Walking on irregular surfaces and in the presence of unexpected events is a challenging problem for bipedal machines. Up to date, their ability to cope with gait disturbances is far less successful than humans’: Neither trajectory controlled robots, nor dynamic walking machines (Limit Cycle Walkers) are able to handle them satisfactorily. On the contrary, humans reject gait perturbations naturally and efficiently relying on their sensory organs that, if needed, elicit a recovery action. A similar approach may be envisioned for bipedal robots and exoskeletons: An algorithm continuously observes the state of the walker and, if an unexpected event happens, triggers an adequate reaction. This paper presents a monitoring algorithm that provides immediate detection of any type of perturbation based solely on a phase representation of the normal walking of the robot. The proposed method was evaluated in a Limit Cycle Walker prototype that suffered push and trip perturbations at different moments of the gait cycle, providing 100% successful detections for the current experimental apparatus and adequately tuned parameters, with no false positives when the robot is walking unperturbed.
Walking in the presence of perturbations poses a demanding challenge to bipedal robots. In this context, we propose an approach based on an algorithm that observes continuously the state of the robot, and if a perturbation is detected, triggers a recovery action. This paper focuses in the former, it presents a simple algorithm to detect online the occurrence of a perturbation with short delay. Simulations with a complex biped model yield no false positives when walking stably, and provide detection of trips with an average delay of 30.2 ms, comparable to short latency reflexes in humans.
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