RRT-based nonholonomic motion planning using any-angle path biasing

Palmieri, Luigi; Koenig, Sven; Arras, Kai O.

doi:10.1109/icra.2016.7487439

Cited by 109 publications

(63 citation statements)

References 15 publications

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“…Remark 2. In contrast to [18], where the authors assume that g(x)g(x) T is positive definite, we do not make any assumptions on the system vector fields f and g. In fact, for m < n, this condition is not satisfied for (4). Furthermore, [18] does not consider any input constraints.…”

Section: B Main Resultsmentioning

confidence: 99%

Control-Lyapunov and Control-Barrier Functions based Quadratic Program for Spatio-temporal Specifications

Garg

Panagou

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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This paper presents a method for control synthesis under spatio-temporal constraints. First, we consider the problem of reaching a set S in a user-defined or prescribed time T . We define a new class of control Lyapunov functions, called prescribed-time control Lyapunov functions (PT CLF), and present sufficient conditions on the existence of a controller for this problem in terms of PT CLF. Then, we formulate a quadratic program (QP) to compute a control input that satisfies these sufficient conditions. Next, we consider control synthesis under spatio-temporal objectives given as: the closed-loop trajectories remain in a given set Ss at all times; and, remain in a specific set Si during the time interval [ti, ti+1) for i = 0, 1, · · · , N ; and, reach the set Si+1 on or before t = ti+1. We show that such spatio-temporal specifications can be translated into temporal logic formulas. We present sufficient conditions on the existence of a control input in terms of PT CLF and control barrier functions. Then, we present a QP to compute the control input efficiently, and show its feasibility under the assumptions of existence of a PT CLF. To the best of authors' knowledge, this is the first paper proposing a QP based method for the aforementioned problem of satisfying spatio-temporal specifications for nonlinear controlaffine dynamics with input constraints. We also discuss the limitations of the proposed methods and directions of future work to overcome these limitations. We present numerical examples to corroborate our proposed methods.

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Section: B Main Resultsmentioning

confidence: 99%

Control-Lyapunov and Control-Barrier Functions based Quadratic Program for Spatio-temporal Specifications

Garg

Panagou

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…Steer(x i , x j ): it generates a trajectory σ and the set of controls u needed to steer the system from x i to x j . The analytical steer function connects any pair of states and respects the topological property as described in [7,17].…”

Section: B Cliff-rrt*mentioning

confidence: 99%

Kinodynamic motion planning on Gaussian mixture fields

Palmieri

Kucner

Magnusson

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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We present a mobile robot motion planning approach under kinodynamic constraints that exploits learned perception priors in the form of continuous Gaussian mixture fields. Our Gaussian mixture fields are statistical multi-modal motion models of discrete objects or continuous media in the environment that encode e.g. the dynamics of air or pedestrian flows. We approach this task using a recently proposed circular linear flow field map based on semi-wrapped GMMs whose mixture components guide sampling and rewiring in an RRT* algorithm using a steer function for non-holonomic mobile robots. In our experiments with three alternative baselines, we show that this combination allows the planner to very efficiently generate high-quality solutions in terms of path smoothness, path length as well as natural yet minimum control effort motions through multi-modal representations of Gaussian mixture fields.

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“…random variable. Biasing techniques towards the goal region or promising areas of the configuration space may be used if available [16], [17]. The randomness of the samples set ensures good exploration of the configuration space, but comes at the expense of stochastic results which may strongly vary for each planning query in terms of planning efficiency and path quality.…”

Section: Introductionmentioning

confidence: 99%

Dispertio: Optimal Sampling For Safe Deterministic Motion Planning

Palmieri

Bruns

Meurer

et al. 2020

IEEE Robot. Autom. Lett.

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A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for samplingbased robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.

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RRT-based nonholonomic motion planning using any-angle path biasing

Cited by 109 publications

References 15 publications

Control-Lyapunov and Control-Barrier Functions based Quadratic Program for Spatio-temporal Specifications

Control-Lyapunov and Control-Barrier Functions based Quadratic Program for Spatio-temporal Specifications

Kinodynamic motion planning on Gaussian mixture fields

Dispertio: Optimal Sampling For Safe Deterministic Motion Planning

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