Multi Robot Exploration For Environmental Monitoring

Multi-robot Exploration for Environmental Monitoring: The Resource Constrained Perspective provides readers with the necessary robotics and mathematical tools required to realize the correct architecture. The architecture discussed in the book is not confined to environment monitoring, but can also be extended to search-and-rescue, border patrolling, crowd management and related applications. Several law enforcement agencies have already started to deploy UAVs, but instead of using teleoperated UAVs this book proposes methods to fully automate surveillance missions. Similarly, several government agencies like the US-EPA can benefit from this book by automating the process. Several challenges when deploying such models in real missions are addressed and solved, thus laying stepping stones towards realizing the architecture proposed. This book will be a great resource for graduate students in Computer Science, Computer Engineering, Robotics, Machine Learning and Mechatronics. - Analyzes the constant conflict between machine learning models and robot resources - Presents a novel range estimation framework tested on real robots (custom built and commercially available)

This work supports a research program that seeks to radically improve the monitoring and physical collection of harmful algal blooms. The proposed system will outperform current manual, aerial, and satellite observations in both spatial and temporal resolution, via the coordination of unmanned aerial vehicles and unmanned surface vehicles in a collaborative heterogeneous robotic swarm system. In this thesis research, a low-cost highly scalable prototype for autonomously monitoring and mitigating the algal blooms is being developed and tested experimentally. New contributions include development and programming of a scalable low-cost agent-to-agent wireless communication platform. The unmanned surface vehicles are studied with different autonomous navigation patterns for target detection, to improve the overall process efficiency. Initial field experiments are performed with the autonomous boat platforms to establish ground truths on the hardware level.

A revolutionary new framework that draws on insights from ecology for the design and analysis of long-duration robots Robots are increasingly leaving the confines of laboratories, warehouses, and manufacturing facilities, venturing into agriculture and other settings where they must operate in uncertain conditions over long timescales. This multidisciplinary book draws on the principles of ecology to show how robots can take full advantage of the environments they inhabit, including as sources of energy. Magnus Egerstedt introduces a revolutionary new design paradigm—robot ecology—that makes it possible to achieve long-duration autonomy while avoiding catastrophic failures. Central to ecology is the idea that the richness of an organism’s behavior is a function of the environmental constraints imposed by its habitat. Moving beyond traditional strategies that focus on optimal policies for making robots achieve targeted tasks, Egerstedt explores how to use survivability constraints to produce both effective and provably safe robot behaviors. He blends discussions of ecological principles with the development of control barrier functions as a formal approach to constraint-based control design, and provides an in-depth look at the design of the SlothBot, a slow and energy-efficient robot used for environmental monitoring and conservation. Visionary in scope, Robot Ecology presents a comprehensive and unified methodology for designing robots that can function over long durations in diverse natural environments.

Exploration is one of the main research topics in the field of mobile robotics. Multiple robots have been applied for different exploration algorithms In most of the these algorithms the robots exchange data via wireless communication assuming perfect data transmission in the whole environment. Unfortunately, this assumption does not always hold, as e.g. the range of wireless communication is limited. In this thesis Communicative Exploration is presented, which is based on Frontier-Based Exploration. For communicative exploration a group of robots is exploring unknown environments and maintain communication throughout the whole exploration process. The robots form a mobile ad-hoc network that is fully connected for the entire exploration. The motion of the robots is controlled via a heuristic utility function which assigns values to a population of configurations. The configuration with the highest utility value defines the motions of the robots. Different versions of communicative exploration are presented. In the first version a basestation is located outside the environment, from where a human operator can control or supervise the group of robots. During exploration the robots also maintain communication with the basestation. An extension to this version is presented where an improved motion control algorithm is implemented. By calculating a distance transform over the environment the robots are guided better towards the unknown areas of the environment. In a different version the basestation is omitted from the setup to enlarge the reachability of the robots. The group of robots explores the environment acting as a robot pack. Surprisingly, this version introduce situations where the robots get stuck in the environment in deadlock situations. Strategies are introduced that try to avoid or recover from these deadlocks.

