Probabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents

We present a novel method for guiding a large-scale swarm of autonomous agents into a desired formation shape in a distributed and scalable manner. Our Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) algorithm adopts an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled. Each agent determines its bin transition probabilities using a time-inhomogeneous Markov chain. These time-varying Markov matrices are constructed by each agent in real-time using the feedback from the current swarm distribution, which is estimated in a distributed manner. The PSG-IMC algorithm minimizes the expected cost of the transitions per time instant, required to achieve and maintain the desired formation shape, even when agents are added to or removed from the swarm. The algorithm scales well with a large number of agents and complex formation shapes, and can also be adapted for area exploration applications. We demonstrate the effectiveness of this proposed swarm guidance algorithm by using results of numerical simulations and hardware experiments with multiple quadrotors.Comment: Submitted to IEEE Transactions on Robotic

Micro guidance and control synthesis: New components, architectures, and capabilities

New GN&C (guidance, navigation and control) system capabilities are shown to arise from component innovations that involve the synergistic use of microminiature sensors and actuators, microelectronics, and fiber optics. Micro-GN&C system and component concepts are defined that include micro-actuated adaptive optics, micromachined inertial sensors, fiber-optic data nets and light-power transmission, and VLSI microcomputers. The thesis is advanced that these micro-miniaturization products are capable of having a revolutionary impact on space missions and systems, and that GN&C is the pathfinder micro-technology application that can bring that about

Attitude Estimation in Fractionated Spacecraft Cluster Systems

An attitude estimation was examined in fractioned free-flying spacecraft. Instead of a single, monolithic spacecraft, a fractionated free-flying spacecraft uses multiple spacecraft modules. These modules are connected only through wireless communication links and, potentially, wireless power links. The key advantage of this concept is the ability to respond to uncertainty. For example, if a single spacecraft module in the cluster fails, a new one can be launched at a lower cost and risk than would be incurred with onorbit servicing or replacement of the monolithic spacecraft. In order to create such a system, however, it is essential to know what the navigation capabilities of the fractionated system are as a function of the capabilities of the individual modules, and to have an algorithm that can perform estimation of the attitudes and relative positions of the modules with fractionated sensing capabilities. Looking specifically at fractionated attitude estimation with startrackers and optical relative attitude sensors, a set of mathematical tools has been developed that specify the set of sensors necessary to ensure that the attitude of the entire cluster ( cluster attitude ) can be observed. Also developed was a navigation filter that can estimate the cluster attitude if these conditions are satisfied. Each module in the cluster may have either a startracker, a relative attitude sensor, or both. An extended Kalman filter can be used to estimate the attitude of all modules. A range of estimation performances can be achieved depending on the sensors used and the topology of the sensing network

Adaptive Deadband Synchronization for a Spacecraft Formation

A paper discusses general problems in estimation and control of the states (positions, attitudes, and velocities) of spacecraft flying in formation, then addresses the particular formation-flying-control problem of synchronization of deadbands. The paper presents a deadband synchronization algorithm for the case in which the spacecraft are equipped with pulse-width-modulated thrusters for maintaining their required states. The algorithm synchronizes thruster-firing times across all six degrees of freedom of all the spacecraft. The algorithm is scalable, inherently adapts to disturbances, and does not require knowledge of spacecraft masses and disturbance forces. In this algorithm, one degree of freedom of one spacecraft is designated the leader, and all other degrees of freedom of all spacecraft as followers. The Cassini adaptive optimum deadband drift controller is the subalgorithm for control in each degree of freedom, and the adaptation is run until each spacecraft achieves a specified drift period. The adaptation is critical because a different disturbance affects each different degree of freedom. Then the leader communicates its thruster-firing starting times to the followers. Then, for each follower, a deadband-synchronization subalgorithm determines the shift needed to synchronize its drift period with that of the leader

Micro guidance and control technology overview

This paper gives an overview of micro-guidance and control technologies and in the process previews of the technology/user and systems issues presented in the guidance and control session at the workshop. We first present a discussion of the advantages of using micro-guidance and control components and then detail six micro-guidance and control thrusts that could have a revolutionary impact on space missions and systems. Specific technologies emerging in the micro-guidance and control field will be examined. These technologies fall into two broad categories: micro-attitude determination (inertial and celestial) and micro-actuation, control and sensing. Finally, the scope of the workshop's guidance and control panel are presented

Formation-Initialization Algorithm for N Spacecraft

A paper presents an algorithm to initialize a formation of N distributed spacecraft in deep space

Real-Time Optimal Control and Target Assignment for Autonomous In-Orbit Satellite Assembly from a Modular Heterogeneous Swarm

This paper presents a decentralized optimal guidance and control scheme to combine a heterogeneous swarm of component satellites, rods and connectors, into a large satellite structure. By expanding prior work on a decentralized auction algorithm with model predictive control using sequential convex programming (MPC-SCP) to allow for the limited type heterogeneity and docking ability required for in-orbit assembly. The assignment is performed using a distributed auction with a variable number of targets and strict bonding rules to address the heterogeneity. MPC-SCP is used to generate the collision-free trajectories, with modifications to the constraints to allow docking

Decentralized Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming

This paper presents a decentralized, model predictive control algorithm for the reconfiguration of swarms of spacecraft composed of hundreds to thousands of agents with limited capabilities. In our prior work, sequential convex programming has been used to determine collision-free, fuel-efficient trajectories for the reconfiguration of spacecraft swarms. This paper uses a model predictive control approach to implement the sequential convex programming algorithm in real-time. By updating the optimal trajectories during the reconfiguration, the model predictive control algorithm results in decentralized computations and communication between neighboring spacecraft only. Additionally, model predictive control reduces the horizon of the convex optimizations, which reduces the run time of the algorithm

Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

This paper presents a novel method for expanding the use of sequential convex programming (SCP) to the domain of optimal guidance and control problems with nonlinear dynamics constraints. SCP is a useful tool in obtaining real-time solutions to direct optimal control, but it is unable to adequately model nonlinear dynamics due to the linearization and discretization required. As nonlinear program solvers are not yet functioning in real-time, a tool is needed to bridge the gap between satisfying the nonlinear dynamics and completing execution fast enough to be useful. Two methods are proposed, sequential convex programming with nonlinear dynamics correction (SCPn) and modified SCPn (M-SCPn), which mixes SCP and SCPn to reduce runtime and improve algorithmic robustness. Both methods are proven to generate optimal state and control trajectories that satisfy the nonlinear dynamics. Simulations are presented to validate the efficacy of the methods as compared to SCP