A Real-Time Framework for Kinodynamic Planning with Application to Quadrotor Obstacle Avoidance

Allen, Ross; Pavone, Marco

doi:10.2514/6.2016-1374

Cited by 62 publications

(53 citation statements)

References 30 publications

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“…Despite the fact that the efficiency of the kinodynamic planning techniques keeps improving [12,13], it is still prohibitively expensive for replanning. Allen et al [23] work towards a real-time kinodynamic planning framework by combining FMT* [10] with a support vector machine (SVM) for the classification of the reachable set. This framework [23] reduces the calling of the BVP solver to gain efficiency.…”

Section: B Kinodynamic Motion Planningmentioning

confidence: 99%

“…Allen et al [23] work towards a real-time kinodynamic planning framework by combining FMT* [10] with a support vector machine (SVM) for the classification of the reachable set. This framework [23] reduces the calling of the BVP solver to gain efficiency. However, the solution quality largely depends on the number of states pre-sampled.…”

Section: B Kinodynamic Motion Planningmentioning

confidence: 99%

“…The inadequacy motivates us to propose an efficient kinodynamic planning algorithm which can work in real-time. However, kinodynamic planning is typically time consuming [14,23]. The major computation of the traditional kinodynamic planning lies in three tasks, namely, covering the large state space, solving the BVP and collision checking.…”

Section: A Motivating Examplementioning

confidence: 99%

See 2 more Smart Citations

An Efficient B-Spline-Based Kinodynamic Replanning Framework for Quadrotors

et al. 2019

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Trajectory replanning for quadrotors is essential to enable fully autonomous flight in unknown environments. Hierarchical motion planning frameworks, which combine path planning with path parameterization, are popular due to their time efficiency. However, the path planning cannot properly deal with non-static initial states of the quadrotor, which may result in non-smooth or even dynamically infeasible trajectories. In this paper, we present an efficient kinodynamic replanning framework by exploiting the advantageous properties of the B-spline, which facilitates dealing with the non-static state and guarantees safety and dynamical feasibility. Our framework starts with an efficient B-spline-based kinodynamic (EBK) search algorithm which finds a feasible trajectory with minimum control effort and time. To compensate for the discretization induced by the EBK search, an elastic optimization (EO) approach is proposed to refine the control point placement to the optimal location. Systematic comparisons against the state-of-the-art are conducted to validate the performance. Comprehensive onboard experiments using two different vision-based quadrotors are carried out showing the general applicability of the framework.

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Section: B Kinodynamic Motion Planningmentioning

confidence: 99%

Section: B Kinodynamic Motion Planningmentioning

confidence: 99%

Section: A Motivating Examplementioning

confidence: 99%

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An Efficient B-Spline-Based Kinodynamic Replanning Framework for Quadrotors

et al. 2019

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“…Steering function free-based approaches, such as EST [21] and SST [14], propagate random actions from a selected node. These methods can be applied to a variety of robot dynamics, although they tend to "wander" [1], thus they can take a long time to identify a solution.…”

Section: Related Workmentioning

confidence: 99%

RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators From RL Policies

Chiang

Hsu

Fišer

et al. 2019

IEEE Robot. Autom. Lett.

116

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This paper addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning. First, we use deep reinforcement learning to learn an obstacle-avoiding policy that maps a robot's sensor observations to actions, which is used as a local planner during planning and as a controller during execution. Second, we train a reachability estimator in a supervised manner, which predicts the RL policy's time to reach a state in the presence of obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local planner, and the reachability estimator as the distance function to bias tree-growth towards promising regions. We evaluate our method on three kinodynamic systems, including physical robot experiments. Results across all three robots tested indicate that RL-RRT outperforms state of the art kinodynamic planners in efficiency, and also provides a shorter path finish time than a steering function free method. The learned local planner policy and accompanying reachability estimator demonstrate transferability to the previously unseen experimental environments, making RL-RRT fast because the expensive computations are replaced with simple neural network inference. Video: https://youtu.be/dDMVMTOI8KY

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“…Quadrotor gains its recent popularity from recreational, commercial, and military purpose [31]. The commercial aspect varies from filming landscapes to movies, even delivering purchased items to buyers with the aid of GPS [32].…”

Section: Quadrotor Attitude Dynamicsmentioning

confidence: 99%

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

Muliadi

Kusumoputro

2018

Journal of Advanced Transportation

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This article proposes a comparative method to assess the performance of artificial neural network's direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

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A Real-Time Framework for Kinodynamic Planning with Application to Quadrotor Obstacle Avoidance

Cited by 62 publications

References 30 publications

An Efficient B-Spline-Based Kinodynamic Replanning Framework for Quadrotors

An Efficient B-Spline-Based Kinodynamic Replanning Framework for Quadrotors

RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators From RL Policies

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

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