Motion planning for urban driving using RRT

Kuwata, Y.; Fiore, Gaston; Teo, Justin; Frazzoli, Emilio; How, Jonathan P.

doi:10.1109/iros.2008.4651075

Cited by 198 publications

(115 citation statements)

References 13 publications

(17 reference statements)

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“…Recently, we observe fast development of sampling-based motion planners building upon the concept of RRT (Rapidly Random exploring Trees) [11,15,17,20,23,30] and other closely related probabilistic approaches such as [13]. Their controller-driven variants are usually obtained by integration of a specific extend procedure into the planner.…”

Section: Related Workmentioning

confidence: 99%

“…Remark 2 Note that differential constraint (23) imposed by unicycle kinematics is represented in continuous time and robot configuration domains, as opposed to discretized state transition equations assumed in various methods available in the literature. Constraints (23) and (25) necessitate constraint satisfaction checking in continuous time and robot configuration domains, which is possible thanks to the application of the VFO feedback control law and utilization of its properties developed in Section 4.…”

Section: Formulation Of Problem 1 As a General Optimization Problemmentioning

confidence: 99%

“…Constraints (23) and (25) necessitate constraint satisfaction checking in continuous time and robot configuration domains, which is possible thanks to the application of the VFO feedback control law and utilization of its properties developed in Section 4. In our approach, we opted for MILP formulation to perform the optimization in (at least partially) a continuous domain and exploit linearities visible in our optimization problem.…”

Section: Formulation Of Problem 1 As a General Optimization Problemmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Formulation Of Problem 1 As a General Optimization Problemmentioning

confidence: 99%

Section: Formulation Of Problem 1 As a General Optimization Problemmentioning

confidence: 99%

See 1 more Smart Citation

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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“…This section reviews the real-time closed-loop RRT (CL-RRT) algorithm, proposed by Frazzoli 5 and later extended, [11][12][13] which this paper builds upon. The fundamental operation in the standard RRT algorithm 14 is the incremental growth of a tree of dynamically feasible trajectories, rooted at the system's current state, through simulations of the system's prediction model.…”

Section: A Closed-loop Rrt For Accurate State Predictionmentioning

confidence: 99%

Information-Rich Path Planning with General Constraints Using Rapidly-Exploring Random Trees

Levine

Luders

How

2010

AIAA Infotech@Aerospace 2010

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This paper introduces the Information-rich Rapidly-exploring Random Tree (IRRT), an extension of the RRT algorithm that embeds information collection as predicted using Fisher Information Matrices. The primary contribution of this algorithm is target-based information maximization in general (possibly heavily constrained) environments, with complex vehicle dynamic constraints and sensor limitations, including limited resolution and narrow field-of-view. An extension of IRRT for multi-agent missions is also presented. IRRT is distinguished from previous solutions strategies by its computational tractability and general constraint characterization. A progression of simulation results demonstrates that this implementation can generate complex target-tracking behaviors from a simple model of the trade-off between information gathering and goal arrival.

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“…Other techniques such as rapidly-exploring random trees (RRTs) [7] have been effectively used to generate search spaces around the mobile robot to navigate cluttered, difficult environments and generate sophisticated maneuvers including uturns. [9] presents a reactive path following controller for a unicycle type mobile robot built with a Deformable Virtual Zone to navigate paths without the need for global path replanning.…”

Section: Related Workmentioning

confidence: 99%

Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths

Howard

Green

Kelly

2010

Springer Tracts in Advanced Robotics

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As mobile robots venture into more complex environments, more arbitrary feasible state-space trajectories and paths are required to move safely and efficiently. The capacity to effectively navigate such paths in the face of disturbances and changes in mobility can mean the difference between mission failure and success. This paper describes a technique for model predictive control of a mobile robot that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved solution. The contribution, the receding horizon model-predictive control algorithm, specifically addresses the problem of path following and obstacle avoidance through cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to determine parameterized control inputs that navigate general mobile robots safely through the environment. Experimental results are presented for a a six-wheeled skid-steered field robot in natural terrain.

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Motion planning for urban driving using RRT

Cited by 198 publications

References 13 publications

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

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

Information-Rich Path Planning with General Constraints Using Rapidly-Exploring Random Trees

Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths

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