5,612 research outputs found
Improving Sampling-Based Motion Planning Using Library of Trajectories
PlánovánĂ pohybu je jednĂm z podstatnĂ˝ch problĂ©mĹŻ robotiky. Tato práce kombinuje pokroky v plánovánĂ pohybu a hodnocenĂ podobnosti objektĹŻ za účelem zrychlenĂ plánovánĂ ve statickĂ˝ch prostĹ™edĂch. Prvnà část tĂ©to práce pojednává o souÄŤasnĂ˝ch metodách pouĹľĂvanĂ˝ch pro hodnocenĂ podobnosti objektĹŻ a plánovánĂ pohybu. ProstĹ™ednà část popisuje, jak jsou tyto metody pouĹľity pro zrychlenĂ plánovánĂ s vyuĹľitĂm zĂskanĂ˝ch znalostĂ o prostĹ™edĂ. V poslednà části jsou navrĹľenĂ© metody porovnány s ostatnĂmi plánovaÄŤi v nezávislĂ©m testu. Námi navrĹľenĂ© algoritmy se v experimentech ukázaly bĂ˝t ÄŤasto rychlejšà v porovnánĂ s ostatnĂmi plánovaÄŤi. TakĂ© ÄŤasto nacházely cesty v prostĹ™edĂch, kde ostatnĂ plánovaÄŤe nebyly schopny cestu nalĂ©zt.Motion planning is one of the fundamental problems in robotics. This thesis combines the advances in motion planning and shape matching to improve planning speeds in static environments. The first part of this thesis covers current methods used in object similarity evaluation and motion planning. The middle part describes how these methods are used together to improve planning speeds by utilizing prior knowledge about the environment, along with additional modifications. In the last part, the proposed methods are tested against other state-of-the-art planners in an independent benchmarking facility. The proposed algorithms are shown to be faster than other planners in many cases, often finding paths in environments where the other planners are unable to
Environment Search Planning Subject to High Robot Localization Uncertainty
As robots find applications in more complex roles, ranging from search and rescue to healthcare and services, they must be robust to greater levels of localization uncertainty and uncertainty about their environments. Without consideration for such uncertainties, robots will not be able to compensate accordingly, potentially leading to mission failure or injury to bystanders. This work addresses the task of searching a 2D area while reducing localization uncertainty. Wherein, the environment provides low uncertainty pose updates from beacons with a short range, covering only part of the environment. Otherwise the robot localizes using dead reckoning, relying on wheel encoder and yaw rate information from a gyroscope. As such, outside of the regions with position updates, there will be unconstrained localization error growth over time. The work contributes a Belief Markov Decision Process formulation for solving the search problem and evaluates the performance using Partially Observable Monte Carlo Planning (POMCP). Additionally, the work contributes an approximate Markov Decision Process formulation and reduced complexity state representation. The approximate problem is evaluated using value iteration. To provide a baseline, the Google OR-Tools package is used to solve the travelling salesman problem (TSP). Results are verified by simulating a differential drive robot in the Gazebo simulation environment. POMCP results indicate planning can be tuned to prioritize constraining uncertainty at the cost of increasing path length. The MDP formulation provides consistently lower uncertainty with minimal increases in path length over the TSP solution. Both formulations show improved coverage outcomes
Efficient Configuration Space Construction and Optimization for Motion Planning
The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this paper, we survey some of our recent work on solving two important challenges related to configuration spaces
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Real-time reach planning for animated characters using hardware acceleration
We present a heuristic-based real-time reach planning algorithm for virtual human figures. Given the start and goal positions in a 3D workspace, our problem is to compute a collision-free path that specifies all the configurations for a human arm to move from the start to the goal. Our algorithm consists of three modules: spatial search, inverse kinematics, and collision detection. For the search module, instead of searching in joint configuration space like most existing motion planning methods do, we run a direct search in the workspace, guided by a heuristic distance-to-goal evaluation function. The inverse kinematics module attempts to select natural posture configurations for the arm along the path found in the workspace. During the search, candidate configurations will be checked for collisions taking advantage of the graphics hardware – depth buffer. The algorithm is fast and easy to implement. It allows real-time planning not only in static, structured environments, but also in dynamic, unstructured environments. No preprocessing and prior knowledge about the environment is required. Several examples are shown illustrating the competence of the planner at generating motion plans for a typical human arm model with seven degrees of freedom
Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning
When facing a new motion-planning problem, most motion planners solve it from
scratch, e.g., via sampling and exploration or starting optimization from a
straight-line path. However, most motion planners have to experience a variety
of planning problems throughout their lifetimes, which are yet to be leveraged
for future planning. In this paper, we present a simple but efficient method
called Motion Memory, which allows different motion planners to accelerate
future planning using past experiences. Treating existing motion planners as
either a closed or open box, we present a variety of ways that Motion Memory
can contribute to reduce the planning time when facing a new planning problem.
We provide extensive experiment results with three different motion planners on
three classes of planning problems with over 30,000 problem instances and show
that planning speed can be significantly reduced by up to 89% with the proposed
Motion Memory technique and with increasing past planning experiences
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