Envirobot: A Bio-Inspired Environmental Monitoring Platform

Autonomous marine vehicles are becoming essential tools in aquatic environmental monitoring systems, and can be used for instance for data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. In this work, we present an unconventional bio-inspired autonomous robot aimed for execution of such tasks. The Envirobot platform is based on our existing segmented anguilliform swimming robots, but with important adaptations in terms of energy use and efficiency, control, navigation, and communication possibilities. To this end, Envirobot has been designed to have more endurance, flexible computational power, long range communication link, and versatile flexible environmental sensor integration. Its low level control is powered by an ARM processor in the head unit and micro processors in each active module. On top of this, integration of a computer-on-module enables versatile high level control methods. We present some preliminary results and experiments done with Envirobot to test the added navigation and control strategies

Environmental Monitoring using Autonomous Vehicles: A Survey of Recent Searching Techniques

Autonomous vehicles are becoming an essential tool in a wide range of environmental applications that include ambient data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. Among these applications, pollution source localization has drawn increasing interest due to its scientific and commercial interest and the emergence of a new breed of robotic vehicles capable of performing demanding tasks in harsh environments without human supervision. In this task, the aim is to find the location of a region that is the source of a given substance of interest (e.g. a chemical pollutant at sea or a gas leakage in air) using a group of cooperative autonomous vehicles. Motivated by fast paced advances in this challenging area, this paper surveys recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles

Optimal Search Strategies for Pollutant Source Localization

This paper is aimed at developing optimal motion planning for a single autonomous surface vehicle (ASV) equipped with an on-board pollutant sensor that will maximize the sensor-related information available for source seeking. The ASV uses a nonlinear diffusion model of the pollutant source to estimate the intensity/level of the pollution at the present ASV location. The rate of detection of particles depends on the relative distance between the ASV and the source. First, we use a probabilistic map of the source location built through the sensor information for a dynamic motion planning of source seeking based on an entropy reduction formulation, where an appropriately defined Fisher information matrix (FIM) is used for entropy reduction or information gain. We derive the FIM for the set-up and investigate optimal trajectories. Next, we present an online nonlinear Monte Carlo algorithm that uses the obtained sensor information about pollutant at different vehicle locations to update a probabilistic uncertainty map of pollutant source location. As the mission unfolds the ASV motion is computed by considering a moving-horizon interval of decision, which will allow for the inclusion of new information available for optimal motion planning. The proposed motion planning approach is extended to take into account external disturbances and it is able to minimize the uncertainty in the pollutant source. Finally, we provide two case studies to demonstrate efficacy of the proposed motion planning algorithm