A survey of multi-agent formation control

Formation flying is defined as a set of more than one spacecraft whose states are coupled through a common control law. This paper provides a comprehensive survey of spacecraft formation flying control (FFC), which encompasses design techniques and stability results for these coupled-state control laws. We divide the FFC literature into five FFC architectures: (i) multiple-input multiple-output, in which the formation is treated as a single multiple-input, multiple-output plant, (ii) leader/follower, in which individual spacecraft controllers are connected hierarchically, (iii) virtual structure, in which spacecraft are treated as rigid bodies embedded in an overall virtual rigid body, (iv) cyclic, in which individual spacecraft controllers are connected non-hierarchically, and (v) behavioral, in which multiple controllers for achieving different (and possibly competing) objectives are combined. This survey significantly extends an overview of the FFC literature provided by Lawton, which discussed the L/F, virtual structure and behavioral architectures. We also include a brief history of the formation flying literature, and discuss connections between spacecraft FFC and other multi-vehicle control problems in the robotics and automated highway system literatures.

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A survey of spacecraft formation flying guidance and control. Part II: control

In this paper, we presented a review on the current control issues and strategies on a group of unmanned autonomous vehicles/robots formation Formation control has broad applications and becomes an active research topic in the recent years In this paper, we attempt to review the key issues in formation control with a focus on the main control strategies for formation control under different kinds of scenarios Then, we point out some important open questions and the possible future research directions on formation control This paper contributes with a new and interesting consideration on formation control and its application in distributed parameter systems We pointed out that formation control should be classified as formation regulation control and formation tracking control, similar to regulator and tracker in conventional control

Formation control: a review and a new consideration

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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A Survey on Aerial Swarm Robotics

To increase the science return of future missions to Mars and to enable sample return missions, the accuracy with which a lander can be deliverer to the Martian surface must be improved by orders of magnitude. The prior work developed a convex-optimization-based minimum-fuel powered-descent guidance algorithm. In this paper, this convex-optimization-based approach is extended to handle the case when no feasible trajectory to the target exists. In this case, the objective is to generate the minimum-landing-error trajectory, which is the trajectory that minimizes the distance to the prescribed target while using the available fuel optimally. This problem is inherently a nonconvex optimal control problem due to a nonzero lower bound on the magnitude of the feasible thrust vector. It is first proven that an optimal solution of a convex relaxation of the problem is also optimal for the original nonconvex problem, which is referred to as a lossless convexification of the original nonconvex problem. Then it is shown that the minimum-landing-error trajectory generation problem can be posed as a convex optimization problem and solved to global optimality with known bounds on convergence time. This makes the approach amenable to onboard implementation for real-time applications.

Minimum-Landing-Error Powered-Descent Guidance for Mars Landing Using Convex Optimization

This paper provides a comprehensive survey of spacecraft formation flying guidance (FTG). Here by the term guidance we mean both path planning and optimal, open loop control design.

A survey of spacecraft formation flying guidance and control (part 1): guidance

This paper presents a new onboard-implementable, real-time convex optimization-based powered-descent guidance algorithm for planetary pinpoint landing. Earlier work provided the theoretical basis of convexification, the equivalent representation of the fuel-optimal pinpoint landing trajectory optimization problem with nonconvex control constraints as a convex optimization problem. Once the trajectory optimization problem is convexified, interior-point method algorithms can be used to solve the problem to global optimality. Though having this guarantee of convergence motivated earlier convexification results, there were no real-time interior point method algorithms available for the computation of optimal trajectories on flight computers. This paper presents the first such algorithm developed for onboard use and flight-tested on a terrestrial rocket with the NASA Jet Propulsion Laboratory and the NASA Flight Opportunities Program in 2013. First, earlier convexification results are summarized and the result...

Customized Real-Time Interior-Point Methods for Onboard Powered-Descent Guidance

Onboard, fuel-optimal, constrained powered-descent guidance based on the theory of lossless convexification has been implemented as the Guidance for Fuel-Optimal Large Diverts (G-FOLD) algorithm. Here, “guidance” means generating feedforward reference trajectories for control systems. This paper presents terrestrial flight-test demonstrations of large diverts planned by G-FOLD onboard a vertical-takeoff/vertical-landing rocket. The G-FOLD parser, which transforms the guidance problem into a second-order cone program and so encodes the divert constraints, is described at an engineering level, including new and modified constraints incorporated for these flight tests. Several practical issues, such as discretization effects, are addressed, and the flight-test architecture is presented. A total of eight flight tests were performed. In the first three, the rocket executed diverts of increasing size preplanned by G-FOLD on the ground. Then G-FOLD was demonstrated running onboard five times. Three qualitatively...

Daniel P. Scharf

Papers

A survey of spacecraft formation flying guidance and control. Part II: control

Minimum-Landing-Error Powered-Descent Guidance for Mars Landing Using Convex Optimization

A survey of spacecraft formation flying guidance and control (part 1): guidance

Customized Real-Time Interior-Point Methods for Onboard Powered-Descent Guidance

Implementation and Experimental Demonstration of Onboard Powered-Descent Guidance