The advance of technology has enabled the development of unmanned systems/vehicles used in the air, on the ground or on/in the water. The application range for these systems is continuously increasing, and unmanned platforms continue to be the subject of numerous studies and research contributions. This paper deals with the role of sensors and measurements in ensuring that unmanned systems work properly, meet the requirements of the target application, provide and increase their navigation capabilities, and suitably monitor and gain information on several physical quantities in the environment around them. Unmanned system types and the critical environmental factors affecting their performance are discussed. The measurements that these kinds of vehicles can carry out are presented and discussed, while also describing the most frequently used on-board sensor technologies, as well as their advantages and limitations. The paper provides some examples of sensor specifications related to some current applications, as well as describing the recent research contributions in the field.

https://www.mdpi.com/1424-8220/21/4/1518/pdf

Sensors and Measurements for Unmanned Systems: An Overview.

An economical marine vehicle was designed for environmental monitoring and field-implementation of multi-robot behaviors. This vehicle, classed as a diving autonomous surface vehicle, is capable of maneuvering along the surface using port and starboard thrusters. A variable buoyancy chamber, or buoyancy engine, allows the vehicle to take a vertical profile of the water column by pumping water into and out of a trio of actuated syringes. The operational concept of transiting along the surface and stopping to take a vertical profile, is intended to efficiently measure water column parameters in a manner suitable for expansion to multiple vehicles. Although the same task could be performed by one or more traditional autonomous underwater vehicles, the infrastructure for underwater communication and navigation is cost prohibitive at the level of efficiency achieved by the proposed fleet of diving autonomous vehicles. After progressing through two prototype phases, one vehicle is currently undergoing in-water trials. The other is being used to explore interfaces using the Robot Operating System on its Raspberry Pi control computer. Unifying the independent progress to upgrade both vehicles will result in the first two-vehicle field tests of the control system. Production copies with follow for developing true multi-vehicle adaptive behaviors suitable for marine environmental measurements.

A Diving Autonomous Surface Vehicle for Multi-Robot Research and Development

Environmental fronts are transition regions characterized by a steep gradient in the value of a scalar field. Finding and spatially characterizing these fronts is critical for a variety of scientific applications; however, such work is challenging and usually performed manually, limited to localized characterization, and/or based on assumptions regarding the structure of the front. In this article, we present an automated multirobot control technique to find and spatially characterize general scalar field fronts. This is achieved with a multirobot system that adaptively sizes, orients, and translates based on data sampled by each robot in order to span the width of the front, and to move longitudinally along the front, reversing direction when the end of the front is reached. This span and scan functionality allows the cluster to spatially characterize the front's full extent and to enable comprehensive scientific study of its associated phenomenon. To our knowledge, this is the first comprehensive, adaptive multirobot front-following control strategy proposed for general front structures, and initial verification of its capability is provided through both simulation of behavior on a large-scale front and experimentation with a simple, small-scale indoor multirobot testbed.

Navigation of Scalar Fronts With Multirobot Clusters in Simulation and Experiment

Adaptive navigation of scalar fields is a compelling capability in which mobile robotic systems make real-time navigation decisions based on sensed measurements of the environment. This capability can enable efficient identification and location of specific features of interest within the field of interest, potentially saving time and energy while also being responsive to changing conditions. Applications can include finding the sources and impact zones of a pollutant, establishing hazard perimeters, finding safe zones, and safe paths of travel. This paper presents new work that experimentally verifies several adaptive navigation control policies for moving to/along critical scalar field features with a group of mobile robots. Specifically, we demonstrate the use of a five robot cluster of sensor-equipped mobile robots to descend ridges within a scalar field, to ascend trenches, and to move to and hold a position at saddle points. This is done through the use of differential measurements across the cluster's formation baselines and control laws that have been previously demonstrated in simulation. This paper also incorporates a new state machine within the adaptive navigation control architecture in order to monitor the performance of the individual control primitives and to respond to conditions such as losing track of the feature of interest. Finally, this paper is the first in which we have experimentally demonstrated control of a five robot group of robots using our cluster space control methodology. The experiments were conducted using a novel indoor multi-robot testbed with the ability to establish customizable scalar fields printed in greyscale on large sheets that are actively sensed by the robots to enable controlled experimental evaluation. Four different fields are used in this study in order to demonstrate the new capabilities of interest.

