LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics

Pérez, Alejandro; Platt, Robert; Konidaris, George; Kaelbling, Leslie Pack; Lozano-Pérez, Tomás

doi:10.1109/icra.2012.6225177

Cited by 185 publications

(132 citation statements)

References 9 publications

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“…11,20 To overcome the low exploring efficiency, we propose the dynamic concerned exploration, which is inspired by kinodynamic planning 5 and control effort evaluation. 19 The dynamic concerned exploration facilitates all choosing neighbors to be connectable without complex cost comparison. The redistribution method BPF is also proposed to extend with maximum amount of samplings.…”

Section: Algorithmsmentioning

confidence: 99%

“…Lee et al 18 introduce differential constraints to optimize the nearest neighbor and distance metric, thus enabling high-speed maneuvering by considering high-dimensional dynamical system. In the study by Jaillet et al, infinite-horizon linear-quadratic regulator (LQR) was introduced to calculate the local extension between two states, 19 where in each step a forward simulation to extent toward the newly sampled state is executed to test dynamics feasibility. Fixed final state and fixed final time optimal control problems are solved and embedded in RRT* by Webb and van den Berg.…”

Section: Related Workmentioning

confidence: 99%

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Gart

Yang

Xiao

et al. 2016

International Journal of Advanced Robotic Systems

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The problem of three-dimensional path planning in obstacle-crowded environments is a challenge (an NP-hard problem), which becomes even more complex when considering environmental uncertainty and system control. Int this paper, we mainly focused on more challenging problem, that is, path planning in obstacle-crowded environments, and we try to find the relation between contact information and obstacle modeling. We proposed a newactive exploring sampling-based algorithm based on rapidly exploring random tree (RRT), namely, guiding attraction-based random tree (GART). GART introduces bidirectional potential field to redistribute each newly sampled state, such that the in-collision samples can be redistributed for extension. Furthermore, dynamic constraints are deployed to establish forward extending region by GART. Thus, GART can ensure kinodynamic reachability as well as smoothness. Theoretical analysis demonstrate that GART is probabilistic complete, and it obtains faster convergence rate because of its redistribution ability. In addition to theoretical analysis, this article provides comparative simulations as well as experiments under typical situations. Results demonstrate that GART has a much better time-efficiency performance than RRT*, retraction-based RRT, and other referred algorithms when applying redistribution and dynamic constraints on random exploration. KeywordsSampling-based planning algorithm, bidirectional potential field, control and sensing uncertainty, percentage of useful samples Date

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Section: Algorithmsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Gart

Yang

Xiao

et al. 2016

International Journal of Advanced Robotic Systems

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“…Their controller-driven variants are usually obtained by integration of a specific extend procedure into the planner. Utilized extend procedures range from general optimal-control approches such as [40], through spline path segments (e.g., [18,54]), to more controller-driven approaches such as simulation of mobile robot with closed-loop control system proposed in [38] and control-Lyapunov function approach from [39]. Sampling-based algorithms are attractive due to their generality and ability to cope with virtually any robot and motion environment model.…”

Section: Related Workmentioning

confidence: 99%

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Gawron

Michałek

2017

J Intell Robot Syst

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Integrated motion planning and control for the purposes of maneuvering mobile robots under state-and input constraints is a problem of vital practical importance in applications of mobile robots such as autonomous transportation. Those constraints arise naturally in practice due to specifics of robot mechanical construction and the presence of obstacles in motion environment. In contrast to approaches focusing on feedback control design under the assumption of given reference motion or motion planning with neglection of subsequent feedback motion execution, we adopt a controller-driven motion planning paradigm, which has recently gained attention of many researchers. It postulates design of motion planning algorithms dedicated to specific feedback control policies, which compute a sequence of feedback control subtasks instead of classically planned open-loop controls or parametric paths. In this spirit, we propose a motion planning algorithm driven by the VFO (Vector Field Orientation) control law for the waypointfollowing task. Presented analysis of the VFO control law reveals its beneficial properties, which are subsequently utilized to solve a generally nonlinear and non-convex optimal motion planning problem by formulating it as a mixed-integer linear program (MILP). The solution proposed in this paper yields a waypoint sequence, which is designed for execution by application of the VFO control law to drive a robot to a prescribed final configuration under an input constraint imposed by bounded curvature of robot motion and state constraints resulting from a convex decomposition of task space. Satisfaction of these constraints is guaranteed analytically and exactly, i.e., without utilization of numerical approximations. Moreover, for a given discrete set of possible waypoint orientations, the proposed algorithm computes plans optimal w.r.t. given cost functional, which can be any convex linear combination of quantities such as robot path length, curvature of robot motion, distance to imposed state constraints, etc. Furthermore, the planning algorithm exploits the possibility of both forward or backward movement of the robot to allow maneuvering in demanding environments. Generated waypoint sequences are a compact representation of a motion plan, which can be immediately executed with the VFO controller without any additional postprocessing. Validity of the proposed approach has 266 J Intell Robot Syst (2018) 89:265-297 been confirmed by simulation studies and experimental motion execution with a laboratory-scale mobile robot.

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“…Non-holonomic constraints of car-like robot [61] As RRT* connects pair of states using straight lines, which is not feasible for kinodynamic systems due to the differential constraints. Prior kinodynamic extensions of RRT* such as Kinodynamic-RRT* [62] and LQR-RRT* [63] only satisfy bounded sub optimality and require RRT* to re-propagate the tree partially during each iteration. Thus, making these approaches computationally expensive.…”

Section: Non-holonomic and Kinodynamic Rrt* Approachesmentioning

confidence: 99%

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Noreen¹,

Khan²,

Habib³

2016

ijacsa

201

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Abstract-Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, agricultural robots, planetary and space exploration missions. Rapidly-exploring Random Tree Star (RRT*) is a renowned sampling based planning approach. It has gained immense popularity due to its support for high dimensional complex problems. A significant body of research has addressed the problem of optimal path planning for mobile robots using RRT* based approaches. However, no updated survey on RRT* based approaches is available. Considering the rapid pace of development in this field, this paper presents a comprehensive review of RRT* based path planning approaches. Current issues relevant to noticeable advancements in the field are investigated and whole discussion is concluded with challenges and future research directions.

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LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics

Cited by 185 publications

References 9 publications

Gart

Gart

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

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