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.
View on arXiv@article{zhou2025_2504.17201,
title={ Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion },
author={ Ziyi Zhou and Stefano Di Cairano and Yebin Wang and Karl Berntorp },
journal={arXiv preprint arXiv:2504.17201},
year={ 2025 }
}