Frequency-Aware Model Predictive Control

Grandia, Ruben; Farshidian, Farbod; Dosovitskiy, Alexey; Ranftl, René; Hutter, Marco

doi:10.1109/lra.2019.2895882

Cited by 45 publications

(62 citation statements)

References 33 publications

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“…We found the robot transitioning from swing to stance more aggressively in sWBC than STANCE. Such smooth behavior was also noticed in [10]. Table IV shows the mean, standard deviation, and percentage error of the estimated terrain stiffness of all the four legs against the ground truth value (2400 N/m) obtained from the indentation tests.…”

Section: B Experimentssupporting

confidence: 64%

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STANCE: Locomotion Adaptation Over Soft Terrain

et al. 2020

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Whole-Body Control (WBC) has emerged as an important framework in locomotion control for legged robots. However, most WBC frameworks fail to generalize beyond rigid terrains. Legged locomotion over soft terrain is difficult due to the presence of unmodeled contact dynamics that WBCs do not account for. This introduces uncertainty in locomotion and affects the stability and performance of the system. In this paper, we propose a novel soft terrain adaptation algorithm called STANCE: Soft Terrain Adaptation and Compliance Estimation. STANCE consists of a WBC that exploits the knowledge of the terrain to generate an optimal solution that is contact consistent and an online terrain compliance estimator that provides the WBC with terrain knowledge. We validated STANCE both in simulation and experiment on the Hydraulically actuated Quadruped (HyQ) robot, and we compared it against the state of the art WBC. We demonstrated the capabilities of STANCE with multiple terrains of different compliances, aggressive maneuvers, different forward velocities, and external disturbances. STANCE allowed HyQ to adapt online to terrains with different compliances (rigid and soft) without pre-tuning. HyQ was able to successfully deal with the transition between different terrains and showed the ability to differentiate between compliances under each foot.

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Section: B Experimentssupporting

confidence: 64%

“…. We also ensure the consistency of the physical contact model throughout the optimization problem by ensuring that the penetration is always positive in (10).…”

Section: B Whole-body Optimization Revisitedmentioning

confidence: 99%

STANCE: Locomotion Adaptation Over Soft Terrain

et al. 2020

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“…To this end, to apply the SLQ feedback policy on hardware, we need to encode these bandwidth limitations in our optimization problem. In this work, we use the frequency-aware MPC approach introduced in [11]. This MPC formulation penalizes control actions in the frequency domain and automatically finds a trade-off between the bandwidth limitation of actuators and the stiffness of the high-level feedback policy.…”

Section: A Related Workmentioning

confidence: 99%

“…I-A, it has been proven difficult to use feedback gains from an LQR design on a torquecontrolled robot. We propose to use the frequency-dependent cost function introduced in our previous work [11], which was used to render the feedforward solution robust to high frequency disturbances. In this work, we show that it has a similar effect on the feedback structure.…”

Section: Frequency Shapingmentioning

confidence: 99%

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Feedback MPC for Torque-Controlled Legged Robots

Grandia

Farshidian

Ranftl

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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123

103

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The computational power of mobile robots is currently insufficient to achieve torque level whole-body Model Predictive Control (MPC) at the update rates required for complex dynamic systems such as legged robots. This problem is commonly circumvented by using a fast tracking controller to compensate for model errors between updates. In this work, we show that the feedback policy from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable alternative to bridge the gap between the low MPC update rate and the actuation command rate. We propose to augment the DDP approach with a relaxed barrier function to address inequality constraints arising from the friction cone. A frequency-dependent cost function is used to reduce the sensitivity to high-frequency model errors and actuator bandwidth limits. We demonstrate that our approach can find stable locomotion policies for the torque-controlled quadruped, ANYmal, both in simulation and on hardware.

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VERO: A vacuum‐cleaner‐equipped quadruped robot for efficient litter removal

Amatucci,

Turrisi,

Bratta

et al. 2024

Journal of Field Robotics

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Litter nowadays presents a significant threat to the equilibrium of many ecosystems. An example is the sea, where litter coming from coasts and cities via gutters, streets, and waterways, releases toxic chemicals and microplastics during its decomposition. Litter removal is often carried out manually by humans, which inherently lowers the amount of waste that can be effectively collected from the environment. In this paper, we present a novel quadruped robot prototype that, thanks to its natural mobility, is able to collect cigarette butts (CBs) autonomously, the second most common undisposed waste worldwide, in terrains that are hard to reach for wheeled and tracked robots. The core of our approach is a convolutional neural network for litter detection, followed by a time‐optimal planner for reducing the time needed to collect all the target objects. Precise litter removal is then performed by a visual‐servoing procedure which drives the nozzle of a vacuum cleaner that is attached to one of the robot legs on top of the detected CB. As a result of this particular position of the nozzle, we are able to perform the collection task without even stopping the robot's motion, thus greatly increasing the time‐efficiency of the entire procedure. Extensive tests were conducted in six different outdoor scenarios to show the performance of our prototype and method. To the best knowledge of the authors, this is the first time that such a design and method was presented and successfully tested on a legged robot.

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Frequency-Aware Model Predictive Control

Cited by 45 publications

References 33 publications

STANCE: Locomotion Adaptation Over Soft Terrain

STANCE: Locomotion Adaptation Over Soft Terrain

Feedback MPC for Torque-Controlled Legged Robots

VERO: A vacuum‐cleaner‐equipped quadruped robot for efficient litter removal

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