From a trapezoidal acceleration profile to a learnt time optimal control policy for robot braking

Arthur Esquerre-Pourtère (1) , Nicolas Torres Alberto (1, 2) , Vincent Padois (1)

1 AUCTUS - Augmenting human comfort in the factory using cobots
2 Stellantis - PSA Centre Technique de Vélizy

The work presented in this paper tackles the question of braking efficiently with robots. This is an important feature for collaborative robots. These systems need to ensure safety of their surrounding operators without compromising performance. The proposed approach leverages existing motion planning methods based on trapezoidal acceleration profiles which provide a sub-optimal control policy for braking, assuming minorant constant joint jerk and acceleration capacities. Based on this initial solution to braking, a control policy is learnt by reinforcement using the Proximal Policy Optimization deep learning algorithm. This learnt policy assumes constant torque and torque derivative actuation capabilities and predicts an optimal variation of the sub-optimal control policy which better exploits the actuation capabilities of the robot. The principles of this learning approach are first presented and then evaluated in a simulation with an academic example of a Panda robot restricted to 2 degrees of freedom. The obtained results are promising as they lead to a reduction of the stopping time and energy required to brake.

Domaines: Automatique / Robotique; Informatique; Robotique [cs.RO]; Apprentissage [cs.LG]

Arthur Esquerre-Pourtère, Nicolas Torres Alberto, Vincent Padois. From a trapezoidal acceleration profile to a learnt time optimal control policy for robot braking. 2022. ⟨hal-03650665⟩