International audienceA crucial part in biped walking motion generation is to ensure dynamic feasibility, which takes the form of a nonlinear constraint in the general case. Our proposition is to bound the nonlinear part of the dynamic feasibility constraint between some properly chosen extreme values. Making sure that this constraint is satisfied for the extreme values guarantees its satisfaction for all possible values in between. This follows a classical approach from robust nonlinear control theory, which is to consider a nonlinear dynamical system as a specific selection of a time-invariant Linear Differential Inclusion. As a result, dynamic feasibility can be imposed by using only linear constraints, which can be included in an efficient linear MPC scheme, to generate 3D walking motions online. Our simulation results show two major achievements: 1) walking motions over uneven ground such as stairs can be generated online, with guaranteed kinematic and dynamic feasibility, 2) walking on flat ground is significantly improved, with a 3D motion of the CoM closely resembling the one observed in humans

International audienceThe standard approach to real-time control of humanoid robots relies on approximate models to produce a motion plan, which is then used to control the whole body. Separation of the planning stage from the controller makes it difficult to account for the whole body motion objectives and constraints in the plan. For this reason, we propose to omit the planning stage and introduce long-term balance constraints in the whole body controller to compensate for this omission. The new controller allows for generation of whole body walking motions, which are automatically decided based on both the whole body motion objectives and balance preservation constraints. The validity of the proposed approach is demonstrated in simulation in a case where the walking motion is driven by a desired wrist position. This approach is general enough to allow handling seamlessly various whole body motion objectives, such as desired head motions, obstacle avoidance for all parts of the robot, etc

International audienceSafety needs to be guaranteed before we can introduce robots into our working environments. For a biped robot to navigate safely in a crowd it must maintain balance and avoid collisions. In highly dynamic and unpredictable environments like crowds, collision avoidance is usually interpreted as passive safety, i.e. that the robot can stop before any collision occurs. We show that both balance preservation and passive safety can be analyzed, from the point of view of viability theory, as the ability of the robot to stop safely at some point in the future. This allows us to address both problems with a single model predictive controller with appropriate terminal constraints. We demonstrate that this controller predicts failures (falls and collisions) as early as the duration of the preview horizon. Finally, we propose a new strategy for safe navigation that relaxes the passive safety conditions to allow the robot to avoid a greater number of collisions