The purpose of this article is to provide a tutorial overview of information consensus in multivehicle cooperative control. Theoretical results regarding consensus-seeking under both time invariant and dynamically changing communication topologies are summarized. Several specific applications of consensus algorithms to multivehicle coordination are described

/pdf/information-consensus-in-multivehicle-cooperative-control-3bx4dd2yti.pdf

Information consensus in multivehicle cooperative control

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

A survey of multi-agent formation control

As a distributed solution to multi-agent coordination, consensus or agreement problems have been studied extensively in the literature. This paper provides a survey of consensus problems in multi-agent cooperative control with the goal of promoting research in this area. Theoretical results regarding consensus seeking under both time-invariant and dynamically changing information exchange topologies are summarized. Applications of consensus protocols to multiagent coordination are investigated. Future research directions and open problems are also proposed.

/pdf/a-survey-of-consensus-problems-in-multi-agent-coordination-4ofvnak3df.pdf

A survey of consensus problems in multi-agent coordination

This self-contained introduction to the distributed control of robotic networks offers a distinctive blend of computer science and control theory. The book presents a broad set of tools for understanding coordination algorithms, determining their correctness, and assessing their complexity; and it analyzes various cooperative strategies for tasks such as consensus, rendezvous, connectivity maintenance, deployment, and boundary estimation. The unifying theme is a formal model for robotic networks that explicitly incorporates their communication, sensing, control, and processing capabilities--a model that in turn leads to a common formal language to describe and analyze coordination algorithms.Written for first- and second-year graduate students in control and robotics, the book will also be useful to researchers in control theory, robotics, distributed algorithms, and automata theory. The book provides explanations of the basic concepts and main results, as well as numerous examples and exercises.Self-contained exposition of graph-theoretic concepts, distributed algorithms, and complexity measures for processor networks with fixed interconnection topology and for robotic networks with position-dependent interconnection topology Detailed treatment of averaging and consensus algorithms interpreted as linear iterations on synchronous networks Introduction of geometric notions such as partitions, proximity graphs, and multicenter functions Detailed treatment of motion coordination algorithms for deployment, rendezvous, connectivity maintenance, and boundary estimation

/pdf/distributed-control-of-robotic-networks-a-mathematical-1q6nfcvi2l.pdf

Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms

Inspired by the so-called "bugs" problem from mathematics, we study the geometric formations of multivehicle systems under cyclic pursuit. First, we introduce the notion of cyclic pursuit by examining a system of identical linear agents in the plane. This idea is then extended to a system of wheeled vehicles, each subject to a single nonholonomic constraint (i.e., unicycles), which is the principal focus of this paper. The pursuit framework is particularly simple in that the n identical vehicles are ordered such that vehicle i pursues vehicle i+1 modulo n. In this paper, we assume each vehicle has the same constant forward speed. We show that the system's equilibrium formations are generalized regular polygons and it is exposed how the multivehicle system's global behavior can be shaped through appropriate controller gain assignments. We then study the local stability of these equilibrium polygons, revealing which formations are stable and which are not.

/pdf/formations-of-vehicles-in-cyclic-pursuit-2svomkue8h.pdf

Formations of vehicles in cyclic pursuit

Pursuit formations of unicycles

The present invention provides a system for coordinating multiple vehicles in a passageway environment (e.g., in underground mines). The system includes methods and apparatus for determining the global position and orientation of a vehicle in said passageway environment, and methods for planning routes and monitoring the travels of multiple vehicles in said passageway environment. A global position and orientation estimation system employs one or more odometric sensors and one or more range sensing devices. It works in three basic steps. In the first step, it records and processes sensor data that is descriptive of the passageway environment by moving the system through said passageway environment. In the second step, it generates a globally consistent map of said passageway environment. Finally, real-time localization is provided by employing odometric sensors and range sensing devices to determine the system's global position and orientation with respect to said globally consistent map, both initially and as it travels through the passageway environment. A route planning method accepts higher-level goals for a set of multiple vehicles in said passageway environment and generates a route plan for each vehicle that minimizes the travel time for the group of vehicles, while at the same time avoiding collisions between vehicles. Route plans are sent to the vehicles for implementation and a monitoring method tracks the global positions and orientations of the vehicles and ensures that both safety and efficiency are maintained.

Traffic management system for a passageway environment

This paper describes the design, implementation, and field testing of an infrastructureless system for autonomous tramming (or hauling) of a center-articulated underground mining vehicle. Such vehicles are ubiquitous in underground mining, and effective automation of their tramming function has been a sought-after technology for more than a decade. This paper reports on the successful development of a fast, reliable, and robust “autotramming” technology that does not require the installation of fixed infrastructure. Included are descriptions of the chosen control architecture, map-based localization technique, and the results of integration and field testing. © 2008 Wiley Periodicals, Inc.

Autonomous underground tramming for center-articulated vehicles

The present invention provides a guidance, navigation, and control method and system for an underground mining vehicle that allow said vehicle to be taught a route by a human operator and then have it automatically drive the route with no human intervention. The method works in three steps: teaching, route profiling, and playback. In the teaching step the vehicle is manually driven by a operator (or using tele-operation whereby the operator views a screen displaying live views from vehicle-mounted cameras and using remote controls) along a route which can consist of an arbitrary sequence of maneuvers including tramming forwards, switching directions, tramming backwards, turning, or pausing movement. During this phase raw data from vehicle-mounted sensors including odometric sensors and rangefinders are logged to a file throughout teaching for later processing. During the (offline) route profiling step, the raw data in the log file are processed into a route profile including a vehicle path, a sequence of local metric submaps located along the path, and a profile of desired speed as a function of distance along the path. During the playback step, the vehicle automatically repeats the route that was taught during the teaching phase, as represented by the route profile. This is accomplished by first determining where the vehicle is on the route using a localization method which uses the odometric and laser rangefinder sensors and the local metric maps to determine the vehicle location. A steering control method adjusts the vehicle's steering to ensure it tracks the intended path. A drive control method adjusts the vehicle's speed accordingly and safety method ensures the vehicle stops in the event that an obstruction is on the vehicle's intended path.

Joshua A. Marshall

Papers

Formations of vehicles in cyclic pursuit

Pursuit formations of unicycles

Traffic management system for a passageway environment

Autonomous underground tramming for center-articulated vehicles

Guidance, navigation, and control system for a vehicle