Fast Kinodynamic Bipedal Locomotion Planning with Moving Obstacles

Ahn, Junhyeok; Campbell, Orion; Kim, D.; Sentis, Luis

doi:10.1109/iros.2018.8594156

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…We characterize different dynamic models used for locomotion planning purposes. Dynamic locomotion has been studied using point mass models (PM) with predefined footholds and step timings (Koolen et al, 2012;Englsberger et al, 2015;Ahn et al, 2018) and PM models' variations to account for swing foot angular momentum (Faraji et al, 2019;Seyde et al, 2018;Ahn et al, 2020). These approaches are computationally efficient but require the contact schedule and the swing foot trajectories to be predefined by experts.…”

Section: Dynamic Locomotion Planning For Legged Robotsmentioning

confidence: 99%

Versatile Locomotion Planning and Control for Humanoid Robots

et al. 2021

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We propose a locomotion framework for bipedal robots consisting of a new motion planning method, dubbed trajectory optimization for walking robots plus (TOWR+), and a new whole-body control method, dubbed implicit hierarchical whole-body controller (IHWBC). For versatility, we consider the use of a composite rigid body (CRB) model to optimize the robot’s walking behavior. The proposed CRB model considers the floating base dynamics while accounting for the effects of the heavy distal mass of humanoids using a pre-trained centroidal inertia network. TOWR+ leverages the phase-based parameterization of its precursor, TOWR, and optimizes for base and end-effectors motions, feet contact wrenches, as well as contact timing and locations without the need to solve a complementary problem or integer program. The use of IHWBC enforces unilateral contact constraints (i.e., non-slip and non-penetration constraints) and a task hierarchy through the cost function, relaxing contact constraints and providing an implicit hierarchy between tasks. This controller provides additional flexibility and smooth task and contact transitions as applied to our 10 degree-of-freedom, line-feet biped robot DRACO. In addition, we introduce a new open-source and light-weight software architecture, dubbed planning and control (PnC), that implements and combines TOWR+ and IHWBC. PnC provides modularity, versatility, and scalability so that the provided modules can be interchanged with other motion planners and whole-body controllers and tested in an end-to-end manner. In the experimental section, we first analyze the performance of TOWR+ using various bipeds. We then demonstrate balancing behaviors on the DRACO hardware using the proposed IHWBC method. Finally, we integrate TOWR+ and IHWBC and demonstrate step-and-stop behaviors on the DRACO hardware.

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Section: Dynamic Locomotion Planning For Legged Robotsmentioning

confidence: 99%

Versatile Locomotion Planning and Control for Humanoid Robots

et al. 2021

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“…( 5) is a simple proportional-derivative controller and T x , T y , κ x and κ y are the gain parameters to keep the CoM converging to the desired position. A more detailed derivation of the LIPM is described in [15].…”

Section: Tvr Plannermentioning

confidence: 99%

Data-Efficient and Safe Learning for Humanoid Locomotion Aided by a Dynamic Balancing Model

Ahn

Lee

Sentis

2020

IEEE Robot. Autom. Lett.

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In this letter, we formulate a novel Markov Decision Process (MDP) for data efficient and safe learning for locomotion via a simplified model. In our previous studies on biped locomotion, we relied on a low-dimensional robot model, e.g., the Linear Inverted Pendulum Model (LIPM), commonly used in Walking Pattern Generators (WPG). However, employing low-level control cannot precisely track desired footstep locations due to the discrepancies between the real system and the simplified model. In this work, we propose an approach for mitigating this problem by complementing model-based policies with machine learning. We formulate an MDP process incorporating dynamic properties of robots, desired walking directions, and footstep features. We iteratively update the policy to determine footstep locations based on the previous MDP process aided by a deep reinforcement learning process. The policy of the proposed approach consists of a WPG and a parameterized stochastic policy. In addition, a Control Barrier Function (CBF) process applies corrections the above policy to prevent exploration of unsafe regions during learning. Our contributions include: 1) reduction of footstep tracking errors resulting from employing LIPM; 2) efficient exploration of the data driven process, and; 3) scalability of the procedure to any humanoid robot.

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Control of a High Performance Bipedal Robot using Viscoelastic Liquid Cooled Actuators

Ahn

Kim

Bang

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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This paper describes the control, and evaluation of a new human-scaled biped robot with liquid cooled viscoelastic actuators (VLCA). Based on the lessons learned from previous work from our team on VLCA [1], we present a new system design embodying a Reaction Force Sensing Series Elastic Actuator (RFSEA) and a Force Sensing Series Elastic Actuator (FSEA). These designs are aimed at reducing the size and weight of the robot's actuation system while inheriting the advantages of our designs such as energy efficiency, torque density, impact resistance and position/force controllability. The system design takes into consideration human-inspired kinematics and rangeof-motion (ROM), while relying on foot placement to balance. In terms of actuator control, we perform a stability analysis on a Disturbance Observer (DOB) designed for force control. We then evaluate various position control algorithms both in the time and frequency domains for our VLCA actuators. Having the low level baseline established, we first perform a controller evaluation on the legs using Operational Space Control (OSC) [2]. Finally, we move on to evaluating the full bipedal robot by accomplishing unsupported dynamic walking by means of the algorithms to appear in [3].

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Fast Kinodynamic Bipedal Locomotion Planning with Moving Obstacles

Cited by 3 publications

References 15 publications

Versatile Locomotion Planning and Control for Humanoid Robots

Versatile Locomotion Planning and Control for Humanoid Robots

Data-Efficient and Safe Learning for Humanoid Locomotion Aided by a Dynamic Balancing Model

Control of a High Performance Bipedal Robot using Viscoelastic Liquid Cooled Actuators

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