Fall Detection and Damage Reduction in Biped Humanoid Robots

Abstract

The appropriate management of fall situations, i.e., fast instability detection, avoidance of unintentional falls, falling without damaging the body, fast recovery of the standing position after a fall-is an essential ability for biped humanoid robots. This issue is especially important for biped humanoid robots carrying out demanding movements such as walking on irregular surfaces, running, or playing a given sport. In this paper, we tackle the detection of instability, and the management of falls in biped humanoids using an integrated framework. In this framework, after instability is detected, a fall can be avoided, or at the very least, low-damage falling sequences can be triggered, depending on the degree of the detected instability. The proposed fall detection and fall avoidance methodologies have been validated in real-world experiments with biped humanoid robots (525 collision experiments were carried out). The obtained results show the robustness of the fall detection methodology. In addition, they show under which conditions falls can be avoided, and the reduction of the fall damage when a fall occurs. Besides, results also suggest that the proposed fall avoidance methodology still needs improvements. Moreover, the methodology is more suitable for small-sized and light-weight robots which are more mechanically robust to falling and can bear the required tests.

Semi-Automatic Extraction of Training Examples From Sensor Readings for Fall Detection and Posture Monitoring

“…A fall detection, avoidance, and damage reduction mechanism for biped humanoid robots was proposed in [28]. The authors simulated the real world environment for humanoid robots.…”

“…Only three studies have been reported for biped robots [4], [17], [28]. The focus for these studies were prediction of a fall before it occurs and taking corrective measures to prevent the fall.…”

“…The availability of inexpensive miniature microelectromechanical system (MEMS) sensors have motivated researchers to study their potential for assessing fallrisk (see [18], [29], [33], and the references therein) as well as monitoring activities of daily livings (ADLs) [3], [8], [11], [12], [20]- [22], [29] and fall events (FEs) [5]- [7], [12]- [15], Manuscript [19], [22], [24]- [26], [28], [30], [31], [33]- [35]. These studies have used accelerometers, gyroscopes, magnetometers, or a combination of them to collect motion datasets at 25 Hz to 200 Hz.…”