This paper presents an algorithm for aerial vehicle trajectory generation based on the optimal Rapidly-exploring Random Tree (RRT*). The trajectory generation for the aerial vehicle is a complex path planning problem since the vehicle flies in a heterogeneous environment. The vehicle must also avoid some obstacles or inaccessible zones such as buildings, mountains and even radar detection zones depending on the mission. The RRT* algorithm is used as a basis to find near-optimal solutions for this problem. The shortest Dubins' path in heterogeneous environment is used to compute a distance and a trajectory between two vehicle states. Simulated results show the capability of the algorithm to find a feasible near-optimal trajectory in terms of path length that anticipates future flight conditions, such as the decrease in maneuverability in high altitude. The results also show the advantages over the numerical methods in avoiding obstacles.

This paper presents missile guidance as a complex robotic problem: a hybrid non-linear system moving in a heterogeneous environment. The proposed solution to this problem combines a sampling-based path planner, Dubins' curves and a locally-optimal guidance law. This algorithm aims to find feasible trajectories that anticipate future flight conditions, especially the loss of manoeuverability at high altitude. Simulated results demonstrate the substantial performance improvements over classical midcourse guidance laws and the benefits of using such methods, well-known in robotics, in the missile guidance field of research.

This paper addresses the problem of planning paths for a vehicle that are guaranteed to be safe in the presence of bounded uncertainty on the model of the vehicle, its sensors, and on the initial configuration. A new conceptual path planner based on rapidly-exploring random trees is proposed, which uses a set description of the uncertain configurations. A practical implementation of this path planner based on interval analysis is then described. It provides paths that could be safely followed by the vehicle during navigation, whatever the values taken by the uncertain quantities.

This paper addresses the problem of safe path planning in an uncertain-configuration space. We consider the case of a car-like robot moving in an indoor environment (three-dimensional space). The extended Kalman filter (EKF) is a popular way to localize such a robot and to estimate its configuration uncertainty during navigation. Consequently, we supply an EKF with simulated measurements in order to compute realistic uncertainties (in a four-dimensional space) during path planning. We show that our safe-RRT algorithm, based upon rapidly-exploring random trees (RRT), is an efficient way to find a path in the resulting seven-dimensional uncertain configuration space