The Experts below are selected from a list of 49737 Experts worldwide ranked by ideXlab platform

Shuai D Han - One of the best experts on this subject based on the ideXlab platform.

  • ddm fast near optimal multi robot path planning using diversified path and optimal sub problem solution database heuristics
    International Conference on Robotics and Automation, 2020
    Co-Authors: Shuai D Han
    Abstract:

    We propose a novel centralized and decoupled algorithm, DDM , for solving multi-robot path planning problems in grid graphs, targeting on-demand and automated warehouse-like settings. Two settings are studied: a traditional one whose objective is to move a set of robots from their respective initial vertices to the Goal vertices as quickly as possible, and a dynamic one which requires frequent re-planning to accommodate for Goal Configuration adjustments. Among other techniques, DDM is mainly enabled through exploiting two innovative heuristics: path diversification and optimal sub-problem solution databases. The two heuristics attack two distinct phases of a decoupling-based planner: while path diversification allows the more effective use of the entire workspace for robot travel, optimal sub-problem solution databases facilitate the fast resolution of local path conflicts. Extensive evaluation demonstrates that DDM achieves high levels of scalability and solution quality close to the optimum.

  • sear a polynomial time multi robot path planning algorithm with expected constant factor optimality guarantee
    Intelligent Robots and Systems, 2018
    Co-Authors: Shuai D Han, Edgar J Rodriguez
    Abstract:

    We study the labeled multi-robot path planning problem in continuous 2D and 3D domains in the absence of obstacles where robots must not collide with each other. For an arbitrary number of robots in arbitrary initial and Goal arrangements, we derive a polynomial time, complete algorithm that produces solutions with constant-factor optimality guarantees on both makespan and distance optimality, in expectation, under the assumption that the robot labels are uniformly randomly distributed. Our algorithm only requires a small constant-factor expansion of the initial and Goal Configuration footprints for solving the problem, i.e., the problem can be solved in a fairly small bounded region. Beside theoretical guarantees, we present a thorough computational evaluation of the proposed solution. In addition to the baseline implementation, adapting an effective (but non-polynomial time) routing subroutine, we also provide a highly efficient implementation that quickly computes near-optimal solutions. Hardware experiments on the microMVP platform composed of non-holonomic robots confirms the practical applicability of our algorithmic pipeline.

  • sear a polynomial time expected constant factor optimal algorithmic framework for multi robot path planning
    arXiv: Multiagent Systems, 2017
    Co-Authors: Shuai D Han, Edgar J Rodriguez
    Abstract:

    This work studies the labeled multi-robot path and motion planning problem in continuous domains, in the absence of static obstacles. Given $n$ robots which may be arbitrarily close to each other and assuming random start and Goal Configurations for the robots, we derived an $O(n^3)$, complete algorithm that produces solutions with constant-factor optimality guarantees on both makespan and distance optimality, in expectation. Furthermore, our algorithm only requires a small constant factor expansion of the initial and Goal Configuration footprints for solving the problem. In addition to strong theoretical guarantees, we present a thorough computational evaluation of the proposed solution. Beyond the baseline solution, adapting an effective (but non-polynomial time) robot routing subroutine, we also provide a highly efficient implementation that quickly computes near-optimal solutions. Hardware experiments on microMVP platform composed of non-holonomic robots confirms the practical applicability of our algorithmic pipeline.

Mark H Overmars - One of the best experts on this subject based on the ideXlab platform.

  • roadmap based motion planning in dynamic environments
    IEEE Transactions on Robotics, 2005
    Co-Authors: J P Van Den Berg, Mark H Overmars
    Abstract:

    In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a Goal Configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A/sup */-search to find a global trajectory to the Goal Configuration. The approach is applicable to any robot type in Configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments.

  • roadmap based motion planning in dynamic environments
    Intelligent Robots and Systems, 2004
    Co-Authors: J P Van Den Berg, Mark H Overmars
    Abstract:

    In this paper a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap an approximate time-optimal trajectory from a start to a Goal Configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a search for a shortest path on an implicit grid in state-time space. The approach is applicable to any robot type in Configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for a free-flying robot in a three-dimensional workspace and experiments show that the method achieves interactive performance in complex environments.

Alexander L Wolf - One of the best experts on this subject based on the ideXlab platform.

  • Deployment and dynamic reConfiguration planning for distributed software systems
    Software Quality Journal, 2007
    Co-Authors: Naveed Arshad, Dennis Heimbigner, Alexander L Wolf
    Abstract:

    The quality of software systems are determined in part by their optimal Configurations. Optimal Configurations are desired when the software is being deployed and during its lifetime. However, initial deployment and subsequent dynamic reConfiguration of a software system is difficult because of the interplay of many interdependent factors, including cost, time, application state, and system resources. As the size and complexity of software systems increases, procedures (manual or automated) that assume a static software architecture and environment are becoming untenable. We have developed a novel technique for carrying out the deployment and reConfiguration planning processes that leverages recent advances in the field of temporal planning. We describe a tool called Planit, which manages the deployment and reConfiguration of a software system utilizing a temporal planner. Given a model of the structure of a software system, the network upon which the system should be hosted, and a Goal Configuration, Planit will use the temporal planner to devise possible deployments of the system. Given information about changes in the state of the system, network and a revised Goal, Planit will use the temporal planner to devise possible reConfigurations of the system. We present the results of a case study in which Planit is applied to a system consisting of various components that communicate across an application-level overlay network.

