paper |
Cited by: Papers (12)
Partially-Observable Markov Decision Processes (POMDPs) are a problem class with significant applicability to robotics when considering the uncertainty present in the real world, however, they quickly become intractable for large state and action spaces. A method to create a less complex but accurate action model approximation is proposed and evaluated using a state-of-the-art POMDP solver. We app...Show More

Automated model approximation for robotic navigation with POMDPs

Devin Grady;Mark Moll;Lydia E. Kavraki

Year: 2013 | Conference Paper |

Anytime solution optimization for sampling-based motion planning

Ryan Luna;Ioan A. Şucan;Mark Moll;Lydia E. Kavraki

2013 IEEE International Conference on Robotics and Automation
Year: 2013 | Conference Paper |
Recent work in sampling-based motion planning has yielded several different approaches for computing good quality paths in high degree of freedom systems: path shortcutting methods that attempt to shorten a single solution path by connecting non-consecutive configurations, a path hybridization technique that combines portions of two or more solutions to form a shorter path, and asymptotically opti...Show More

Anytime solution optimization for sampling-based motion planning

Ryan Luna;Ioan A. Şucan;Mark Moll;Lydia E. Kavraki

Year: 2013 | Conference Paper |

Resolution Independent Density Estimation for motion planning in high-dimensional spaces

Bryant Gipson;Mark Moll;Lydia E. Kavraki

2013 IEEE International Conference on Robotics and Automation
Year: 2013 | Conference Paper |
Cited by: Papers (19)
This paper presents a new motion planner, Search Tree with Resolution Independent Density Estimation (STRIDE), designed for rapid exploration and path planning in high-dimensional systems (greater than 10). A Geometric Near-neighbor Access Tree (GNAT) is maintained to estimate the sampling density of the configuration space, allowing an implicit, resolution-independent, Voronoi partitioning to pro...Show More

Resolution Independent Density Estimation for motion planning in high-dimensional spaces

Bryant Gipson;Mark Moll;Lydia E. Kavraki

Year: 2013 | Conference Paper |

Multi-robot target verification with reachability constraints

D. K. Grady;M. Moll;C. Hegde;A. C. Sankaranarayanan;R. G. Baraniuk;L. E. Kavraki

2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)
Year: 2012 | Conference Paper |
In search and rescue applications it is important that mobile ground robots can verify whether a potential target/victim is indeed a target of interest. This paper describes a novel approach to multi-robot target verification of multiple static objects. Suppose a team of multiple mobile ground robots are operating in a partially known environment with knowledge of possible target locations and obs...Show More

Multi-robot target verification with reachability constraints

D. K. Grady;M. Moll;C. Hegde;A. C. Sankaranarayanan;R. G. Baraniuk;L. E. Kavraki

Year: 2012 | Conference Paper |

Multi-objective sensor-based replanning for a car-like robot

D. K. Grady;M. Moll;C. Hegde;A. C. Sankaranarayanan;R. G. Baraniuk;L. E. Kavraki

2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)
Year: 2012 | Conference Paper |
Cited by: Papers (3)
This paper studies a core problem in multi-objective mission planning for robots governed by differential constraints. The problem considered is the following. A car-like robot computes a plan to move from a start configuration to a goal region. The robot is equipped with a sensor that can alert it if an anomaly appears within some range while the robot is moving. In that case, the robot tries to ...Show More

Multi-objective sensor-based replanning for a car-like robot

D. K. Grady;M. Moll;C. Hegde;A. C. Sankaranarayanan;R. G. Baraniuk;L. E. Kavraki

Year: 2012 | Conference Paper |

The Open Motion Planning Library

Ioan A. Sucan;Mark Moll;Lydia E. Kavraki

IEEE Robotics & Automation Magazine
Year: 2012 | Volume: 19, Issue: 4 | Magazine Article |
The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently...Show More

The Open Motion Planning Library

Ioan A. Sucan;Mark Moll;Lydia E. Kavraki

Year: 2012 | Volume: 19, Issue: 4 | Magazine Article |

Teaching motion planning concepts to undergraduate students

Mark Moll;Ioan A. Şucan;Janice Bordeaux;Lydia E. Kavraki

Advanced Robotics and its Social Impacts
Year: 2011 | Conference Paper |
Motion planning is a central problem in robotics. Although it is an engaging topic for undergraduate students, it is difficult to teach, and as a result, the material is often only covered at an abstract level. Deep learning could be achieved by having students implement and test different algorithms. However, there is usually no time within a single class to have students completely implement sev...Show More

Teaching motion planning concepts to undergraduate students

Mark Moll;Ioan A. Şucan;Janice Bordeaux;Lydia E. Kavraki

Year: 2011 | Conference Paper |

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