Multi-contact Planning on Humans for Physical Assistance by Humanoid

International audienceFor robots to interact with humans in close proximity safely and efficiently, a specialized method to compute whole-body robot posture and plan contact locations is required. In our work, a humanoid robot is used as a caregiver that is performing a physical assistance task. We propose a method for formulating and initializing a non-linear optimization posture generation problem from an intuitive description of the assistance task and the result of a human point cloud processing. The proposed method allows to plan whole-body posture and contact locations on a task-specific surface of a human body, under robot equilibrium, friction cone, torque/joint limits, collision avoidance, and assistance task inherent constraints. The proposed framework can uniformly handle any arbitrary surface generated from point clouds, for autonomously planing the contact locations and interaction forces on potentially moving, movable, and deformable surfaces, which occur in direct physical human-robot interaction. We conclude the paper with examples of posture generation for physical human-robot interaction scenarios

A Practical and Effective Layout for a Safe Human-Robot Collaborative Assembly Task

This work describes a layout to carry out a demonstrative assembly task, during which a collaborative robot performs pick-and-place tasks to supply an operator the parts that he/she has to assemble. In this scenario, the robot and operator share the workspace and a real time collision avoidance algorithm is implemented to modify the planned trajectories of the robot avoiding any collision with the human worker. The movements of the operator are tracked by two Microsoft Kinect v2 sensors to overcome problems related with occlusions and poor perception of a single camera. The data obtained by the two Kinect sensors are combined and then given as input to the collision avoidance algorithm. The experimental results show the effectiveness of the collision avoidance algorithm and the significant gain in terms of task times that the highest level of human-robot collaboration can bring

Real-Time Human Pose Estimation from Body-Scanned Point Clouds

International audienceThis paper presents a novel approach to estimate the human pose from a body-scanned point cloud. To do so, a predefined skeleton model is first initialized according to both the skeleton base point and its torso limb obtained by Principal Component Analysis (PCA). Then, the body parts are iteratively clustered and the skeleton limb fitting is performed, based on Expectation Maximization (EM). The human pose is given by the location of each skeletal node in the fitted skeleton model. Experimental results show the ability of the method to estimate the human pose from multiple point cloud video sequences representing the external surface of a scanned human body; being robust, precise and handling large portions of missing data due to occlusions, acquisition hindrances or registration inaccuracies