MPC-based humanoid pursuit-evasion in the presence of obstacles

We consider a pursuit-evasion problem between humanoids in the presence of obstacles. In our scenario, the pursuer enters the safety area of the evader headed for collision, while the latter executes a fast evasive motion. Control schemes are designed for both the pursuer and the evader. They are structurally identical, although the objectives are different: the pursuer tries to align its direction of motion with the line- of-sight to the evader, whereas the evader tries to move in a direction orthogonal to the line-of-sight to the pursuer. At the core of the control architecture is a Model Predictive Control scheme for generating a stable gait. This allows for the inclusion of workspace obstacles, which we take into account at two levels: during the determination of the footsteps orientation and as an explicit MPC constraint. We illustrate the results with simulations on NAO humanoids

Safe 3D Bipedal Walking through Linear MPC with 3D Capturability

International audienceWe propose a linear MPC scheme for online computation of reactive walking motions, necessary for fast interactions such as physical collaboration with humans or collision avoidance in crowds. Unlike other existing schemes, it provides fully adaptable height, adaptable step placement and complete kinematic and dynamic feasibility guarantees, making it possible to walk perfectly safely on a piecewise horizontal ground such as stairs. A linear formulation is proposed, based on efficiently bounding the nonlinear term introduced by vertical motion, considering two linear constraints instead of one nonlinear constraint. Balance and Passive Safety guarantees are secured by enforcing a 3D capturability constraint. Based on a comparison between CoM and CoP trajectories involving exponentials instead of polynomials, this capturability constraint involves a CoM motion stopping along a segment of line, always maintaining complete kinematic and dynamic feasibility

Real-time pursuit-evasion with humanoid robots

We consider a pursuit-evasion problem between humanoids. In our scenario, the pursuer enters the safety area of the evader headed for collision, while the latter executes a fast evasive motion. Control schemes are designed for both the pursuer and the evader. They are structurally identical, although the objectives are different: the pursuer tries to align its direction of motion with the line-of-sight to the evader, whereas the evader tries to move in a direction orthogonal to the line-of-sight to the pursuer. At the core of the control scheme is a maneuver planning module which makes use of closed- form expressions exclusively. This allows its use in a replanning framework, where each robot updates its motion plan upon completion of a step to account for the perceived motion of the other. Simulation and experimental results on NAO humanoids reveal an interesting asymptotic behavior which was predicted using unicycle as template models for trajectory generation

Impact-Aware Online Motion Planning for Fully-Actuated Bipedal Robot Walking

The ability to track a general walking path with specific timing is crucial to the operational safety and reliability of bipedal robots for avoiding dynamic obstacles, such as pedestrians, in complex environments. This paper introduces an online, full-body motion planner that generates the desired impact-aware motion for fully-actuated bipedal robotic walking. The main novelty of the proposed planner lies in its capability of producing desired motions in real-time that respect the discrete impact dynamics and the desired impact timing. To derive the proposed planner, a full-order hybrid dynamic model of fully-actuated bipedal robotic walking is presented, including both continuous dynamics and discrete lading impacts. Next, the proposed impact-aware online motion planner is introduced. Finally, simulation results of a 3-D bipedal robot are provided to confirm the effectiveness of the proposed online impact-aware planner. The online planner is capable of generating full-body motion of one walking step within 0.6 second, which is shorter than a typical bipedal walking step

Viability-Based Guaranteed Safe Robot Navigation

International audienceGuaranteeing safe, i.e. collision-free, motion for robotic systems is usually tackled in the Inevitable Collision State (ICS) framework. This paper explores the use of the more general Viability theory as an alternative when safe motion involves multiple motion constraints and not just collision avoidance. Central to Viability is the so-called viability kernel, i.e. the set of states of the robotic system for which there is at least one trajectory that satisfies the motion constraints forever. The paper presents an algorithm that computes off-line an approximation of the viability kernel that is both conservative and able to handle time-varying constraints such as moving obstacles. Then it demonstrates, for different robotic scenarios involving multiple motion constraints (collision avoidance, visibility, velocity), how to use the viability kernel computed off-line within an on-line reactive navigation scheme that can drive the robotic system without ever violating the motion constraints at hand

Navigation pour robot avec garantie de sécurité basée sur la théorie de la viabilité

Guaranteeing safe, i.e. collision-free, motion for robotic systems is usually tackled in the InevitableCollision State framework. This paper explores the use of the more general Viability theory as analternative when safe motion involves multiple motion constraints and not just collision avoidance. Centralto Viability is the so-called viability kernel, i.e. the set of states of the robotic system for which there isat least one trajectory that satisfies the motion constraints forever. The paper presents an algorithm thatcomputes off-line an approximation of the viability kernel that is both conservative and able to handletime-varying constraints such as moving obstacles. Then it demonstrates, for different robotic scenarios involvingmultiple motion constraints (collision avoidance, visibility, velocity), how to use the viability kernelcomputed off-line within an on-line reactive navigation scheme that can drive the robotic system withoutever violating the motion constraints at hand.La garantie de mouvement sans collision pour les systèmes robotiques est généralement abordéedans le cadre des Etats de Collision Inévitable. Cet article explore l’utilisation de la théorie plusgénérale de la Viabilité comme alternative lorsque le mouvement implique des contraintes de mouvementautres que l’évitement de collision. Le noyau de viabilité, i.e. l’ensemble des états du systèmerobotique pour lequel il existe au moins une trajectoire qui satisfait à jamais les contraintes de mouvement,est un élément central de la théorie de la viabilité. Cet article présente un algorithme qui calculehors ligne une approximation du noyau de viabilité qui est à la fois conservative et capable de gérer descontraintes dynamiques telles que des obstacles mobiles. Ensuite, il démontre, pour différents scénariosrobotiques impliquant plusieurs contraintes de mouvement (évitement de collision, visibilité, vitesse),comment utiliser le noyau de viabilité calculé hors ligne dans un schéma de navigation réactive en lignecapable de piloter le système robotique sans jamais violer les différentes contraintes de mouvement

Safe navigation strategies for a biped robot walking in a crowd

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