Section: Overall Objectives

Research themes

Our approach for addressing the previous challenge is to bring together probabilistic methods, planning techniques and multi-agent decision models. This will be done in cooperation with other disciplines such as sociology for the purpose of taking into account human models. Two main research themes of mobile robotics are addressed : i) Perception and situation awareness ii) Navigation and Cooperation in Dynamic Environments. Next, we elaborate more about these two research axes.

• Perception and Situation Awareness. The main problem is to understand complex dynamic scenes involving mobile objects and human beings, by exploiting prior knowledge and a stream of perceptual data coming from various sensors. Our approach for solving this problem is to develop three complementary problem domains:

  • Bayesian Perception: How to take into account prior knowledge and uncertain sensory data in a dynamic context?

  • Situation awareness : How to interpret the perceived scene and to predict their likely future motion (including near future collision risk) ?

  • Robust state estimation: acquire a deep understanding on several sensor fusion problems and investigate their observability properties in the case of unknown inputs.

• Navigation and Cooperation in Dynamic Environments. The challenge is to build models allowing robots to move and to coordinate efficiently in dynamic environments. We focus on two problems : navigation in human-populated environment (social navigation) and cooperation in large distributed fleet of robots (scalability and robustness issues).

  • Motion-planning in human-populated environment. How to plan trajectories that take into account the uncertainty of human-populated environments and that can respect the social rules of humans ? Such a challenge requires human behavior models, or to learn them, and planning algorithms that take into account them.

  • Decision Making in Multi-robot systems. The goal of this axis is to develop models and algorithms that provide both scalability and performance guarantees in real-world robotic systems. Our methodology builds upon complementary advantages of two orthogonal approaches, Multi-Agent Sequential Decision Making (MA-SDM) and Swarm Intelligence (SI).

The Chroma project is also concerned with applications and transfer of the scientific results. Our main application domains concern autonomous connected vehicles and service robotics. They are presented in Sections 4.2 and 4.3. Chroma have currently projects developed with industrial (as Renault and Toyota) and startup partners.