Robotic systems are increasingly being utilized as fundamental data-gathering tools by scientists, allowing new perspectives and a greater understanding of the planet and its environmental processes. Today's robots are already exploring our deep oceans, tracking harmful algal blooms and pollution spread, monitoring climate variables, and even studying remote volcanoes. This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades. Emerging research trends for achieving large-scale environmental monitoring are also reviewed, including cooperative robotic teams, robot and wireless sensor network (WSN) interaction, adaptive sampling and model-aided path planning. These trends offer efficient and precise measurement of environmental processes at unprecedented scales that will push the frontiers of robotic and natural sciences.

Unmanned aerial vehicles (UAV) have been serving people, fulfilling various roles in research, industry and military, not to exclude personal entertainment. They have also been in use more often for malicious purposes, such as annoying neighbors, disrupting air-travel, attacking people or infrastructure. Therefore, motivated particularly by malicious uses, the research community has been interested in proposing solutions for detection of UAVs. In this work, we present a ground based aerial target detection system using lidars, relying on sparse detections rather than dense point clouds, with the aim of not only detecting but also estimating their motion and active tracking. We lay a theoretical groundwork to analyse the performance of such a lidar based detection system and help understanding its limitations. The work is complemented with field experiments utilizing a state-of-the-art lidar and several drones of different sizes, showing effectiveness in both detection and tracking using sparse measurements.

Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to localize fire sources in an efficient way. In order to achieve this goal, the robots should cooperate in an effective way, so they can individually and simultaneously explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost-gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots motion while avoiding obstacles. When a robot detects a fire, it estimates the flame's position by triangulation. The communication between the robots is done in a decentralized control way where they share the necessary data to generate the map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulation and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements.