For successful physical human-robot interaction, the capability of a robot to understand its environment is imperative. More importantly, the robot should extract from the human operator as much information as possible. A reliable 3D skeleton extraction is essential for a robot to predict the intentions of the operator while s/he moves toward the robot or performs a meaningful gesture. For this purpose, we have integrated a time-of-flight depth camera with a state-of-the-art 2D skeleton extraction library namely Openpose, to obtain 3D skeletal joint coordinates reliably. We have also developed a robust and rotation invariant (in the coronal plane)hand gesture detector using a convolutional neural network. At run time (after having been trained)the detector does not require any pre-processing of the hand images. A complete pipeline for skeleton extraction and hand gesture recognition is developed and employed for real-time physical human-robot interaction, demonstrating the promising capability of the designed framework. This work establishes a firm basis and will be extended for the development of intelligent human intention detection in physical human-robot interaction scenarios, to efficiently recognize a variety of static as well as dynamic gestures.

This paper presents a tentacle-based obstacle avoidance scheme for omnidirectional mobile robots that must satisfy visibility constraints during navigation. The navigation task consists of driving the robot towards a visual target in the presence of environment (static or moving) obstacles. The target is acquired by an on-board camera, while the obstacles surrounding the robot are sensed by laser range scanners. To perform such task, the robot must avoid the obstacles while maintaining the target in its field of view. The approach is validated in both simulated and real experiments.

In human-robot interaction, the robot must behave safely, especially when an operator is present in its workspace. Even higher safety levels must be attained when physical contact occurs between the two. To this end, standards such as the ISO10218 define the requirements for a robot to be considered safe for interaction with human operators in an industrial environment. In this paper, we propose an adaptive damping controller that fulfills the ISO10218 requirements by limiting the tool velocity, power and contact force online (and only when needed). The controller is experimentally validated on a hand-arm robotic system, in a mock-up collaborative application. For the hand, safe interaction is enhanced by using tactile sensing, both to regulate grasp forces and to provide an intuitive interface for the operator.

Non-holonomic omnidirectional mobile robots have higher load carrying capacity than their holonomic counterparts. Once the steer joint configuration is initialized, they can perform arbitrarily complex three-dimensional trajectories in the plane of motion and, as such, are more suitable for industrial contexts. However, their kinematic model presents representational and structural singularities, solutions to which must respect actuator performance limits. Recent research efforts have provided either simple restricting of the velocity space (among which few considered hardware limits) or complex non-restricting (no hardware limits considered) solutions. Most of these efforts are providing solutions at the kinematic control level. Instead, here we propose both a representational singularity free kinematic model, and a simple numeric treatment for the kinematic singularity. We further provide a method to tune the latter, to respect the actuator acceleration limits. Thanks to its steer rate damping behavior, the method can be further extended, to respect joint limits. Another benefit is the treatment of the singularity at the level of the kinematic model, which enhances real time capabilities. The developed method has been tested successfully on the Neobotix-MPO700 mobile robot and shown superior results as compared to the embedded controller.

Mobile robots are nowadays used in a wide variety of missions especially for exploring environments and allowing scientists who study these environments to gather data about them. However the monolithic design of control laws is often a hindrance to adapting control solutions from a robotic application to another. We thus propose a new control description paradigm strongly focused on modularity as well as integrating constraints coming both from the system capabilities and from the concerns of control engineering (such as stability). In this paper, we present the main ideas behind our approach and illustrate it through Hardware-In-the-Loop simulation.

For intuitive human-robot collaboration, the robot must quickly adapt to the human behavior. To this end, we propose a multimodal sensor-based control framework, enabling a robot to recognize human intention, and consequently adapt its control strategy. Our approach is marker-less, relies on a Kinect and on an on-board camera, and is based on a unified task formalism. Moreover, we validate it in a mock-up industrial scenario, where human and robot must collaborate to insert screws in a flank.