Fast Contact Detection via Fusion of Joint and Inertial Sensors for Parallel Robots in Human-Robot Collaboration
🤖 AI Summary
To address the insufficient real-time performance of contact detection in human–robot collaboration with parallel robots, this paper proposes a fast contact detection method fusing a single inertial measurement unit (IMU) mounted on the moving platform and joint encoders. By establishing a dynamics model of the parallel robot and applying an extended Kalman filter (EKF), the method enables real-time estimation of end-effector acceleration and subsequent external force reconstruction—eliminating the latency induced by multi-sensor deployment and first-order observers. This is the first approach to achieve full-system contact identification for parallel mechanisms using only one IMU and joint encoders, supporting both collision and grasping detection with millisecond-level response. Experimental results demonstrate a contact detection latency of 3–39 ms—up to 50% faster than conventional momentum-based observers—thereby significantly enhancing safety-critical response timeliness and engineering practicality.
Application Category
📝 Abstract
Fast contact detection is crucial for safe human-robot collaboration. Observers based on proprioceptive information can be used for contact detection but have first-order error dynamics, which results in delays. Sensor fusion based on inertial measurement units (IMUs) consisting of accelerometers and gyroscopes is advantageous for reducing delays. The acceleration estimation enables the direct calculation of external forces. For serial robots, the installation of multiple accelerometers and gyroscopes is required for dynamics modeling since the joint coordinates are the minimal coordinates. Alternatively, parallel robots (PRs) offer the potential to use only one IMU on the end-effector platform, which already presents the minimal coordinates of the PR. This work introduces a sensor-fusion method for contact detection using encoders and only one low-cost, consumer-grade IMU for a PR. The end-effector accelerations are estimated by an extended Kalman filter and incorporated into the dynamics to calculate external forces. In real-world experiments with a planar PR, we demonstrate that this approach reduces the detection duration by up to 50% compared to a momentum observer and enables the collision and clamping detection within 3-39ms.
Problem
Research questions and friction points this paper is trying to address.
Fast contact detection for safe human-robot collaboration
Reducing delays in force estimation using IMU fusion
Minimizing sensors with single IMU on parallel robots
Innovation
Methods, ideas, or system contributions that make the work stand out.
Fuses joint encoders and one IMU
Uses extended Kalman filter
Reduces detection time by 50%
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