Real-Time Path Planning in Unknown Environments for Bipedal Robots

Hildebrandt, Arne-Christoph; Klischat, Moritz; Wahrmann, Daniel; Wittmann, Robert; Sygulla, Felix; Seiwald, Philipp; Rixen, Daniel J.; Buschmann, Thomas

doi:10.1109/lra.2017.2712650

Cited by 35 publications

(16 citation statements)

References 14 publications

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“…Subsequently, a sequence of foothold positions following this path is planned. Both can be done by path-planning algorithms with obstacle avoidance as proposed in [30]. Note that one may choose an arbitrary other algorithm as this does not constrain the following stages.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Quintic Spline Collocation for Real-Time Biped Walking-Pattern Generation with variable Torso Height

Seiwald

Sygulla

Staufenberg

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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This paper presents our newest findings in planning a dynamically and kinematically feasible center of mass motion for bipedal walking robots. We use a simplified robot model to incorporate multi-body dynamics and kinematic limits, while still being able to meet hard real-time requirements. The vertical center of mass motion is obtained through interpolation of a quintic spline whose control points are projected onto the kinematically feasible region. Subsequently, the horizontal motion is computed from multi-body dynamics which we approximate by solving an overdetermined boundary value problem via spline collocation based on quintic polynomials. The proposed algorithm is an improvement of our previous method, which used a parametric torso height optimization for vertical and cubic spline collocation for horizontal components. The novel center of mass motion improves stability, especially for stepping up and down platforms. Moreover, the new method leads to a less complex overall algorithm since it removes the necessity of manually tuned parameters and strongly simplifies the incorporation of boundary values. Lastly, the new approach is more efficient, which leads to a significantly reduced total runtime. The proposed method is validated through successfully conducted simulations and experiments on our humanoid robot platform, LOLA.

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Section: Proposed Algorithmmentioning

confidence: 99%

Quintic Spline Collocation for Real-Time Biped Walking-Pattern Generation with variable Torso Height

Seiwald

Sygulla

Staufenberg

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

Self Cite

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“…Gait Stability Navigation Automatic learning [42], [43], [50] [65], [78] [44], [51], [52], [53], [54], [55], [62], [64], [66], [69], [70], [71], [68], [75] [47], [49], [59], [60], [61], [77] [45], [56], [57], [58], [63], [67] [72], [73], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88]…”

Section: Papermentioning

confidence: 99%

Towards the Development of a Navigation System for a Bipedal Robot: Preliminary Analysis of the Literature

Ortega-Palacios¹,

Guerrero-García²,

González-Calleros³

2019

RCS

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In the literature, several works are reported about the gait of bipedal robots. Some papers are associated with stability or automatic learning and navigation, but no paper was found that includes the variables of bipedal gait, stability, navigation and automatic learning, all applied in an indoor bipedal robot. Our objective is, after the analysis of the state of the art, to propose a methodology that will allow the autonomous navigation of a bipedal robot and validate its degree of autonomy with respect to the evaluation metrics reported in the literature.

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“…Similarly to recent work on grasp [17], contact [18], and footstep planning for humanoid robots [19], we plan only a few steps (hand re-positions) ahead, and determine possible movements based on a set of simple physical criteria which ensure the manipulated object is always supported. This allows trajectory planning for hand movements to be very fast, as the computed trajectories are typically very short and the overall search space for valid hand positions is kept to a manageable size.…”

Section: Fig 1: Robot Setup With Human Interaction Partnermentioning

confidence: 99%