Section: Overall Objectives

Research themes

To address previous challenges, we take advantage of recent advances in all: probabilistic methods, planning techniques, multi-agent decision making, and machine learning. We also draw inspiration from other disciplines such as Sociology to take 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 themes.

• Perception and Situation Awareness. This theme aims at understanding complex dynamic scenes, involving mobile objects and human beings, by exploiting prior knowledge and streams of perceptual data coming from various sensors. To this end, we investigate three complementary research problems:

  • 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. This theme aims at designing models and algorithms 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 respect the social rules of human beings? Such a challenge requires models of human behavior to be learnt or designed as well as dedicated learning or planning algorithms.

  • Multi-robot decision making in complex environments. How to design models and algorithms that can achieve 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).

Chroma is also concerned with applications and transfer of the scientific results. Our main applications include autonomous and connected vehicles as well as service robotics. They are presented in Sections 4.2 and 4.3, respectively. Chroma is currently involved on many projects in collaboration with automobile companies (including Renault, Toyota and Volvo) and startups.