Combiner Optimisation Stochastique et Frontières pour l'Exploration 3D avec un Flotte de Drones

National audienc

Combining Stochastic Optimization and Frontiers for Aerial Multi-Robot Exploration of 3D Terrains

International audienceThis paper addresses the problem of exploring unknown terrains with a fleet of cooperating aerial vehicles. We present a novel decentralized approach which alternates gradient-free stochastic optimization and frontier-based approaches. Our method allows each robot to generate its trajectory based on the collected data and the local map built integrating the information shared by its team-mates. Whenever a local optimum is reached, which corresponds to a location surrounded by already explored areas, the algorithm identifies the closest frontier to get over it and restarts the local optimization. Its low computational cost, the capability to deal with constraints and the decentralized decision-making make it particularly suitable for multi-robot applications in complex 3D environments. Simulation results show that our approach generates feasible and safe trajectories which drive multiple robots to completely explore realistic environments. Furthermore, in terms of exploration time, our algorithm significantly outperforms a standard solution based on closest frontier points while providing similar performances compared to a computationally more expensive centralized greedy solution

A Common Optimization Framework for Multi-Robot Exploration and Coverage in 3D Environments

International audienceThis paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives

Combiner Optimisation Stochastique et Frontières pour l'Exploration 3D avec un Flotte de Drones

National audienc

Combining Stochastic Optimization and Frontiers for Aerial Multi-Robot Exploration of 3D Terrains

International audienceThis paper addresses the problem of exploring unknown terrains with a fleet of cooperating aerial vehicles. We present a novel decentralized approach which alternates gradient-free stochastic optimization and frontier-based approaches. Our method allows each robot to generate its trajectory based on the collected data and the local map built integrating the information shared by its team-mates. Whenever a local optimum is reached, which corresponds to a location surrounded by already explored areas, the algorithm identifies the closest frontier to get over it and restarts the local optimization. Its low computational cost, the capability to deal with constraints and the decentralized decision-making make it particularly suitable for multi-robot applications in complex 3D environments. Simulation results show that our approach generates feasible and safe trajectories which drive multiple robots to completely explore realistic environments. Furthermore, in terms of exploration time, our algorithm significantly outperforms a standard solution based on closest frontier points while providing similar performances compared to a computationally more expensive centralized greedy solution

Computing capture tubes

International audienceA dynamic system can often be described by a state equation ˙x = h(x, u, t)where x ∈ Rn is the state vector, u ∈ Rm is the control vector and h :Rn × Rp × R → Rn is the evolution function. Assume that the control lowu = g (x, t) is known (this can be obtained using control theory), the systembecomes autonomous. If we define f (x, t) = h(x, g (x, t) , t), we get the followingequation.˙x = f (x, t) .The validation of some stability properties of this system is an important anddifficult problem [2] which can be transformed into proving the inconsistency of aconstraint satisfaction problem. For some particular properties and for invariantsystem (i.e., f does not depend on t), it has been shown [1] that the V-stabilityapproach combined interval analysis [3] can solve the problem efficiently. Here,we extend this work to systems where f depends on time

Computing capture tubes

International audienceMany mobile robots such as wheeled robots, boats, or plane are described by nonholonomic differential equations. As a consequence, they have to satisfy some differential constraints such as having a radius of curvature for their trajectory lower than a known value. For this type of robots, it is difficult to prove some properties such as the avoidance of collisions with some moving obstacles. This is even more difficult when the initial condition is not known exactly or when some uncertainties occur. This paper proposes a method to compute an enclosure (a tube) for the trajectory of the robot in situations where a guaranteed interval integration cannot provide any acceptable enclosures. All properties that are satisfied by the tube (such as the non-collision) will also be satisfied by the actual trajectory of the robot