Optimized trajectories of multi-robot deploying wireless sensor nodes

International audienceA main reason to the growth of wireless sensor networks deployed worldwide is their easy and fast deployment. In this paper we consider deployments assisted by mobile robots where static sensor nodes are deployed by mobile robots in a given area. Each robot must make a tour to place its sensor nodes. All sensor nodes must be placed at their precomputed positions. The Multi-Robot Deploying wireless Sensor nodes problem, called the MRDS problem, consists in minimizing the longest tour duration (i.e. the total deployment duration), the number of robots used and the standard deviation between duration of robots tours. After a formal definition of the MRDS problem, we show how to use a multi-objective version of genetic algorithms, more precisely the NSGA-II algorithm, to solve this multi-objective optimization problem. The solutions belonging to the best Pareto front are given to the designer in charge of selecting the best trade-off taking into account various criteria. We then show how to extend this method to take obstacles into account, which is more representative of real situations

A 2-dimensional ACO-based path planner for off-line robot path planning

2013-2014 > Academic research: refereed > Refereed conference paperAccepted ManuscriptPublishe

NeBula: TEAM CoSTAR’s robotic autonomy solution that won phase II of DARPA subterranean challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR’s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.Peer ReviewedAgha, A., Otsu, K., Morrell, B., Fan, D. D., Thakker, R., Santamaria-Navarro, A., Kim, S.-K., Bouman, A., Lei, X., Edlund, J., Ginting, M. F., Ebadi, K., Anderson, M., Pailevanian, T., Terry, E., Wolf, M., Tagliabue, A., Vaquero, T. S., Palieri, M., Tepsuporn, S., Chang, Y., Kalantari, A., Chavez, F., Lopez, B., Funabiki, N., Miles, G., Touma, T., Buscicchio, A., Tordesillas, J., Alatur, N., Nash, J., Walsh, W., Jung, S., Lee, H., Kanellakis, C., Mayo, J., Harper, S., Kaufmann, M., Dixit, A., Correa, G. J., Lee, C., Gao, J., Merewether, G., Maldonado-Contreras, J., Salhotra, G., Da Silva, M. S., Ramtoula, B., Fakoorian, S., Hatteland, A., Kim, T., Bartlett, T., Stephens, A., Kim, L., Bergh, C., Heiden, E., Lew, T., Cauligi, A., Heywood, T., Kramer, A., Leopold, H. A., Melikyan, H., Choi, H. C., Daftry, S., Toupet, O., Wee, I., Thakur, A., Feras, M., Beltrame, G., Nikolakopoulos, G., Shim, D., Carlone, L., & Burdick, JPostprint (published version