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

Lecture Notes in Economics and Mathematical Systems

Aspects of the invention relate generally to autonomous vehicles. Specifically, the features described may be used alone or in combination in order to improve the safety, use, driver experience, and performance of these vehicles.

System and method for predicting behaviors of detected objects

Decomposition is a well-known strategy in traditional multiobjective optimization. However, the decomposition strategy was not widely employed in evolutionary multiobjective optimization until Zhang and Li proposed multiobjective evolutionary algorithm based on decomposition (MOEA/D) in 2007. MOEA/D proposed by Zhang and Li decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them in a collaborative manner using an evolutionary algorithm (EA). Each subproblem is optimized by utilizing the information mainly from its several neighboring subproblems. Since the proposition of MOEA/D in 2007, decomposition-based MOEAs have attracted significant attention from the researchers. Investigations have been undertaken in several directions, including development of novel weight vector generation methods, use of new decomposition approaches, efficient allocation of computational resources, modifications in the reproduction operation, mating selection and replacement mechanism, hybridizing decomposition- and dominance-based approaches, etc. Furthermore, several attempts have been made at extending the decomposition-based framework to constrained multiobjective optimization, many-objective optimization, and incorporate the preference of decision makers. Additionally, there have been many attempts at application of decomposition-based MOEAs to solve complex real-world optimization problems. This paper presents a comprehensive survey of the decomposition-based MOEAs proposed in the last decade.

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition

Interest in control of multiple autonomous vehicles continues to grow for applications such as weather monitoring, geographical mapping fauna surveys, and extra-terrestrial exploration. The task of persistent surveillance is of particular significance in that the target area needs to be continuously surveyed, minimizing the time between visitations to the same region. This distinction from one-time coverage does not allow a straightforward application of most exploration techniques to the problem, though ideas from these methods can still be used. The aerial vehicle dynamic and endurance constraints add additional complexity to the autonomous control problem, whereas stochastic environments and vehicle failures introduce uncertainty. In this work, we investigate techniques for high-level control, that are scalable, reliable, efficient, and robust to problem dynamics. Next, we suggest a modification to the control policy to account for aircraft dynamic constraints. We also devise a health monitoring policy and a control policy modification to improve performance under endurance constraints. The Vehicle Swarm Technology Laboratory-a hardware testbed developed at Boeing Research and Technology, Seattle, WA, for evaluating a swarm of unmanned air vehicles-is then described, and these control policies are tested in a realistic scenario.

Control of Multiple UAVs for Persistent Surveillance: Algorithm and Flight Test Results

Systems and methods for development testing of vehicles and components are disclosed. In one embodiment, a system includes a position reference system and a command and control architecture. The position reference system is configured to repetitively measure one or more position and motion characteristics of one or more vehicles operating within a control volume. The command and control architecture is configured to receive the repetitively measured characteristics from the position reference system, and to determine corresponding control signals based thereon. The control signals are then transmitted to the one or more vehicles to control at least one of position, movement, and stabilization of the one or more vehicles in a closed-loop feedback manner. The system may further include a health monitoring component configured to monitor health conditions of the one or more vehicles, the control signals being determined at least in part on the health conditions.

Autonomous vehicle rapid development testbed systems and methods

Aerospace systems continue to grow in complexity while demanding optimal performance. This requires the systems to be both designed and controlled optimally. Aerospace systems are also typically comprised of many interacting components, some of which may have competing requirements. The optimization approaches used for aerospace systems usually require centralized coordination and synchronous updates. In addition, while the approaches treat the large numbers of variables, they may not take advantage of the fact that the coupling may only be between a relatively small number of the variables. Distributed optimization algorithms, such as the approach based upon collectives presented in this paper, attempt to exploit this aspect. A collective is deflned as a multi-agent system where each agent is self-interested and capable of learning. Furthermore, a collective has a specifled system objective which rates the performance of the joint actions of the agents. Although collectives have been used for a number of distributed optimization problems in computer science, recent developments based upon Probability Collectives (PC) theory enhance their applicability to discrete, continuous, mixed, and constrained optimization problems. This paper will present the theoretical underpinnings of the approach for these various problem domains. Several example problems are used to illustrate the technique and to provide insight into its behavior. The examples include discrete, constrained, and continuous problems. In particular a constrained discrete structural optimization and a continuous trajectory optimization illustrate the breadth of the collectives approach.

