This paper addresses the problem of motion planning (MP) in dynamic environments. It is first argued that dynamic environments impose a real-time constraint upon MP: it has a limited time only to compute a motion, the time available being a function of the dynamicity of the environment. Now, given the intrinsic complexity of MP, computing a complete motion to the goal within the time available is impossible to achieve in most real situations. Partial Motion Planning (PMP) is the answer to this problem proposed in this paper. PMP is a motion planning scheme with an anytime flavor: when the time available is over, PMP returns the best partial motion to the goal computed so far. Like reactive navigation scheme, PMP faces a safety issue: what guarantee is there that the system will never end up in a critical situation yielding an inevitable collision? The answer proposed in this paper to this safety issue relies upon the concept of Inevitable Collision States (ICS). ICS takes into account the dynamics of both the system and the moving obstacles. By computing ICS-free partial motion, the system safety can be guaranteed. Application of PMP to the case of a car-like system in a dynamic environment is presented.

This paper addresses the problem of autonomous navigation of a car-like robot evolving in an urban environment. Such an environment exhibits an heterogeneous geometry and is cluttered with moving obstacles. Furthermore, in this context, motion safety is a critical issue. The proposed approach to the problem lies in the design of perception and planning modules that consider explicitly the dynamic nature of the vehicle and the environment while enforcing the safety constraint. The main contributions of this work are the development of such modules and integration into a single application. Initial full scale experiments validating the approach are presented.

The paper addresses the problem of autonomous navigation of a car-like robot evolving in an urban environment. Such an environment exhibits an heterogeneous geometry and is cluttered with moving obstacles. Furthermore, in this context, motion safety is a critical issue. The proposed approach to the problem lies in the coupling of two crucial robotic capabilities, namely perception and planning. The main contributions of this work are the development and integration of these modules into one single application, considering explicitly the constraints related to the environment and the system.

The paper addresses the problem of motion autonomy of Cybercars across a urban intersection. Cybercars are small electric city vehicles aimed at navigating autonomously. In the context of a crossing, the motion generation together with its safety are critical issues. The proposed approach to the problem lies in the coupling of perception and planning capabilities. A new car to car communication algorithm provides necessary information to a trajectory planner capable of iteratively generate safe trajectories within a dynamic environment in order to drive Cybercars safely through the intersection. The main contributions of this work are the development and integration of these modules into one single application, considering explicitly the constraints related to the environment and the system and to provide an original answer to the problem of intelligent crossing.