/pdf/experimental-implementation-and-verification-of-scalar-field-4ezwj4rxvr.pdf

Experimental Implementation and Verification of Scalar Field Ridge, Trench, and Saddle Point Maneuvers Using Multirobot Adaptive Navigation

Swarm control strategies allow for decentralized control of many simple robots to perform collective behaviors based on local interactions. Despite the broad applications of swarm robotics, there lacks a generalized and unified set of control equations for swarms. First, this work presents a novel mathematical control law for decentralized velocity control of planar, homogeneous swarms called reactive particle swarms (RPS). The architecture unifies the development of new RPS behaviors. The weighted summation of simple base behaviors and external command inputs form complex composite behaviors. Second, a series of simulated and on-hardware case studies demonstrate the utility and flexibility of the architecture. Third, this work establishes a library of verified RPS base behaviors. 

Unifying Control Architecture for Reactive Particle Swarms

Conventional navigation typically focuses on guiding a vehicle along a pre-defined path or through a series of waypoints. In contrast, Adaptive Navigation techniques determine how to move based on environmental measurements collected during the navigation process. The most studied version of this is navigation through a scalar field, where a single point measurement such as temperature is associated with each point in the field, in order to find the location of minimum or maximum value. Many proposed adaptive navigation techniques use multiple robots, exploiting their spatial distribution in order to improve the navigation process. The potential of such methods is compelling, enabling navigation to/along a variety of critical field features relevant to a wide range of applications, and doing so more efficiently than conventional navigation techniques. Unfortunately, most of these methods have not advanced beyond the simulation phase given the challenge of having access to a convenient experimental testbed that can support maturation of these techniques and their experimental verification. In addition to supporting multirobot operation, such a testbed should allow the scalar field of interest to be explicitly specified and customized in order to evaluate the navigation task of interest. To address this challenge, our team has developed a low-cost indoor multirobot adaptive navigation testbed with several intriguing features; these include a suite of 12 small omnidrive robots tracked via an optical tracking system, on-board sensors that measure the reflectivity of a printed mat on which the robots drive and which graphically represents a scalar field of interest, and a virtual reality visualization system for graphically presenting complex scalar fields and navigation performance. This paper reviews adaptive navigation, summarizes challenges in establishing experimental testbeds, and presents the design and functionality of our testbed.

A low-cost indoor testbed for multirobot adaptive navigation research

Adaptive navigation is the process of modifying a vehicle's direction or path based on measurements taken while moving. Significant work in this area has been performed for scalar fields, in which a single measurement such as temperature or the concentration level of a pollutant is associated with every point in the field; scalar field capabilities that have been developed include control strategies for finding local extrema, moving along contour lines, and navigating along ridges/trenches within a field. One can imagine the ability to perform similar maneuvers in flow fields, in which a vector quantity such as fluid flow is associated with each point in the field. In such a field, various applications could benefit from the ability to navigate to/along vector field primitives such as sources, sinks, vortices, uniform fileds, and combinations of them. In this paper, we propose several simple control strategies for a multirobot formation to navigate to/along such features. Each technique is explained, and we describe how each can be implemented in a multilayer control system in which adaptive navigation commands are issued to a multirobot formation control layer, which in turn issues directives to individual robots. Simple simulation case studies are used to demonstrate the behavior of each control technique. Finally, we also describe initial work in preparing for experimental demonstration of these techniques using a cluster of automated surface vessels to demonstrate the techniques in the field.

Initial Study of Multirobot Adaptive Navigation for Exploring Environmental Vector Fields

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A low-cost indoor testbed for multirobot adaptive navigation research

Initial Study of Multirobot Adaptive Navigation for Exploring Environmental Vector Fields