Perceptive Locomotion in Rough Terrain – Online Foothold Optimization

Jenelten, Fabian; Miki, Takahiro; Vijayan, Aravind E.; Bjelonic, Marko; Hutter, Marco

doi:10.1109/lra.2020.3007427

Cited by 83 publications

(62 citation statements)

References 16 publications

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“…First, we compute a feasible trajectory that steers the system from standing still at (p x , p y ) = (0, 0.85) to a goal state x g = (10, 0.85, 0, 0, 9.9, 10.2, 0, 0). In order to compute a feasible trajectory x 0 , we fixed a sequence of regions {D t } T t=0 where the system should be at each time t and we solved the resulting NLP 1 with IPOPT [22] using CASADI [23]. Furthermore, we added a slack variable to the terminal constraint, we used…”

Section: B Simulationsmentioning

confidence: 99%

“…Yet the presence of discrete variables make planning and control problems challenging, as it is required to reason about all possible combinations of discrete events. This challenge can be mitigated by designing hierarchical strategies, where a high-level planner computes the discrete variables and a low-level controller optimizes the system trajectory described by continuous variables [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

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Iterative Model Predictive Control for Piecewise Systems

Rosolia

Ames

2022

IEEE Control Syst. Lett.

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In this paper, we present an iterative Model Predictive Control (MPC) design for piecewise nonlinear systems. We consider finite time control tasks where the goal of the controller is to steer the system from a starting configuration to a goal state while minimizing a cost function. First, we present an algorithm that leverages a feasible trajectory that completes the task to construct a control policy which guarantees that state and input constraints are recursively satisfied and that the closed-loop system reaches the goal state in finite time. Utilizing this construction, we present a policy iteration scheme that iteratively generates safe trajectories which have nondecreasing performance. Finally, we test the proposed strategy on a discretized Spring Loaded Inverted Pendulum (SLIP) model with massless legs. We show that our methodology is robust to changes in initial conditions and disturbances acting on the system. Furthermore, we demonstrate the effectiveness of our policy iteration algorithm in a minimum time control task.

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Section: B Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative Model Predictive Control for Piecewise Systems

Rosolia

Ames

2022

IEEE Control Syst. Lett.

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“…In contrast, Model Predictive Control has become a central method for the online synthesis and control of dynamic systems over a given time horizon [15]. In the context of the stepping-stones problem, a distinction can be made between MPC based approaches where the footholds locations are determined separately from the torso motion optimization [16], [17], [18], and MPC based approaches where the foothold location and torso motions are jointly optimized. The benefit of jointly optimizing torso and leg motions has been demonstrated in the field of trajectory optimization [19], [20].…”

Section: A Related Workmentioning

confidence: 99%

Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control

Grandia

Taylor

Ames

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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The problem of dynamic locomotion over rough terrain requires both accurate foot placement together with an emphasis on dynamic stability. Existing approaches to this problem prioritize immediate safe foot placement over longer term dynamic stability considerations, or relegate the coordination of foot placement and dynamic stability to heuristic methods. We propose a multi-layered locomotion framework that unifies Control Barrier Functions (CBFs) with Model Predictive Control (MPC) to simultaneously achieve safe foot placement and dynamic stability. Our approach incorporates CBF based safety constraints both in a low frequency kino-dynamic MPC formulation and a high frequency inverse dynamics tracking controller. This ensures that safety-critical execution is considered when optimizing locomotion over a longer horizon. We validate the proposed method in a 3D stepping-stone scenario in simulation and experimentally on the ANYmal quadruped platform.

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“…To dealt with fast adaptation toward change of external input, some researcher build hirarchical sytsem with CNN model for ground surface condition recognition [20][21][22] . However, the recognition process requires high computational cost.…”

Section: Issue In Fast Adaptabilty Toward External Inputmentioning

confidence: 99%

Multi-Scopic Neuro-Cognitive Adaptation for Legged Locomotion Robots

Saputra

Wada

Masuda

et al. 2021

Preprint

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Dynamic locomotion is realized through a simultaneous integration of adaptability and optimality. This article proposes a neuro-cognitive model for multi-legged locomotion robot that can seamlessly integrate multi-modal sensing, ecological perception, and cognition through the coordination of interoceptive and exteroceptive sensory information. Importantly, cognitive models can be discussed as micro-, meso-, and macro-scopic; these concepts correspond to sensing, perception, and cognition; and short-, medium-, and long-term adaptation (in terms of ecological psychology). The proposed neuro-cognitive model integrates these intelligent functions from a multi-scopic point of view. Macroscopic-level presents an attention mechanism with short-term adaptive locomotion control conducted by lower-level sensorimotor coordination-based model. Macrosopic-level serves environmental cognitive map featuring higher-level behavior planning. Mesoscopic level shows integration between the microscopic and macroscopic approaches, enabling the model to reconstruct a map and conduct localization using bottom-up facial environmental information and top-down map information, generating intention towards the ultimate goal at the macroscopic level. The experiments demonstrated that adaptability and optimality of multi-legged locomotion could be achieved using the proposed multi-scale neuro-cognitive model, from short to long-term adaptation, with efficient computational usage. Future research directions can be implemented not only in robotics contexts but also in the context of interdisciplinary studies incorporating cognitive science and ecological psychology.

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Perceptive Locomotion in Rough Terrain – Online Foothold Optimization

Cited by 83 publications

References 16 publications

Iterative Model Predictive Control for Piecewise Systems

Iterative Model Predictive Control for Piecewise Systems

Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control

Multi-Scopic Neuro-Cognitive Adaptation for Legged Locomotion Robots

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