Lazy collision checking in asymptotically-optimal motion planning

Hauser, Kris

doi:10.1109/icra.2015.7139603

Cited by 142 publications

(107 citation statements)

References 13 publications

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“…Also, there is no need to check the entire tree because most of the nodes will not contribute to the optimal path. In this paper, we use the lazy-validitychecking strategy, similar to Hauser (2015). The main idea is that we only check the validity of the most promising nodes until a feasible optimal path (flight corridor) is found.…”

Section: Incremental Flight Corridor Refinementmentioning

confidence: 99%

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

Gao

et al. 2018

Journal of Field Robotics

136

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Micro aerial vehicles (MAVs), especially quadrotors, have been widely used in field applications, such as disaster response, field surveillance, and search‐and‐rescue. For accomplishing such missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement. In this paper, we present a framework for online generating safe and dynamically feasible trajectories directly on the point cloud, which is the lowest‐level representation of range measurements and is applicable to different sensor types. We develop a quadrotor platform equipped with a three‐dimensional (3D) light detection and ranging (LiDAR) and an inertial measurement unit (IMU) for simultaneously estimating states of the vehicle and building point cloud maps of the environment. Based on the incrementally registered point clouds, we online generate and refine a flight corridor, which represents the free space that the trajectory of the quadrotor should lie in. We represent the trajectory as piecewise Bézier curves by using the Bernstein polynomial basis and formulate the trajectory generation problem as a convex program. By using Bézier curves, we can constrain the position and kinodynamics of the trajectory entirely within the flight corridor and given physical limits. The proposed approach is implemented to run onboard in real‐time and is integrated into an autonomous quadrotor platform. We demonstrate fully autonomous quadrotor flights in unknown, complex environments to validate the proposed method.

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Section: Incremental Flight Corridor Refinementmentioning

confidence: 99%

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

Gao

et al. 2018

Journal of Field Robotics

136

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“…Effective if invalid edges are near the start. 1 The framework can be extended to handle non-uniform evaluation cost as well…”

Section: B the Lazy Shortest Path (Lazysp) Frameworkmentioning

confidence: 99%

Leveraging Experience in Lazy Search

Bhardwaj

Choudhury²,

Boots

et al. 2019

Robotics: Science and Systems XV

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Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the shortest potentially feasible path, evaluating edges along that path, and repeating until a feasible path is found. The order in which edges are selected is critical to minimizing the total number of edge evaluations: a good edge selector chooses edges that are not only likely to be invalid, but also eliminates future paths from consideration. We wish to learn such a selector by leveraging prior experience. We formulate this problem as a Markov Decision Process (MDP) on the state of the search problem. While solving this large MDP is generally intractable, we show that we can compute oracular selectors that can solve the MDP during training. With access to such oracles, we use imitation learning to find effective policies. If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly. We evaluate our algorithms on a wide range of 2D and 7D problems and show that the learned selector outperforms baseline commonly used heuristics.

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“…Sampling‐based planners (LaValle, ) have enjoyed a lot of attention due to their simplicity and asymptotic guarantees (Karaman & Frazzoli, ). They are also amenable to a lot of modifications to improve real‐time performance such as biased sampling (Hsu, Sánchez‐Ante, & Sun, ; Urmson & Simmons, ), lazy evaluation (Bohlin & Kavraki, ; Gammell, Srinivasa, & Barfoot, ; Hauser, ), or hybrid local global optimization (Choudhury, Gammell, Barfoot, Srinivasa, & Scherer, ; Luna, Şucan, Moll, & Kavraki, ; Raveh, Enosh, & Halperin, ). However, a large number of such sampling‐based methods require solving the boundary value problem online, that is, finding a steering input to drive the system from one configuration to another.…”

Section: Related Workmentioning

confidence: 99%

“…We also present analysis for each module in both simulation and real flights (Section 9). This paper builds upon our previous works on most of the individual components of our framework: using an ensemble of planners , 2015, dynamics projection filter , guaranteeing safety (Arora, Choudhury, Althoff, & Scherer, 2014, 2015, training a dynamic list of planners (Tallavajhula & Choudhury, 2015), and smooth route optimization (Dugar, Choudhury, & Scherer, 2017a, 2017b. In this paper, for the first time we present a description of the unified architecture, details of the trajectory planning framework and extensive flight test evaluation of the whole system.…”

Section: Contributionmentioning

confidence: 99%

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Choudhury

Dugar

Maeta

et al. 2019

Journal of Field Robotics

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Autonomous flight of unmanned full‐size rotor‐craft has the potential to enable many new applications. However, the dynamics of these aircraft, prevailing wind conditions, the need to operate over a variety of speeds and stringent safety requirements make it difficult to generate safe plans for these systems. Prior work has shown results for only parts of the problem. Here we present the first comprehensive approach to planning safe trajectories for autonomous helicopters from takeoff to landing. Our approach is based on two key insights. First, we compose an approximate solution by cascading various modules that can efficiently solve different relaxations of the planning problem. Our framework invokes a long‐term route optimizer, which feeds a receding‐horizon planner which in turn feeds a high‐fidelity safety executive. Secondly, to deal with the diverse planning scenarios that may arise, we hedge our bets with an ensemble of planners. We use a data‐driven approach that maps a planning context to a diverse list of planning algorithms that maximize the likelihood of success. Our approach was extensively evaluated in simulation and in real‐world flight tests on three different helicopter systems for duration of more than 109 autonomous hours and 590 pilot‐in‐the‐loop hours. We provide an in‐depth analysis and discuss the various tradeoffs of decoupling the problem, using approximations and leveraging statistical techniques. We summarize the insights with the hope that it generalizes to other platforms and applications.

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Lazy collision checking in asymptotically-optimal motion planning

Cited by 142 publications

References 13 publications

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments

Leveraging Experience in Lazy Search

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

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