Humanoid Loco-Manipulation Planning Based on Graph Search and Reachability Maps

Murooka, Masaki; Kumagai, Iori; Morisawa, Masaaki; Kanehiro, Fumio; Kheddar, Abderrahmane

doi:10.1109/lra.2021.3060728

Cited by 25 publications

(17 citation statements)

References 27 publications

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“…The pattern generator (PG) generates a CoM trajectory to track the input reference ZMP trajectory. The reference ZMP trajectory is determined by the footstep sequence obtained from the operator command or planner [26] at the upper layer. In loco-manipulation, the reference trajectories of the force, moment, and position of manipulation external contacts (e.g., hands) are also inputted to the PG.…”

Section: Pattern Generator With External Forcesmentioning

confidence: 99%

Humanoid Loco-Manipulations Pattern Generation and Stabilization Control

Murooka

Chappellet²,

Tanguy³

et al. 2021

IEEE Robot. Autom. Lett.

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In order for a humanoid robot to perform locomanipulation such as moving an object while walking, it is necessary to account for sustained or alternating external forces other than ground-feet reaction, resulting from humanoid-object contact interactions. In this letter, we propose a bipedal control strategy for humanoid loco-manipulation that can cope with such external forces. First, the basic formulas of the bipedal dynamics, i.e., linear inverted pendulum mode and divergent component of motion, are derived, taking into account the effects of external manipulation forces. Then, we propose a pattern generator to plan center of mass trajectories consistent with the reference trajectory of the manipulation forces, and a stabilizer to compensate for the error between desired and actual manipulation forces. The effectiveness of our controller is assessed both in simulation and loco-manipulation experiments with real humanoid robots.

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Section: Pattern Generator With External Forcesmentioning

confidence: 99%

Humanoid Loco-Manipulations Pattern Generation and Stabilization Control

Murooka

Chappellet²,

Tanguy³

et al. 2021

IEEE Robot. Autom. Lett.

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“…Tazaki et al [50] proposed a graph-based MPC which draws possible modes in a graph and simulates bipedal walking at different speeds. Murooka et al [51] proposed locomanipulation planning for humanoid robots based on graph search.…”

Section: B Model Predictive Controlmentioning

confidence: 99%

Controlling Pivoting Gait using Graph Model Predictive Control

Zhang,

Koyama,

Wan

et al. 2021

Preprint

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Pivoting gait is efficient for manipulating a big and heavy object with relatively small manipulating force, in which a robot iteratively tilts the object, rotates it around the vertex, and then puts it down to the floor. However, pivoting gait can easily fail even with a small external disturbance due to its instability in nature. To cope with this problem, we propose a controller to robustly control the object motion during the pivoting gait by introducing two gait modes, i.e., one is the double-support mode, which can manipulate a relatively light object with faster speed, and the other is the quadruplesupport mode, which can manipulate a relatively heavy object with lower speed. To control the pivoting gait, a graph model predictive control is applied taking into account of these two gait modes. By adaptively switching the gait mode according to the applied external disturbance, a robot can stably perform the pivoting gait even if the external disturbance is applied to the object.

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“…Loco-manipulation on legged robot is particularly interesting to study because of the maneuverability and high degree-offreedoms legged robots have, which allows the possibilities of various control schemes and robot configurations to manipulate an object. For example, humanoid robot pushing an object with hand [6] and quadruped robot manipulate objects with arms and legs [7].…”

Section: Introductionmentioning

confidence: 99%

“…Humanoid loco-manipulation control in [6] considers the interaction forces with both the object and the ground (i.e. pushing force and ground reaction force), and assumes the contact is consistent and unchanged.…”

Section: Introductionmentioning

confidence: 99%

Multi-contact MPC for Dynamic Loco-manipulation on Humanoid Robots

Li¹,

Nguyen²

2022

Preprint

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This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via Multi-contact Model Predictive Control (MPC) framework. In this framework, we proposed a multi-contact dynamics model that can represent different contact modes in loco-manipulation (e.g., hand contact with object and foot contacts with ground). The proposed dynamics model simplifies the object dynamics as external force applied to the system (external force model) to ensure the simplicity and feasibility of the MPC problem. In numerical validations, our Multicontact MPC framework only needs contact timings of each task and desired states to give MPC the knowledge of changes in contact modes in the prediction horizons in loco-manipulation. The proposed framework can control the humanoid robot to complete multi-tasks dynamic loco-manipulation applications such as efficiently picking up and dropping off objects while turning and walking.

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Humanoid Loco-Manipulation Planning Based on Graph Search and Reachability Maps

Cited by 25 publications

References 27 publications

Humanoid Loco-Manipulations Pattern Generation and Stabilization Control

Humanoid Loco-Manipulations Pattern Generation and Stabilization Control

Controlling Pivoting Gait using Graph Model Predictive Control

Multi-contact MPC for Dynamic Loco-manipulation on Humanoid Robots

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