Cooperative collision avoidance control and coordination for multiagent Lagrangian systems with disturbances

Multi-agent systems like a network of autonomous robots, have tremendous potential in many military and civilian applications. But, even viewed as a pure academic problem, designing controllers for such complex systems is a matter of much interest. Controller design for multi-agent system might focus on achieving several objectives, such as formation control, coverage control, consensus, target capture, pursuit evasion etc., while all at the same time aiming to be optimal in some sense, or following certain constraints imposed by the environment or communication limitations. Whatever is the objective, we always want to have a safety guarantee for the agents; the agents should avoid collisions with themselves and any static obstacles, while performing an objective. This thesis studies one such controller, which guarantees collision avoidance among the agents, in presence of bounded disturbances, while the agents carry out a coordination objective. The agents are assumed to follow a Lagrangian dynamics. The collision avoidance controller takes up the second part of the thesis. In the first part of this thesis, a particular Lagrangian system, the Raven II surgical robot, is studied in with the aim of highlighting the process of modelling and identifying such system. This is done for two reasons. One because Lagrangian dynamics is commonly used to model the agents in a multi-agent system. And second reason that motivates the modelling Raven II in part I, is to aid in future research direction pertaining to the control of Raven II

A Distributed Pipeline for Scalable, Deconflicted Formation Flying

Reliance on external localization infrastructure and centralized coordination are main limiting factors for formation flying of vehicles in large numbers and in unprepared environments. While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability. To address these shortcomings, we present a unified pipeline with onboard localization and a distributed, collision-free motion planning strategy that scales to a large number of vehicles. Since distributed collision avoidance strategies are known to result in gridlock, we also present a decentralized task assignment solution to deconflict vehicles. We experimentally validate our pipeline in simulation and hardware. The results show that our approach for solving the optimization problem associated with motion planning gives solutions within seconds in cases where general purpose solvers fail due to high complexity. In addition, our lightweight assignment strategy leads to successful and quicker formation convergence in 96-100% of all trials, whereas indefinite gridlocks occur without it for 33-50% of trials. By enabling large-scale, deconflicted coordination, this pipeline should help pave the way for anytime, anywhere deployment of aerial swarms.Comment: 8 main pages, 1 additional page, accepted to RA-L and IROS'2

Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

This paper proposes a parallel optimization algorithm for cooperative automation of large-scale connected vehicles. The task of cooperative automation is formulated as a centralized optimization problem taking the whole decision space of all vehicles into account. Considering the uncertainty of the environment, the problem is solved in a receding horizon fashion. Then, we employ the alternating direction method of multipliers (ADMM) to solve the centralized optimization in a parallel way, which scales more favorably to large-scale instances. Also, Taylor series is used to linearize nonconvex constraints caused by coupling collision avoidance constraints among interactive vehicles. Simulations with two typical traffic scenes for multiple vehicles demonstrate the effectiveness and efficiency of our method

Safe Positively Invariant Sets for Spacecraft Obstacle Avoidance

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140643/1/1.g000115.pd

A Survey on Aerial Swarm Robotics

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