  • deployment and dynamic reConfiguration planning for distributed software systems
    International Conference on Tools with Artificial Intelligence, 2003
    Co-Authors: Naveed Arshad, Dennis Heimbigner, Alexander L Wolf
    Abstract:

    Initial deployment and subsequent dynamic reConfiguration of a software system is difficult because of the interplay of many interdependent factors, including cost, time, application state, and system resources. As the size and complexity of software systems increases, procedures (manual or automated) that assume a static software architecture and environment are becoming untenable. We have developed a novel technique for carrying out the deployment and reConfiguration planning processes that leverages recent advances in the field of temporal planning. We describe a tool called Planit, which manages the deployment and reConfiguration of a software system utilizing a temporal planner. Given a model of the structure of a software system, the network upon which the system should be hosted, and a Goal Configuration, Planit will use the temporal planner to devise possible deployments of the system. Given information about changes in the state of the system, network and a revised Goal, Planit will use the temporal planner to devise possible reConfigurations of the system. We present the results of a case study in which Planit is applied to a system consisting of various components that communicate across an application-level overlay network.

J P Van Den Berg - One of the best experts on this subject based on the ideXlab platform.

  • roadmap based motion planning in dynamic environments
    IEEE Transactions on Robotics, 2005
    Co-Authors: J P Van Den Berg, Mark H Overmars
    Abstract:

    In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a Goal Configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A/sup */-search to find a global trajectory to the Goal Configuration. The approach is applicable to any robot type in Configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments.

  • roadmap based motion planning in dynamic environments
    Intelligent Robots and Systems, 2004
    Co-Authors: J P Van Den Berg, Mark H Overmars
    Abstract:

    In this paper a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap an approximate time-optimal trajectory from a start to a Goal Configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a search for a shortest path on an implicit grid in state-time space. The approach is applicable to any robot type in Configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for a free-flying robot in a three-dimensional workspace and experiments show that the method achieves interactive performance in complex environments.

Naveed Arshad - One of the best experts on this subject based on the ideXlab platform.

  • Deployment and dynamic reConfiguration planning for distributed software systems
    Software Quality Journal, 2007
    Co-Authors: Naveed Arshad, Dennis Heimbigner, Alexander L Wolf
    Abstract:

    The quality of software systems are determined in part by their optimal Configurations. Optimal Configurations are desired when the software is being deployed and during its lifetime. However, initial deployment and subsequent dynamic reConfiguration of a software system is difficult because of the interplay of many interdependent factors, including cost, time, application state, and system resources. As the size and complexity of software systems increases, procedures (manual or automated) that assume a static software architecture and environment are becoming untenable. We have developed a novel technique for carrying out the deployment and reConfiguration planning processes that leverages recent advances in the field of temporal planning. We describe a tool called Planit, which manages the deployment and reConfiguration of a software system utilizing a temporal planner. Given a model of the structure of a software system, the network upon which the system should be hosted, and a Goal Configuration, Planit will use the temporal planner to devise possible deployments of the system. Given information about changes in the state of the system, network and a revised Goal, Planit will use the temporal planner to devise possible reConfigurations of the system. We present the results of a case study in which Planit is applied to a system consisting of various components that communicate across an application-level overlay network.

  • deployment and dynamic reConfiguration planning for distributed software systems
    International Conference on Tools with Artificial Intelligence, 2003
    Co-Authors: Naveed Arshad, Dennis Heimbigner, Alexander L Wolf
    Abstract:

    Initial deployment and subsequent dynamic reConfiguration of a software system is difficult because of the interplay of many interdependent factors, including cost, time, application state, and system resources. As the size and complexity of software systems increases, procedures (manual or automated) that assume a static software architecture and environment are becoming untenable. We have developed a novel technique for carrying out the deployment and reConfiguration planning processes that leverages recent advances in the field of temporal planning. We describe a tool called Planit, which manages the deployment and reConfiguration of a software system utilizing a temporal planner. Given a model of the structure of a software system, the network upon which the system should be hosted, and a Goal Configuration, Planit will use the temporal planner to devise possible deployments of the system. Given information about changes in the state of the system, network and a revised Goal, Planit will use the temporal planner to devise possible reConfigurations of the system. We present the results of a case study in which Planit is applied to a system consisting of various components that communicate across an application-level overlay network.