https://ti.arc.nasa.gov/m/profile/dhw/papers/4.pdf

Discrete, Continuous, and Constrained Optimization Using Collectives

The increasing complexity of aerospace systems demands new approaches for their design and control. Approaches are required to address the trend towards aerospace systems comprised of a large number of inherently distributed and highly nonlinear components with complex and sometimes competing interactions. This work introduces collectives to address these challenges. Although collectives have been used for distributed optimization problems in computer science, recent developments based upon Probability Collectives (PC) theory enhance their applicability to discrete, continuous, mixed, and constrained optimization problems. Further, they are naturally applied to distributed systems and those involving uncertainty, such as control in the presence of noise and disturbances. This work describes collectives theory and its implementation, including its connections to multi-agent systems, machine learning, statistics, and gradient-based optimization. 
To demonstrate the approach, two experiments were developed. These experiments built upon recent advances in actuator technology that resulted in small, simple flow control devices. Miniature-Trailing Edge Effectors (MiTE), consisting of a small, 1-5% chord, moveable surface mounted at the wing trailing edge, are used for the experiments. The high bandwidth, distributed placement, and good control authority make these ideal candidates for rigid and flexible mode control of flight vehicles. This is demonstrated in two experiments: flutter suppression of a flexible wing, and flight control of a remotely piloted aircraft. The first experiment successfully increased the flutter speed by over 25%. The second experiment included a novel distributed flight control system based upon the MiTEs that includes distributed sensing, logic, and actuation. Flight tests validated the control capability of the MiTEs and the associated flight control architecture. The collectives approach was used to design controllers for the distributed flight control system. These controllers increased the flight vehicle stability by 85% and alleviated gust loads by 78%, when compared with open loop. 
This work demonstrates the use of collectives for the range of optimization problems of interest in aerospace systems, provides the mathematical foundations and implementation details, and focuses on applications to nonlinear, robust, distributed control. The two successful experiments validate both the collectives approach and the use of MiTEs for the control of flight vehicles.

Distributed optimization and flight control using collectives

Product distribution theory is a new collective intelligence-based framework for analyzing and controlling distributed systems. Its usefulness in distributed stochastic optimization is illustrated here through an airline fleet assignment problem. This problem involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of linear and non-linear constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of this new stochastic optimization algorithm to a non-linear objective cold start fleet assignment problem. Results show that the optimizer can successfully solve such highly-constrained problems (130 variables, 184 constraints).

/pdf/fleet-assignment-using-collective-intelligence-36ukejplem.pdf

Fleet Assignment Using Collective Intelligence

Often adaptive, distributed control can be viewed as an iterated game between independent players. The coupling between the players' mixed strategies, arising as the system evolves from one instant to the next, is determined by the system designer. Information theory tells us that the most likely joint strategy of the players, given a value of the expectation of the overall control objective function, is the minimizer of a Lagrangian function of the joint strategy. So the goal of the system designer is to speed evolution of the joint strategy to that Lagrangian minimizing point, lower the expectated value of the control objective function, and repeat. Here we elaborate the theory of algorithms that do this using local descent procedures, and that thereby achieve efficient, adaptive, distributed control.

Stefan R. Bieniawski

Papers

Autonomous vehicle rapid development testbed systems and methods

Discrete, Continuous, and Constrained Optimization Using Collectives

Distributed optimization and flight control using collectives

Fleet Assignment Using Collective Intelligence

Distributed Control by Lagrangian Steepest Descent