Missile trajectory shaping using sampling-based path planning

International audienceThis 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

Vers une planification robuste et sûre pour les systèmes autonomes

Many tools exist to solve constrained path-planning problems. They can be classified as follows. In the older ones, paths are planned in a discretized state space. The most recent, sampling-based path planners can explore the whole state space more efficiently. These path planners are used in many fields, e;g., chemistry, biology, automatic control or robotics. The main contribution of our work is to provide a solution to the path planning problem when uncertainty is present. Modern planning techniques are used in combination with localization algorithms that make it possible to characterize the uncertainty on the system state at a given time. Two approaches are considered. In the first one, the path planner uses a probabilistic representation of the state space at any given time using multivariate Gaussian distributions. An extended Kalman filter is used to propagate the state error. In the second approach, all states that are consistent with bounds on the errors are enclosed in a computable set. Contrary to the previous probabilistic method, this one is able to guarantee the safety of the system moving along the planned path, provided of course that the hypotheses on which it is based are satisfied.De nombreux outils existent pour résoudre les problèmes de planification sous contraintes. On peut les regrouper en deux classes principales. Les plus anciens planificateurs utilisent une discrétisation préalable de l'espace d'état. Les plus récents, les planificateurs à échantillonnage, permettent une exploration plus efficace. Ces planificateurs sont utilisés dans de nombreux domaines, comme la chimie, la biologie, la robotique, l'automatique ou encore l'intelligence artificielle. La contribution majeure de nos travaux est d'apporter une réponse au problème de planification de trajectoires en présence d'incertitudes en associant une technique de planification moderne, permettant une exploration rapide de l'espace d'état à des méthodes de localisation permettant de caractériser l'incertitude sur l'état du système à un instant donné. Deux approches ont été suivies. Dans la première, le planificateur utilise une représentation probabiliste de l'état du système à un instant donné, par une densité de probabilité gaussienne. La propagation des erreurs est effectuée en utilisant un filtre de Kalman étendu. Dans la deuxième approche, nous englobons dans un ensemble calculable les états que peut prendre le système à un instant donné compte tenu de bornes sur les erreurs commises. Contrairement à l'approche probabiliste précédente, cette approche permet de fournir une garantie sur la sûreté du système, à condition bien sûr que les hypothèses sur les bruits d'états qui la fondent soient satisfaites

Vers une planification robuste et sûre pour les systèmes autonomes

ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Shortest paths for the Dubins' vehicle in heterogeneous environments

International audienceIn this paper, the problem of finding minimum length paths for a Dubins' vehicle that can only move forward in an heterogeneous environment is considered. An hybrid version of the Pontryagin's maximum principle is used to derive necessary conditions for optimality. Unlike in the case of homogeneous environments, it is deduced that heterogeneity of the environment implies that optimal paths can contain reflections. A subclass of environments is analyzed more specifically in order to obtain additional necessary conditions. Based on these results, two concrete application cases are detailed to demonstrate the usefulness of the approach in practice. The first example concerns a mobile robot and the second example concerns a glider

Sampling-based path planning: a new tool for missile guidance

International audienceA new missile midcourse guidance algorithm is proposed in this paper. It is a combination of sampling based path planning, Dubins' curves and classical guidance laws. Moreover, a realistic interceptor missile model is used. It allows to anticipate the future changes of flight conditions along the trajectory, especially the loss of maneuverability at high altitude. Simulation results are presented to demonstrate the substantial performance improvements over classical midcourse guidance laws

Shortest path for aerial vehicles in heterogeneous environment using RRT

International audienceThis 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