Deterministic Kinodynamic Planning with hardware demonstrations

Gaillard,; Soulignac,; Dinont,; Mathieu, Philippe

doi:10.1109/iros.2011.6048376

Cited by 2 publications

(1 citation statement)

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“…Our hybrid approach DKP, first introduced in [9], proposes an intermediate solution between these two categories. DKP builds in a 2-D space an exploration tree of which the branches are spline trajectories guided by an optimization criterion.…”

Section: Introductionmentioning

confidence: 99%

Deterministic Kinodynamic Planning with hardware demonstrations

Soulignac

Dinont

Mathieu

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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DKP (Deterministic Kinodynamic Planning) is a bottom-up trajectory planner for robots with flatnessproperties. DKP builds an exploration tree of which the branches are spline trajectories. DKP employs an A * -like algorithm to select which branch of the tree to grow. The selected trajectories are then grown in a propagation process which respects the kinematic constraints, such as linear/angular speed limits or obstacle avoidance. In addition, DKP produces trajectories that are immediately executable by the robot. Various experiments are provided to show the ability of DKP to effectively handle complex environments with one or more robots. I. INTRODUCTIONComputing a trajectory that takes into account both kinematic and dynamic constraints is known as kinodynamic planning [5], and is proven to be PSPACE-hard [22].Decoupled approaches separate kinodynamic planning in two successive problems; first, compute a path taking into account a part of the problem constraints (classically obstacles) and, then, smooth this path with the remaining constraints to make the solution admissible by the robot. The efficiency of decoupled approaches, such as variants of Elastic Bands [23], is explained by the fact that they are generally customized for specific kinodynamic problems [15]. They also provide bounds on the computation time, allowing on-line planning, which explains their wide usage. However, decoupled approaches present difficulties to solve complicated problems, with many degrees of freedom. Moreover, they suffer from incompleteness issues: since the initial path is not guaranteed to be feasible by the robot, the path smoothing phase may fail to satisfy all kinodynamic constraints or fail to find a solution even if one exists.To solve these difficulties [18], we can distinguish two categories of hybrid approaches which incorporate a local motion planner (selection process) within a global path planner (propagation process) to ensure the respect of constraints. The first category contains heavily customized approaches: both local and global motion planner are then designed to generate complex local maneuvers [11] and/or improve the quality of the global path to be tracked. They successfully deal with very specific problems, such as [6] which integrates perception sensors in the local planner; or the multi resolution approaches like AD * [18].

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Section: Introductionmentioning

confidence: 99%

Deterministic Kinodynamic Planning with hardware demonstrations

Soulignac

Dinont

Mathieu

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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View full text Add to dashboard Cite

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Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior

et al. 2017

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Navigating a robot in a dynamic environment is a challenging task, especially when the behavior of other agents such as pedestrians, is only partially predictable. Also, the kinodynamic constraints on robot motion add an extra challenge. This paper proposes a novel navigational strategy for collision avoidance of a kinodynamically constrained robot from multiple moving passive agents with partially predictable behavior. Specifically, this paper presents a new approach to identify the set of control inputs to the robot, named control obstacle, which leads it towards a collision with a passive agent moving along an arbitrary path. The proposed method is developed by generalizing the concept of nonlinear velocity obstacle (NLVO), which is used to avoid collision with a passive agent, and takes into account the kinodynamic constraints on robot motion. Further, it formulates the navigational problem as an optimization problem, which allows the robot to make a safe decision in the presence of various sources of unmodelled uncertainties. Finally, the performance of the algorithm is evaluated for different parameters and is compared to existing velocity obstacle-based approaches. The simulated experiments show the excellent performance of the proposed approach in term of computation time and success rate.

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Deterministic Kinodynamic Planning with hardware demonstrations

Cited by 2 publications

References 11 publications

Deterministic Kinodynamic Planning with hardware demonstrations

Deterministic Kinodynamic Planning with hardware demonstrations

Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior

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