5 research outputs found

    Moving objects and removing obstacles with two robotic hands

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    This work deals with the problem of planning the movements of a two-hand system in order to grasp an object with one hand and using the other to remove potential obstacles. The approach is based on a Probabilistic Road Map that does not rule out samples with collisions with removable objects but instead classifies them according to the collided obstacle(s), and allows the search of free paths with the indication of which objects must be removed from the work-space to make the path be valid. The approach has been implemented and some examples are presented in this work

    ADAPTIVE PROBABILISTIC ROADMAP CONSTRUCTION WITH MULTI-HEURISTIC LOCAL PLANNING

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    The motion planning problem means the computation of a collision-free motion for a movable object among obstacles from the given initial placement to the given end placement. Efficient motion planning methods have many applications in many fields, such as robotics, computer aided design, and pharmacology. The problem is known to be PSPACE-hard. Because of the computational complexity, practical applications often use heuristic or incomplete algorithms. Probabilistic roadmap is a probabilistically complete motion planning method that has been an object of intensive study over the past years. The method is known to be susceptible to the problem of “narrow passages”: Finding a motion that passes a narrow, winding tunnel can be very expensive. This thesis presents a probabilistic roadmap method that addresses the narrow passage problem with a local planner based on heuristic search. The algorithm is suitable for planning motions for rigid bodies and articulated robots including multirobot systems with many degrees-of-freedom. Variants of the algorithm are describe

    Motion Planning under Uncertainty for Autonomous Navigation of Mobile Robots and UAVs

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    This thesis presents a reliable and efficient motion planning approach based on state lattices for the autonomous navigation of mobile robots and UAVs. The proposal retrieves optimal paths in terms of safety and traversal time, and deals with the kinematic constraints and the motion and sensing uncertainty at planning time. The efficiency is improved by a novel graduated fidelity state lattice which adapts to the obstacles in the map and the maneuverability of the robot, and by a new multi-resolution heuristic which reduces the computational complexity. The motion planner also includes a novel method to reliably estimate the probability of collision of the paths considering the uncertainty in heading and the robot dimensions

    Using trails to improve map generation for virtual agents in large scale, online environments

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    This thesis looks at improving the generation of maps for intelligent virtual agents in large scale environments. Virtual environments are growing larger in size and becoming more complex. There is a major challenge in providing agents that are able to autonomously generate their own map representations of the environment for use in navigation. Currently, map generation for agents in large scale virtual environments is performed either by hand or requires a lengthy pre-processing step where the map is built online. We are interested in environments where this process is not possible, such as those that encourage user generated content. We look at improving map generation in these environments by using trails. Trails are a set of observations of how a user navigates an environment over time. By observing trails an agent is able to identify free space in an environment and how to navigate between points without needing to perform any collision checking. We found that trails in a virtual environments are a useful source of information for an agent's map building process. Trails can be used to improve rapidly exploring randomised tree and probabilistic roadmap generation, as well as being used as a source of information for segmenting maps in very large scale environments

    A robust motion planning approach for autonomous driving in urban areas

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 161-167).This thesis presents an improved sampling-based motion planning algorithm, Robust RRT, that is designed specifically for large robotic vehicles and uncertain, dynamic environments. Five main extensions have been made to the original RRT algorithm to improve performance in this type of applications. The closed-loop system is used for state propagation, enabling easy handling of complex, nonlinear, and unstable dynamics. The environment structure is exploited during the sampling process, increasing the probability that a given sample will be reachable. Efficient heuristics are employed in the expansion of the tree and a risk penalty is incorporated to capture uncertainty in the environment and keep the vehicle a safe distance away from hazards. The safety of the vehicle is guaranteed with the assumption of no unexpected changes in the environment, which is achieved by requiring that every trajectory sent for execution ends in a state with the vehicle stopped. Finally, risk evaluation follows a lazy evaluation strategy, allowing the algorithm to spend most of the computation time in the expansion step. The effectiveness of the Robust RRT algorithm for planning in an urban environment is demonstrated through numerous simulated scenarios and real data corresponding to its implementation in MIT's robotic vehicle that competed in the DARPA Urban Challenge.by Gaston A. Fiore.S.M
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