A minimized falling damage method for humanoid robots

Li, Qingqing; Chen, Xuechao; Zhou, Yuhang; Yu, Zhangguo; Zhang, Weimin

doi:10.1177/1729881417728016

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…During the "pre-impact" phase, the robot adapts its posture by avoiding hand-designed "fall singularities" postures that increase the impact [17], by seeking to take a safe posture when falling on the back [18], or, by performing a rollover strategy [19]. During the "impact-time" phase, the PD-gains are automatically adapted by incorporating them in the QPcontroller to prevent the actuators from reaching their torque limits while still reaching the desired posture [20].…”

Section: B Fall and Damage Mitigationmentioning

confidence: 99%

First do not fall: learning to exploit a wall with a damaged humanoid robot

Anne¹,

Dalin²,

Bergonzani³

et al. 2022

Preprint

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Humanoid robots could replace humans in hazardous situations but most of such situations are equally dangerous for them, which means that they have a high chance of being damaged and fall. We hypothesize that humanoid robots would be mostly used in buildings, which makes them likely to be close to a wall. To avoid a fall, they can therefore lean on the closest wall, like a human would do, provided that they find in a few milliseconds where to put the hand(s). This article introduces a method, called D-Reflex, that learns a neural network that chooses this contact position given the wall orientation, the wall distance, and the posture of the robot. This contact position is then used by a whole-body controller to reach a stable posture. We show that D-Reflex allows a simulated TALOS robot (1.75m, 100kg, 30 degrees of freedom) to avoid more than 75% of the avoidable falls.

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Section: B Fall and Damage Mitigationmentioning

confidence: 99%

First do not fall: learning to exploit a wall with a damaged humanoid robot

Anne¹,

Dalin²,

Bergonzani³

et al. 2022

Preprint

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“…(2) collecting human kinematic, dynamic, and physiological data through motion capture systems, which are ultimately converted into the angles or moments of the corresponding joints of the robot, as shown in articles [8][9][10]. Thirdly, learning the optimal location of the multiple contact points through reinforcement learning algorithms or trajectory optimization, as introduced in articles [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support

Zuo,

Gao,

Liu

et al. 2024

Biomimetics

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When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms’ optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot’s movement. These constraints include whole-body dynamics, center of mass constraints, arm’s end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.

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“…Some robots are designed simply for walking or crawling [2,4,7], while others are designed for jumping alone [9,10]. The purpose of this paper is to add a jumping pattern to a versatile BHR6 robot, which is actuated using electric motors, and that already has walking, rolling, and fall protection capabilities [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Improvement of their locomotion abilities by addition of walking, crawling, climbing, fall protection, and jumping capabilities [1][2][3][4][5][6][7][8][9][10][11][12][13] is important to allow these robots to navigate across such adverse terrain. Unfortunately, most contemporary humanoid robots only have segmental functions.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning for Bipedal Robot to Perform Jump Maneuver

Jiang

Chen

et al. 2018

Applied Sciences

Self Cite

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Abstract:The remarkable ability of humans to perform jump maneuvers greatly contributes to the improvements of the obstacle negotiation ability of humans. The paper proposes a jumping control scheme for a bipedal robot to perform a high jump. The half-body of the robot is modeled as three planar links and the motion during the launching phase is taken into account. A geometrically simple motion was first conducted through which the gear reduction ratio that matches the maximum motor output for high jumping was selected. Then, the following strategies to further exploit the motor output performance was examined: (1) to set the maximum torque of each joint as the baseline that is explicitly modeled as a piecewise linear function dependent on the joint angular velocity; (2) to exert it with a correction of the joint angular accelerations in order to satisfy some balancing criteria during the motion. The criteria include the location of ZMP (zero moment point) and the torque limit. Using the technique described above, the jumping pattern is pre-calculated to maximize the jump height. Finally, the effectiveness of the proposed method is evaluated through simulations. In the simulation, the bipedal robot model achieved a 0.477-m high jump.

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A minimized falling damage method for humanoid robots

Cited by 11 publications

References 17 publications

First do not fall: learning to exploit a wall with a damaged humanoid robot

First do not fall: learning to exploit a wall with a damaged humanoid robot

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support

Motion Planning for Bipedal Robot to Perform Jump Maneuver

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