12,780 research outputs found

    Optimal Motion Planning with constraints for mobile robot navigation

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    Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 34-36).Motion planning is the process of planning a sequence of motions to move an object from one configuration to another. Recently, randomized techniques known as PRMs have shown great potential for solving motion planning problems in complicated high-dimensional space. Motion Planning, or path planning for robots, becomes increasing difficult as the dimension of the planning space increases with the robot's degrees of freedom (dof). While the running time of deterministic motion planning algorithms grows exponentially with an increase in dof, PRMs can produce solutions in times that do not depend on the dof but only the difficulty of the problem. PRMs randomly generate collision free configurations in a robot's Configuration-space (Cspace), representing feasible positions and orientations for the robot. Nearby configurations are linked together by so-called local planners, and these connections are edges in a roadmap, a graph containing representative discrete paths the robot may travel. We present methods to extract optimal paths from roadmap-based motion planners. Our system uses Markov-like states and flexible goal states so that general optimization criteria including collision detection, kinematic/dynamic constraints, or minimum clearance can be used in various applications. Our algorithm is an augmented version of Dijkstra's shortest path algorithm. We present simulation results maximizing minimum path clearance, minimizing localization effort, and enforcing kinematic/ dynamic constraints

    Sampling-based optimal kinodynamic planning with motion primitives

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    This paper proposes a novel sampling-based motion planner, which integrates in RRT* (Rapidly exploring Random Tree star) a database of pre-computed motion primitives to alleviate its computational load and allow for motion planning in a dynamic or partially known environment. The database is built by considering a set of initial and final state pairs in some grid space, and determining for each pair an optimal trajectory that is compatible with the system dynamics and constraints, while minimizing a cost. Nodes are progressively added to the tree {of feasible trajectories in the RRT* by extracting at random a sample in the gridded state space and selecting the best obstacle-free motion primitive in the database that joins it to an existing node. The tree is rewired if some nodes can be reached from the new sampled state through an obstacle-free motion primitive with lower cost. The computationally more intensive part of motion planning is thus moved to the preliminary offline phase of the database construction at the price of some performance degradation due to gridding. Grid resolution can be tuned so as to compromise between (sub)optimality and size of the database. The planner is shown to be asymptotically optimal as the grid resolution goes to zero and the number of sampled states grows to infinity

    An Extensible Benchmarking Infrastructure for Motion Planning Algorithms

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    Sampling-based planning algorithms are the most common probabilistically complete algorithms and are widely used on many robot platforms. Within this class of algorithms, many variants have been proposed over the last 20 years, yet there is still no characterization of which algorithms are well-suited for which classes of problems. This has motivated us to develop a benchmarking infrastructure for motion planning algorithms. It consists of three main components. First, we have created an extensive benchmarking software framework that is included with the Open Motion Planning Library (OMPL), a C++ library that contains implementations of many sampling-based algorithms. Second, we have defined extensible formats for storing benchmark results. The formats are fairly straightforward so that other planning libraries could easily produce compatible output. Finally, we have created an interactive, versatile visualization tool for compact presentation of collected benchmark data. The tool and underlying database facilitate the analysis of performance across benchmark problems and planners.Comment: Submitted to IEEE Robotics & Automation Magazine (Special Issue on Replicable and Measurable Robotics Research), 201

    RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning

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    Motivated by what is required for real-time path planning, the paper starts out by presenting sRMPD, a new recursive "local" planner founded on the key notion that, unless made necessary by an obstacle, there must be no deviation from the shortest path between any two points, which would normally be a straight line path in the configuration space. Subsequently, we increase the power of sRMPD by using it as a "connect" subroutine call in a higher-level sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence, mRMPD spawns a larger number of space exploring trees in regions of the configuration space that are characterized by a higher density of obstacles. The overall effect is a hybrid tree growing strategy with a trade-off between random exploration as made possible by multi-RRT based logic and immediate exploitation of opportunities to connect two states as made possible by sRMPD. The mRMPD planner can be biased with regard to this trade-off for solving different kinds of planning problems efficiently. Based on the test cases we have run, our experiments show that mRMPD can reduce planning time by up to 80% compared to basic RRT
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