A Multi-Robot Sense-Act Approach to Lead to a Proper Acting in Environmental Incidents

Many environmental incidents affect large areas, often in rough terrain constrained by natural obstacles, which makes intervention difficult. New technologies, such as unmanned aerial vehicles, may help address this issue due to their suitability to reach and easily cover large areas. Thus, unmanned aerial vehicles may be used to inspect the terrain and make a first assessment of the affected areas; however, nowadays they do not have the capability to act. On the other hand, ground vehicles rely on enough power to perform the intervention but exhibit more mobility constraints. This paper proposes a multi-robot sense-act system, composed of aerial and ground vehicles. This combination allows performing autonomous tasks in large outdoor areas by integrating both types of platforms in a fully automated manner. Aerial units are used to easily obtain relevant data from the environment and ground units use this information to carry out interventions more efficiently. This paper describes the platforms and sensors required by this multi-robot sense-act system as well as proposes a software system to automatically handle the workflow for any generic environmental task. The proposed system has proved to be suitable to reduce the amount of herbicide applied in agricultural treatments. Although herbicides are very polluting, they are massively deployed on complete agricultural fields to remove weeds. Nevertheless, the amount of herbicide required for treatment is radically reduced when it is accurately applied on patches by the proposed multi-robot system. Thus, the aerial units were employed to scout the crop and build an accurate weed distribution map which was subsequently used to plan the task of the ground units. The whole workflow was executed in a fully autonomous way, without human intervention except when required by Spanish law due to safety reasons.This project was funded in part by the 7th Framework Programme of the European Union under Grant Agreement No. 245986. The authors recognise the following RHEA beneficiaries: CSIC (Spain), CogVis (Austria), FTW (Austria), Cyberbotics (Switzerland), University of Pisa (Italy), University Complutense of Madrid (Spain), Tropical (Greece), AGROSAP (Spain), Polytechnic University of Madrid (Spain), AirRobot (Germany), University of Florence (Italy), IRSTEA (France), CNH (Belgium), Bluebotics (Switzerland), and CM (Italy). We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)