Prescribed performance distance-based formation control of Multi-Agent Systems

This paper presents a novel control protocol for robust distance-based formation control with prescribed performance in which agents are subjected to unknown external disturbances. Connectivity maintenance and collision avoidance among neighboring agents are also handled by the appropriate design of certain performance bounds that constrain the inter-agent distance errors. As an extension to the proposed scheme, distance-based formation centroid maneuvering is also studied for disturbance-free agents, in which the formation centroid tracks a desired time-varying velocity. The proposed control laws are decentralized, in the sense that each agent employs local relative information regarding its neighbors to calculate its control signal. Therefore, the control scheme is implementable on the agents' local coordinate frames. Using rigid graph theory, input-to-state stability, and Lyapunov based analysis, the results are established for minimally and infinitesimally rigid formations in 2-D or 3-D space. Furthermore, it is argued that the proposed approach increases formation robustness against shape distortions and can prevent formation convergence to incorrect shapes, which is likely to happen in conventional distance-based formation control methods. Finally, extensive simulation studies clarify and verify the proposed approach.

Prescribed Performance Distance-Based Formation Control of Multi-Agent Systems (Extended Version).

This letter considers distributed flocking control on the Special Euclidean group for networked rigid bodies. The method captures the three flocking rules proposed by Reynolds: cohesion; alignment; and separation. The proposed controller is based only on relative pose (position and attitude) information with respect to neighboring rigid bodies so that it can be implemented in a fully distributed manner using only local sensors. The flocking algorithm is moreover based on pose synchronization methods for the cohesion/alignment rules and achieves safe separation distances through the application of control barrier functions. The control input for each rigid body is chosen by solving a distributed optimization problem with constraints for pose synchronization and collision avoidance. Here, the inherent conflict between cohesion and separation is explicitly handled by relaxing the position synchronization constraint. The effectiveness of the proposed flocking algorithm is demonstrated via simulation and hardware experiments.

Optimization-Based Distributed Flocking Control for Multiple Rigid Bodies

Unmanned Aerial Vehicles have undergone rapid developments in recent decades. This has made them very popular for various military and civilian applications allowing us to reach places that were previously hard to reach in addition to saving time and lives. A highly desirable direction when developing unmanned aerial vehicles is towards achieving fully autonomous missions and performing their dedicated tasks with minimum human interaction. Thus, this paper provides a survey of some of the recent developments in the field of unmanned aerial vehicles related to safe autonomous navigation, which is a very critical component in the whole system. A great part of this paper focus on advanced methods capable of producing three-dimensional avoidance maneuvers and safe trajectories. Research challenges related to unmanned aerial vehicle development are also highlighted.

Towards Fully Autonomous UAVs: A Survey.

This article studies the vision-based tracking control problem for a nonholonomic multirobot formation system with uncertain dynamic models and visibility constraints. A fixed onboard vision sensor that provides the relative distance and bearing angle is subject to limited range and angle of view due to limited sensing capability. The constraint resulting from collision avoidance is also taken into account for safe operations of the formation system. Furthermore, the preselected specifications on transient and steady-state performance are provided by considering the time-varying and asymmetric constraint requirements on formation tracking errors for each robot. To address the constraint problems, we incorporate a novel barrier Lyapunov function into controller design and analysis. Based on the recursive adaptive backstepping procedure and neural-network approximation, we develop a vision-based formation tracking control protocol such that formation tracking errors can converge into a small neighborhood of the origin in finite time while meeting the requirements of visibility and performance constraints. The proposed protocol is decentralized in the sense that the control action on each robot only depends on the local relative information, without the need for explicit network communication. Moreover, the control protocol could extend to an unconstrained multirobot system. Both simulation and experimental results show the effectiveness of the control protocol. 

Adaptive Finite-Time Tracking Control of Nonholonomic Multirobot Formation Systems With Limited Field-of-View Sensors

In this brief, we present solutions to the flocking and target interception problems of multiple nonholonomic unicycle-type robots using the distance-based framework. The control laws are designed at the kinematic level and are based on the rigidity properties of the graph modeling the sensing/communication interactions among the robots. An input transformation is used to facilitate the control design by converting the nonholonomic model into the single-integrator-like equation. We assume only a subset of the robots knows the desired, time-varying flocking velocity, or the target’s motion. The resulting control schemes include distributed, variable structure observers to estimate the unknown signals. Our stability analyses prove convergence to the desired formation while tracking the flocking velocity or the target motion. The results are supported by experiments.

Victor Fernandez-Kim

Papers

Flocking and Target Interception Control for Formations of Nonholonomic Kinematic Agents

Switching formation shape control with distance + area/angle feedback