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.
In order to fluidly perform complex tasks in collaboration with a human being, such as table handling, a humanoid robot has to recognize and adapt to human movements. To achieve such goals, a realistic model of the human locomotion that is computable on a robot is needed. In this paper, we focus on making a humanoid robot follow a human-like locomotion path. We mainly present two models of human walking which lead to compute an average trajectory of the body center of mass from which a twist in the 2D plane can be deduced. Then the velocities generated by both models are used by a walking pattern generator to drive a real TALOS robot [1]. To determine which of these models is the most realistic for a humanoid robot, we measure human walking paths with motion capture and compare them to the computed trajectories.
This system paper describes the integration and the evaluation of an ICP-based localization system on the TALOS humanoid robot. The new generation of flash LiDAR systems, here an Ouster OS1-64, have made it possible to obtain 3D clouds at 10 Hz. Coupled with an Intel RealSense T265 providing visual-inertial odometry it is possible to localize the robot and use this information to generate foot steps in real time to reach specific points. The approach is validated with a Qualisys motion capture system. It is also used to generate real-time walking motion on the TALOS humanoid robot. This paper is an integration paper showing that it is now feasible to accurately guide a humanoid robot in an environment in real time using a LiDAR system.
Some works have already studied human trajectories during spontaneous locomotion. However, this topic has not been thoroughly studied in the context of human-human interactions, especially during collaborative carriage tasks. Thus, this manuscript aims to provide a broad analysis of the kinematics of two subjects carrying a table. In the present study, 20 pairs of subjects moved a table to 9 different goal positions distant of 2.7–5.4 m. This was performed with only one or both subjects knowing the target location. The analysis of the collected data demonstrated that there is no shared strategy implemented by all the pairs to move the table around. We observed a great variability in the pairs’ behaviours. Even the same pair can implement various strategies to move a table to the same goal position. Moreover, a model of the trajectories adopted by collaborating pairs was proposed and optimized with an inverse optimal control scheme. Even if it produced consistent results, due to the great variability which origins were not elucidated, it was not possible to accurately simulate the average trajectories nor the individual ones. Thus, the approach that was shown to be efficient to simulate single walking subjects failed to model the behaviour of collaborating pairs.
The study of human-human interactions is essential for a better understanding of human behaviour during collaborative tasks. This knowledge is not only interesting in life science but can also be useful in robotic science. Indeed, to efficiently assist a human partner during a human-robot collaboration, the robot needs to be as reactive as a human would be. This can only be achieved by embedding a model of human behaviour into the robot control scheme. In this paper, a human-humanoid robot collaboration to carry a table is tackled. First, the experimental Center of Mass (CoM) trajectories of a table carried by 20 pairs of subjects to various goal positions are studied and modeled using an optimal control problem. Then, based on this model, a prediction process which accurately predicts the table trajectories is designed. Finally, this prediction process is coupled with the robot Walking Pattern Generator (WPG). Using a torque whole-body controller, this framework is tested in simulation on Gazebo on a TALOS humanoid robot model. In this simulation, the robot actively assists a simulated human partner in lifting and carrying a table to an unknown goal position.
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