ICAI: The Labs – Robotics in NL
This ICAI the Labs session is focused on Robotics in The Netherlands. EAISI FAST lab and the AIRLab Delft share their story. Two speakers highlight their recent work. And two leaders in the field point out future directions.
12.00 (noon): Jesse Scholtes (TU Eindhoven) presents the FAST Lab
12.05: Margot Neggers about Human Comfort in the Context of Human Aware (Mobile Robot) Navigation
12.20: Javier Alonso-Mora (TU Delft) presents the AIRLab Delft
12.25: Max Spahn about Real-time Motion Planning for Mobile Manipulation
12.40: Jesse Scholtes and Javier Alonso-Mora discuss what’s next in Robotics in The Netherlands and beyond
All times are CET.
Human Comfort in the Context of Human Aware (Mobile Robot) Navigation
Margot Neggers (TU Eindhoven)
Autonomous mobile robots that operate in environments with people are expected to be able to deal with human proxemics and social distances. Previous research investigated how robots can approach persons or how to implement human-aware navigation algorithms. However, experimental research on how robots can avoid a person in a comfortable way is largely missing. The aim of the current work is to experimentally determine the shape and size of personal space of a human passed by a robot. In two studies, both a humanoid as well as a non-humanoid robot were used to pass a person at different sides and distances, after which they were asked to rate their perceived comfort. As expected, perceived comfort increases with distance. However, the shape was not circular: passing at the back of a person is more uncomfortable compared to passing at the front, especially in the case of the humanoid robot. These results gives us more insight in the shape and size of personal space in human-robot interaction. Furthermore, they can serve as necessary input to human-aware navigation algorithms for autonomous mobile robots in which human comfort is traded off with efficiency goals.
Real-time Motion Planning for Mobile Manipulation
Max Spahn (TU Delft)
In the future mobile manipulators may operate side by side with humans in retail environments. In this talk we present a method for whole-body trajectory optimization of mobile manipulators in dynamic and unstructured environments. Current trajectory optimization methods typically use decoupling of the mobile base and the robotic arm, which reduces flexibility in motion, does not scale to unstructured environments, and does not consider the future evolution of the environment, which is crucial to avoid dynamic obstacles. Given a goal configuration, such as waypoints generated by a global path planner, we formulate a model predictive control problem (MPC) minimizing the distance-to-target while avoiding collisions with static and dynamic obstacles. The presented method unifies the control of a robotic arm and a non-holonomic base to allow coupled trajectory planning. For collision avoidance, we propose to compute three convex regions englobing the robot’s major body parts (i.e., base, shoulder-link and wrist-link) and thus reducing and limiting the number of inequality constraints, regardless of the number of obstacles in the environment. Moreover, our approach incorporates predicted trajectory information to smoothly, and in advance, avoid dynamic obstacles. The presented results show that trajectory optimization for the coupled system can reduce the total execution time by 48% and that applying the convex region generation for individual links allows keeping the computational costs low, even for complex scenarios, enabling onboard implementation.