Cobotic applications require a good knowledge of human behaviour in order to be cleverly, securely and fluidly performed. For example, to make a human and a humanoid robot perform a co-navigation or a co-manipulation task, a model of human walking trajectories is essential to make the robot follow or even anticipate the human movements. This paper aims to study the Center of Mass (CoM) path during locomotion and generate human-like trajectories with an optimal control scheme. It also proposes a metric which allows to assess this model compared to the human behaviour. CoM trajectories during locomotion of 10 healthy subjects were recorded and analysed as part of this study. Inverse optimal control was used to find the optimal cost function which best fits the model to the measurements. Then, the measurements and the generated data were compared in order to assess the performance of the presented model. Even if the experiments show a great variability in human behaviours, the model presented in this study gives an accurate approximation of the average human walking trajectories. Furthermore, this model gives an approximation of human locomotion good enough to improve cobotic tasks allowing a humanoid robot to anticipate the human behaviour.
In order to smoothly perform interactions between a humanoid robot and a human, knowledge about the human locomotion can be efficiently used. Indeed, in a human-robot collaboration, a prediction model of the human behaviour allows the robot to act proactively. In this paper, an optimal control based model predicting the human Center of Mass (CoM) trajectory during gait is presented. A Walking Pattern Generator (WPG) based on non-linear model predictive control is, then, introduced in order to generate the robot CoM and footsteps along the predicted trajectory. The combination of the human trajectory prediction model and this new WPG aims to allow the robot to proactively walk along with a human instead of passively follow him. These models have been tested in simulation on Gazebo on a TALOS humanoid robot model using measured human trajectories. To perform the CoM and foot trajectories computed by the WPG, a real-time whole-body controller is used. This controller is a Quadratic Program which solves the inverse dynamics of the robot at torque level.
Context. Cometary dust particles are remnants of the primordial accretion of refractory material that occurred during the initial stages of the Solar System formation. Understanding their physical structure can help constrain their accretion process.Aims. The in situ study of dust particles collected at slow speeds by instruments on-board the Rosetta space mission, including GIADA, MIDAS and COSIMA, can be used to infer the physical properties, size distribution, and typologies of the dust. Methods. We have developed a simple numerical simulation of aggregate impact flattening to interpret the properties of particles collected by COSIMA. The aspect ratios of flattened particles from both simulations and observations are compared to differentiate between initial families of aggregates characterized by different fractal dimensions D f . This dimension can differentiate between certain growth modes, namely the Diffusion Limited Cluster-cluster Aggregates (DLCA, D f ≈ 1.8), Diffusion Limited Particle-cluster Aggregates (DLPA, D f ≈ 2.5), Reaction Limited Cluster-cluster Aggregates (RLCA, D f ≈ 2.1), and Reaction Limited Particle-cluster Aggregates (RLPA, D f ≈ 3.0). Results. The diversity of aspect ratios measured by COSIMA is consistent with either two families of aggregates with different initial D f (a family of compact aggregates with fractal dimensions close to 2.5-3 and some fluffier aggregates with fractal dimensions around 2). Alternatively, the distribution of morphologies seen by COSIMA could originate from a single type of aggregation process, such as DLPA, but to explain the range of aspect ratios observed by COSIMA a large range of dust particle cohesive strength is necessary. Furthermore, variations in cohesive strength and velocity may play a role in the higher aspect ratio range detected (>0.3). Conclusions. Our work allows us to explain the particle morphologies observed by COSIMA and those generated by laboratory experiments in a consistent framework. Taking into account all observations from the three dust instruments on-board Rosetta, we favor an interpretation of our simulations based on two different families of dust particles with significantly distinct fractal dimensions ejected from the cometary nucleus.
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