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Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.


Papers
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Proceedings Article
11 Jul 2009
TL;DR: This paper presents a novel approach to adaptive informative path planning that plans ahead for possible observations that can be made in the future, and develops an algorithm that performs provably near-optimally in settings where the adaptivity gap is small.
Abstract: Many robotic path planning applications, such as search and rescue, involve uncertain environments with complex dynamics that can be only partially observed. When selecting the best subset of observation locations subject to constrained resources (such as limited time or battery capacity) it is an important problem to trade off exploration (gathering information about the environment) and exploitation (using the current knowledge about the environment most effectively) for efficiently observing these environments. Even the nonadaptive setting, where paths are planned before observations are made, is NP-hard, and has been subject to much research. In this paper, we present a novel approach to adaptive informative path planning that addresses this exploration-exploitation tradeoff. Our approach is nonmyopic, i.e. it plans ahead for possible observations that can be made in the future. We quantify the benefit of exploration through the "adaptivity gap" between an adaptive and a nonadaptive algorithm in terms of the uncertainty in the environment. Exploiting the submodularity (a diminishing returns property) and locality properties of the objective function, we develop an algorithm that performs provably near-optimally in settings where the adaptivity gap is small. In case of large gap, we use an objective function that simultaneously optimizes paths for exploration and exploitation. We also provide an algorithm to extend any single robot algorithm for adaptive informative path planning to the multi robot setting while approximately preserving the theoretical guarantee of the single robot algorithm. We extensively evaluate our approach on a search and rescue domain and a scientific monitoring problem using a real robotic system.

127 citations

Book ChapterDOI
01 Jan 2016
TL;DR: This chapter describes motion planning and obstacle avoidance for mobile robots and will see how the two areas do not share the same modeling background.
Abstract: This chapter describes motion planning and obstacle avoidance for mobile robots. We will see how the two areas do not share the same modeling background. From the very beginning of motion planning, research has been dominated by computer sciences. Researchers aim at devising well-grounded algorithms with well-understood completeness and exactness properties.

127 citations

Proceedings ArticleDOI
05 Dec 2005
TL;DR: A new variant of the dynamic-domain RRT, which iteratively adapts the sampling domain for the Voronoi region of each node during the search process, which allows automatic tuning of the parameter and significantly increases the robustness of the algorithm.
Abstract: Sampling based planners have become increasingly efficient in solving the problems of classical motion planning and its applications. In particular, techniques based on the rapidly-exploring random trees (RRTs) have generated highly successful single-query planners. Recently, a variant of this planner called dynamic-domain RRT was introduced by Yershova et al. (2005). It relies on a new sampling scheme that improves the performance of the RRT approach on many motion planning problems. One of the drawbacks of this method is that it introduces a new parameter that requires careful tuning. In this paper we analyze the influence of this parameter and propose a new variant of the dynamic-domain RRT, which iteratively adapts the sampling domain for the Voronoi region of each node during the search process. This allows automatic tuning of the parameter and significantly increases the robustness of the algorithm. The resulting variant of the algorithm has been tested on several path planning problems.

127 citations

Journal ArticleDOI
TL;DR: This dissertation presents a framework and algorithms for solving real-time task and path planning problems by combining Evolutionary Computation (EC) based techniques with a Market-based planning architecture that takes advantage of the flexibility of EC-based techniques and the distributed structure of Market- based planning.
Abstract: In a highly dynamic environment, an adaptive real-time mission planner is essential for controlling a team of autonomous vehicles to execute a set of tasks. An optimal plan computed prior to the operation will no longer be optimal when the vehicles execute the plan. This dissertation presents a framework and algorithms for solving real-time task and path planning problems by combining Evolutionary Computation (EC) based techniques with a Market-based planning architecture. The planning system takes advantage of the flexibility of EC-based techniques and the distributed structure of Market-based planning. This property allows the vehicles to evolve their task plans and routes in response to the changing environment in real time, and under varying computational time windows.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer.

127 citations

Journal Article
TL;DR: This work revisits the benefits, in terms of travelling time, of path planning in marine environments showing spatial variability, and focuses on the application to a real environment of such techniques with autonomous underwater glider properties as the mobile platform.
Abstract: Autonomous Underwater Vehicles (AUVs) usually operate in ocean environments characterized by complex spatial variability which can jeopardize their missions. To avoid this, planning safety routes with minimum energy cost is of primary importance. This work revisits the benefits, in terms of travelling time, of path planning in marine environments showing spatial variability. By means of a path planner presented in a previous paper, this work focuses on the application to a real environment of such techniques. Extensive computations have been carried out to calculate optimal paths on realistic ocean environments, based on autonomous underwater glider properties as the mobile platform. Unlike previous works, the more realistic and applied case of an autonomous underwater glider surveying the Western Mediterranean Sea is considered. Results indicate that substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity and the vehicle speed are comparable. Conversely, the straight line path betwe en starting and ending points can be considered an optimum path when the current speed does not exceed half of the vehicle velocity. In both situations, benefits of path planning seem dependent also on the spatial structure of the current field.

127 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,512
20223,388
20212,138
20202,668
20192,648
20182,266