Collision-Free MPC for Legged Robots in Static and Dynamic Scenes

Gaertner, Magnus; Bjelonic, Marko; Farshidian, Farbod; Hutter, Marco

doi:10.1109/icra48506.2021.9561326

Cited by 30 publications

(14 citation statements)

References 26 publications

(36 reference statements)

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“…Unfortunately, this constraint is concave and not well suited for gradient-based optimization. Instead, we use a one-sided quadratic barrier approximation [52], 3) Nominal Kinematics: We want to relate a set of nominal footholds with a nominal base pose. By introducing a desired leg extension vector l des = 0 0 h des T with h des the desired height above ground, we can write…”

Section: B Objectives 1) Footholds On Groundmentioning

confidence: 99%

TAMOLS: Terrain-Aware Motion Optimization for Legged Systems

et al. 2022

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Terrain geometry is, in general, non-smooth, nonlinear, non-convex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This work presents the complete control pipeline capable of handling the aforementioned problems in real-time. We formulate a trajectory optimization problem that jointly optimizes over the base pose and footholds, subject to a heightmap. To avoid converging into undesirable local optima, we deploy a graduated optimization technique. We embed a compact, contact-force free stability criterion that is compatible with the non-flat ground formulation. Direct collocation is used as transcription method, resulting in a non-linear optimization problem that can be solved online in less than ten milliseconds. To increase robustness in the presence of external disturbances, we close the tracking loop with a momentum observer. Our experiments demonstrate stair climbing, walking on stepping stones, and over gaps, utilizing various dynamic gaits.

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Section: B Objectives 1) Footholds On Groundmentioning

confidence: 99%

TAMOLS: Terrain-Aware Motion Optimization for Legged Systems

et al. 2022

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“…This has been a common strategy in past as well as in recent work. Particularly spheres are applied as collision primitives in countless papers due to their ease-ofuse [17], [18]. Ellipsoids and capsules are also commonly found [1], [19]- [21], as well as boxes [22].…”

Section: Related Workmentioning

confidence: 99%

Differentiable Collision Avoidance Using Collision Primitives

Zimmermann¹,

Busenhart²,

Huber³

et al. 2022

Preprint

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A central aspect of robotic motion planning is collision avoidance, where a multitude of different approaches are currently in use. Optimization-based motion planning is one method, that often heavily relies on distance computations between robots and obstacles. These computations can easily become a bottleneck, as they do not scale well with the complexity of the robots or the environment. To improve performance, many different methods suggested to use collision primitives, i.e. simple shapes that approximate the more complex rigid bodies, and that are simpler to compute distances to and from. However, each pair of primitives requires its own specialized code, and certain pairs are known to suffer from numerical issues. In this paper, we propose an easy-to-use, unified treatment of a wide variety of primitives. We formulate distance computation as a minimization problem, which we solve iteratively. We show how to take derivatives of this minimization problem, allowing it to be seamlessly integrated into a trajectory optimization method. Our experiments show that our method performs favourably, both in terms of timing and the quality of the trajectory. The source code of our implementation will be released upon acceptance.

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“…The existing MPC-based schemes can be mainly categorized into gradient-based and sampling-based trajectory optimization methods. The gradientbased MPC methods have been successfully applied to real robotic systems, obtaining smooth collision-free trajectories in the presence of obstacles and other constraints [3]- [6]. However, the gradient-based frameworks are typically based on strong assumptions: the cost function, and sometimes system constraints, need to be differentiable in order to leverage the gradient for computing the optimal solution.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy

Mohamed¹,

Yin²,

Liu³

2022

Preprint

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Sampling-based model predictive control (MPC) optimization methods, such as Model Predictive Path Integral (MPPI), have recently shown promising results in various robotic tasks. However, it might produce an infeasible trajectory when the distributions of all sampled trajectories are concentrated within high-cost even infeasible regions. In this study, we propose a new method called log-MPPI equipped with a more effective trajectory sampling distribution policy which significantly improves the trajectory feasibility in terms of satisfying system constraints. The key point is to draw the trajectory samples from the normal log-normal (NLN) mixture distribution, rather than from Gaussian distribution. Furthermore, this work presents a method for collision-free navigation in unknown cluttered environments by incorporating the 2D occupancy grid map into the optimization problem of the sampling-based MPC algorithm. We first validate the efficiency and robustness of our proposed control strategy through extensive simulations of 2D autonomous navigation in different types of cluttered environments as well as the cart-pole swing-up task. We further demonstrate, through realworld experiments, the applicability of log-MPPI for performing a 2D grid-based collision-free navigation in an unknown cluttered environment, showing its superiority to be utilized with the local costmap without adding additional complexity to the optimization problem. A video demonstrating the real-world and simulation results is available at https://youtu.be/_uGWQEFJSN0.

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Collision-Free MPC for Legged Robots in Static and Dynamic Scenes

Cited by 30 publications

References 26 publications

TAMOLS: Terrain-Aware Motion Optimization for Legged Systems

TAMOLS: Terrain-Aware Motion Optimization for Legged Systems

Differentiable Collision Avoidance Using Collision Primitives

Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy

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