Model Predictive Control With Environment Adaptation for Legged Locomotion

Rathod, Niraj; Bratta, Angelo; Focchi, Michele; Zanon, Mario; Villarreal, Octavio; Semini, Claudio; Bemporad, Alberto

doi:10.1109/access.2021.3118957

Cited by 32 publications

(27 citation statements)

References 49 publications

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“…Many works have recently demonstrated dynamic locomotion realized using MPC with reduced models such as inverted pendulum model [8], [9] or the single-rigid body dynamics [28], [12], [29] with full kinematics [13], [30]. With former models based on inverted pendulum, we can improve the robot's stability compared with the zero moment point (ZMP) approach [31], as we could incorporate further constraints.…”

Section: B Related Workmentioning

confidence: 99%

Agile Maneuvers in Legged Robots: a Predictive Control Approach

Mastalli¹,

Merkt²,

Xin³

et al. 2022

Preprint

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Section: B Related Workmentioning

confidence: 99%

Agile Maneuvers in Legged Robots: a Predictive Control Approach

Mastalli¹,

Merkt²,

Xin³

et al. 2022

Preprint

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“…In particular, Model Predictive Control (MPC) approaches can compensate for uncertainties, disturbances and changes in the environment, by computing a new trajectory online, while the robot moves. In our previous work [1] we presented a Nonlinear Model Predictive Control (NMPC) formulation, which runs at 25 Hz and allows the Hydraulically actuated Quadruped (HyQ) [6] robot to perform omni-directional motions and detect a pallet and step on it with improved leg mobility. Minniti et al [7] integrated Control Lyapunov Functions into their MPC to guarantee stability when the robot Fig.…”

Section: A Related Workmentioning

confidence: 99%

“…1: Overview of our locmotion framework. The Governor presented in this work is integrated with the NMPC presented in our previous work [1]. The controller introduced in [22] allows the robot to track the trajectories computed by the NMPC.…”

Section: A Related Workmentioning

confidence: 99%

“…In this work we introduce the Governor, an optimizationbased reference generator which endows the cost function of a NMPC [1] with physically informed trajectories to be tracked. We integrated the Governor with our previous works, Fig 1 . To summarize, the contributions of the paper are:…”

Section: B Proposed Approach and Contributionmentioning

confidence: 99%

“…in a trot). The obtained trajectories are used as references for the cost function of the Nonlinear MPC presented in our previous work [1]. We also present a formulation that can guarantee a certain response time to reach a goal, without the need to tune the weights of the cost terms.…”

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confidence: 99%

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Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Bratta¹,

Focchi²,

Rathod³

et al. 2022

Preprint

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Model Predictive Control (MPC) approaches are widely used in robotics, since they allow to compute updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of parameters of the cost function in order to obtain good performance. When for example, a legged robot has to react to disturbances from the environment (e.g., recover after a push) or track a certain goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work we propose a novel optimization-based Reference Generator, named Governor, which exploits a Linear Inverted Pendulum model to compute reference trajectories for the Center of Mass, while taking into account the possible under-actuation of a gait (e.g. in a trot). The obtained trajectories are used as references for the cost function of the Nonlinear MPC presented in our previous work [1]. We also present a formulation that can guarantee a certain response time to reach a goal, without the need to tune the weights of the cost terms. In addition, foothold locations are corrected to drive the robot towards the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.

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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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Model Predictive Control With Environment Adaptation for Legged Locomotion

Cited by 32 publications

References 49 publications

Agile Maneuvers in Legged Robots: a Predictive Control Approach

Agile Maneuvers in Legged Robots: a Predictive Control Approach

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

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

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