A robot acts upon its environment through motion, the ability to plan its movements is therefore an essential component of its autonomy. Motion planning is an area of research that has been widely studied in recent decades. The objective of this thesis is to design algorithmic methods to perform automatic trajectory computation for complex robotic systems in the context of assistive robotics. This emerging field of autonomous robotics applications brings new constraints and new challenges. Such systems that are designed to serve humans and to help in daily tasks must consider the safety and well-being of the surrounding humans. To do this, the robot's motions must be generated by considering the human partner explicitly. For comfort and efficiency, the robot must take into account a model of human social behavior, capabilities and limitations to produce an optimal synergistic behavior. In this thesis we extend to cluttered environments the pioneering work that has been conducted at LAAS in this field. Algorithms that explore the configuration space by random sampling are combined with trajectory optimization algorithms to produce safe and human aware motions. Secondly we propose a planner for object handover taking into account the mobility of the human recipient allowing to share the effort during the transfer. The relevance of this approach has been studied in a user study. Finally, we present a software architecture that allows to take dynamically into account humans during the execution of interactive manipulation tasks. This architecture, developed in collaboration with a partner of the European project Dexmart was also evaluated in a user study.
