Abstract
The ability to anticipate a fall is fundamental for any robot that has to balance. Currently, fast fall-prediction algorithms only exist for simple models, such as the linear inverted pendulum model (LIPM), whose validity breaks down in multicontact scenarios (i.e., when contacts are not limited to a flat ground). This paper presents a fast fall-prediction algorithm based on the point-mass model, which remains valid in multicontact scenarios. The key assumption of our algorithm is that, in order to come to a stop without changing its contacts, a robot only needs to accelerate its center of mass in the direction opposite to its velocity. This assumption allows us to predict the fall by means of a convex optimal control problem, which we solve with a fast custom algorithm (less than 11 ms of computation time). We validated the approach through extensive simulations with the humanoid robot HRP-2 in randomly-sampled scenarios. Comparisons with standard LIPM-based methods demonstrate the superiority of our algorithm in predicting the fall of the robot, when controlled with a state-of-the-art balance controller. This paper lays the foundations for the solution of the challenging problem of push recovery in multicontact scenarios.
Original language | English |
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Pages (from-to) | 1021-1034 |
Number of pages | 14 |
Journal | IEEE Transactions on Robotics |
Volume | 34 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Aug 2018 |
Keywords / Materials (for Non-textual outputs)
- humanoid robots
- legged locomotion
- optimal control
- pendulums
- robot dynamics
- zero step capturability
- legged robots
- fast fall-prediction algorithm
- multicontact scenarios
- point-mass model
- fast custom algorithm
- humanoid robot HRP-2
- randomly-sampled scenarios
- linear inverted pendulum model
- convex optimal control problem
- LIPM-based methods
- Legged locomotion
- Prediction algorithms
- Humanoid robots
- Minimization
- Friction
- Acceleration
- Legged robots
- multicontact
- stability
- viability