INTERACT: Intuitive Interaction for Robots among Humans

People

Saray Bakker

Andreu Matoses Gimenez

Dr. Clarence Chen

Prof. Javier Alonso-Mora - Autonomous Multi-Robot Lab (AMR) TU Delft

Prof. Wendelin Bohmer - Key collaborator.

Funding

This project is funded the ERC Starting Grant project "Intuitive Interaction for Humans among Robots (INTERACT)".

About the Project

This project addresses the interaction of highly automated vehicles with vulnerable road users (VRU) such as pedestrians and cyclists, in the context of future urban mobility. The project pursues an integrated approach, covering the spectrum of VRU sensing, cooperative localization, behaviour modeling and intent recognition and vehicle control. Within the AMR group we focus on the vehicle control, enabling safe and efficient autonomous driving.

Partners

Amsterdam Institute for Advanced Metropolitan Solutions (AMS).

Related Publications

Decentralized Multi-Agent Trajectory Planning in Dynamic Environments with Spatiotemporal Occupancy Grid Maps
Siyuan Wu, Gang Chen, Moji Shi, Javier Alonso-Mora. In IEEE Int. Conf. on Robotics and Automation (ICRA), 2024.

abstract pdf

This paper proposes a decentralized trajectory planning framework for the collision avoidance problem of mul- tiple micro aerial vehicles (MAVs) in environments with static and dynamic obstacles. The framework utilizes spatiotemporal occupancy grid maps (SOGM), which forecast the occupancy status of neighboring space in the near future, as the environ- ment representation. Based on this representation, we extend the kinodynamic A* and the corridor-constrained trajectory optimization algorithms to efficiently tackle static and dynamic obstacles with arbitrary shapes. Collision avoidance between communicating robots is integrated by sharing planned tra- jectories and projecting them onto the SOGM. The simulation results show that our method achieves competitive performance against state-of-the-art methods in dynamic environments with different numbers and shapes of obstacles. Finally, the proposed method is validated in real experiments.

Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics
S. Bakker, L. Knoedler, M. Spahn, W. Böhmer, J. Alonso-Mora. In Proc. IEEE International Symposium on Multi-Robot and Multi-Agent Systems, 2023.

abstract pdf code video

Abstract: In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick-and-place scenarios with multiple manipulators, showing high success rates and real-time performance.

Social behavior for autonomous vehicles
W. Schwarting, A. Pearson, J. Alonso-Mora, S. Karaman, D. Rus. In , Proceedings of the National Academy of Sciences USA (PNAS), 2019.

abstract web pdf pdf-supplemental video

Abstract: We present a framework that integrates social psychology tools into controller design for autonomous vehicles. Our key insight utilizes Social Value Orientation (SVO), quantifying an agent’s degree of selfishness or altruism, which allows us to better predict driver behavior. We model interactions between human and autonomous agents with game theory and the principle of best response. Our unified algorithm estimates driver SVOs and incorporates their predicted trajectories into the autonomous vehicle’s control while respecting safety constraints. We study common-yet-difficult traffic scenarios: highway merging and unprotected left turns. Incorporating SVO reduces error in predictions by 25%, validated on 92 human driving merges. Furthermore, we find that merging drivers are more competitive than nonmerging drivers.