四足机器人用传感器与动力学模型同步检测碰撞并估算受力。
Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion
- 用多模态卡尔曼滤波融合编码器数据和动力学信息。
- 可识别接触状态与外部受力，支持任意步态。
- 适合需要快速避障和自适应平衡的四足机器人应用。
本文针对四足运动中的碰撞检测与外力估计问题，仅使用关节编码器信息和机器人动力学模型，设计了一种交互式多模型卡尔曼滤波器（IMM-KF），用于同时估计作用在机器人上的外部力和多种可能的接触模式。该方法对任何步态设计均保持不变性。通过基于机器人动力学和编码器信息的伪测量，实现外力估计。根据估计的接触模式和外力，设计了反射式运动控制与顺应性控制器，调整摆动腿参考轨迹以避免碰撞；同时采用力自适应模型预测控制器提升平衡性能。仿真消融实验与实际测试验证了该方法的有效性。
原文摘要 · Abstract (English)
In this paper we address the simultaneous collision detection and force estimation problem for quadrupedal locomotion using joint encoder information and the robot dynamics only. We design an interacting multiple-model Kalman filter (IMM-KF) that estimates the external force exerted on the robot and multiple possible contact modes. The method is invariant to any gait pattern design. Our approach leverages pseudo-measurement information of the external forces based on the robot dynamics and encoder information. Based on the estimated contact mode and external force, we design a reflex motion and an admittance controller for the swing leg to avoid collisions by adjusting the leg's reference motion. Additionally, we implement a force-adaptive model predictive controller to enhance balancing. Simulation ablatation studies and experiments show the efficacy of the approach.
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