Momentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematics
The current state-of-the-art gradient-based optimisation frameworks are able to produce impressive dynamic manoeuvres such as linear and rotational jumps. However, these methods, which optimise over the full rigid-body dynamics of the robot, often require precise foothold locations apriori, while re...
| Автори: | , , , |
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| Формат: | Conference item |
| Мова: | English |
| Опубліковано: |
IEEE
2024
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| _version_ | 1826317708257394688 |
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| author | Papatheodorou, A Merkt, W Mitchell, AL Havoutis, I |
| author_facet | Papatheodorou, A Merkt, W Mitchell, AL Havoutis, I |
| author_sort | Papatheodorou, A |
| collection | OXFORD |
| description | The current state-of-the-art gradient-based optimisation frameworks are able to produce impressive dynamic manoeuvres such as linear and rotational jumps. However, these methods, which optimise over the full rigid-body dynamics of the robot, often require precise foothold locations apriori, while real-time performance is not guaranteed without elaborate regularisation and tuning of the cost function. In contrast, we investigate the advantages of a task-space optimisation framework, with special focus on acrobatic motions. Our proposed formulation exploits the system's high-order nonlinearities, such as the nonholonomy of the angular momentum, in order to produce feasible, high-acceleration manoeuvres. By leveraging the full-centroidal dynamics of the quadruped ANYmal C and directly optimising its footholds and contact forces, the framework is capable of producing efficient motion plans with low computational overhead. Finally, we deploy our proposed framework on the ANYmal C platform, and demonstrate its true capabilities through real-world experiments, with the successful execution of high-acceleration motions, such as linear and rotational jumps. Extensive analysis of these shows that the robot's dynamics can be exploited to surpass its hardware limitations of having a high mass and low-torque limits. |
| first_indexed | 2025-03-11T16:58:11Z |
| format | Conference item |
| id | oxford-uuid:74f7adc6-2221-4ce6-b45f-01905cc87e07 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-03-11T16:58:11Z |
| publishDate | 2024 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:74f7adc6-2221-4ce6-b45f-01905cc87e072025-03-04T13:04:22ZMomentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematicsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:74f7adc6-2221-4ce6-b45f-01905cc87e07EnglishSymplectic ElementsIEEE2024Papatheodorou, AMerkt, WMitchell, ALHavoutis, IThe current state-of-the-art gradient-based optimisation frameworks are able to produce impressive dynamic manoeuvres such as linear and rotational jumps. However, these methods, which optimise over the full rigid-body dynamics of the robot, often require precise foothold locations apriori, while real-time performance is not guaranteed without elaborate regularisation and tuning of the cost function. In contrast, we investigate the advantages of a task-space optimisation framework, with special focus on acrobatic motions. Our proposed formulation exploits the system's high-order nonlinearities, such as the nonholonomy of the angular momentum, in order to produce feasible, high-acceleration manoeuvres. By leveraging the full-centroidal dynamics of the quadruped ANYmal C and directly optimising its footholds and contact forces, the framework is capable of producing efficient motion plans with low computational overhead. Finally, we deploy our proposed framework on the ANYmal C platform, and demonstrate its true capabilities through real-world experiments, with the successful execution of high-acceleration motions, such as linear and rotational jumps. Extensive analysis of these shows that the robot's dynamics can be exploited to surpass its hardware limitations of having a high mass and low-torque limits. |
| spellingShingle | Papatheodorou, A Merkt, W Mitchell, AL Havoutis, I Momentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematics |
| title | Momentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematics |
| title_full | Momentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematics |
| title_fullStr | Momentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematics |
| title_full_unstemmed | Momentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematics |
| title_short | Momentum-aware trajectory optimisation using full-centroidal dynamics and implicit inverse kinematics |
| title_sort | momentum aware trajectory optimisation using full centroidal dynamics and implicit inverse kinematics |
| work_keys_str_mv | AT papatheodoroua momentumawaretrajectoryoptimisationusingfullcentroidaldynamicsandimplicitinversekinematics AT merktw momentumawaretrajectoryoptimisationusingfullcentroidaldynamicsandimplicitinversekinematics AT mitchellal momentumawaretrajectoryoptimisationusingfullcentroidaldynamicsandimplicitinversekinematics AT havoutisi momentumawaretrajectoryoptimisationusingfullcentroidaldynamicsandimplicitinversekinematics |