Multi-sensor information fusion theory concerns the environmental perception activities to combine data from multiple sensory resources. Humans, as any other animals, gather information from the environment around them using different biological sensors. Combining them allows structuring the decisions and actions when interacting with the environment. Under disaster conditions, effective mult-robot information sensor fusion can yield a better situation awareness to support the collective decision-making. Mobile robots can gather information from the environment by combining data from different sensors as a way to organize decisions and augment human perception. The is especially useful to retrieve contextual environmental information in catastrophic incidents where human perception may be limited (e.g., lack of visibility). To that end, this work proposes a specific configuration of sensors assembled in a mobile robot, which can be used as a proof of concept to measure important environmental variables in an urban search and rescue (USAR) mission, such as toxic gas density, temperature gradient and smoke particles density. This data is processed through a support vector machine classifier with the purpose of detecting relevant contexts in the course of the mission. The outcome provided by the experiments conducted with TraxBot and Pioneer-3DX robots under the Robot Operating System framework opens the door for new multi-robot applications on USAR scenarios. This work was developed within the CHOPIN research project which aims at exploiting the cooperation between human and robotic teams in catastrophic accidents.

Many tasks in the modern world involve collecting information, such as infrastructure inspection, security and surveillance, environmental monitoring, and search and rescue. All of these tasks involve searching an environment to detect, localize, and track objects of interest, such as damage to roadways, suspicious packages, plant species, or victims of a natural disaster. In any of these tasks the number of objects of interest is often not known at the onset of exploration. Teams of robots can automate these often dull, dirty, or dangerous tasks to decrease costs and improve speed and safety. This dissertation addresses the problem of automating data collection processes, so that a team of mobile sensor platforms is able to explore an environment to determine the number of objects of interest and their locations. In real-world scenarios, robots may fail to detect objects within the field of view, receive false positive measurements to clutter objects, and be unable to disambiguate true objects. This makes data association, i.e., matching individual measurements to targets, difficult. To account for this, we utilize filtering algorithms based on random finite sets to simultaneously estimate the number of objects and their locations within the environment without the need to explicitly consider data association. Using the resulting estimates they receive, robots choose actions that maximize the mutual information between the set of targets and the binary events of receiving no detections. This effectively hedges against uninformative actions and leads to a closed form equation to compute mutual information, allowing the robot team to plan over a long time horizon. The robots either communicate with a central agent, which performs the estimation and control computations, or act in a decentralized manner. Our extensive hardware and simulated experiments validate the unified estimation and control framework, using robots with a wide variety of mobility and sensing capabilities to showcase the broad applicability of the framework.

Over the years the growing influence of robotics in the human domain has been noticeable from industrial to space and medical applications as well as a tool in adverse environments and even in everyday tasks. Many of these applications require the use of a team of several cooperating mobile robots - Cooperative Multi-Robot System (CMRS) - to make the execution of certain tasks possible and to improve the performance achieved by only one robot.Although the cooperation capacity is innate to humans, the robotic domain features a number of new challenges: communication, timing of the information obtained and the merger of that information.When cooperation among multiple robots is applied in an exploration context challenges increase. It is essential to take the costs and utility of that exploration into account.This dissertation aims to present a solution to the aforementioned problem. Therefore, a method has been developed, capable of assigning to different robots a cooperative behavior in order to explore an environment, following a philosophy of "divide and conquer".The CoopExp, package with the operation algorithm, was developed according to a distributed approach in order to increase resistance to individual failures of the exploration agents. Accordingly, a method that is able to calculate the costs involved in a faster and more efficient way, was created. Furthermore, an approach to exploration utility was also established, based on a compendium of techniques described in the literature.The development of such programs would have practically been impossible without performing tests on its functioning. In the absence of a simulator for this type of operation, the ARENA (cooperAtive multi Robot frontiEr exploratioN simulAtor) was developed. It consists of a set of new packages specifically designed for frontier identification (aap_frontiers) and to optimize the simulation, through simplifications in the process ofachieving the maps (aap_mapping) and their subsequent combination, yielding the global map (aap_map_merger).Such solutions were validated through simulation tests, using mobile units equipped with a LRF (Laser Range Finder). These tests showed that exploration time decreases when the number of robots is increased, presenting a proper performance in terms of scalability and efficiency in exploration. Last but not least, a exploration with a real team of robots was successfully carried out that was able to communicate through a wireless network in order to validate the practical functioning of this project