Decentralized abstractions and timed constrained planning of a general class of coupled multi-agent systems

Abstract

This paper presents a fully automated procedure for controller synthesis for a general class of multi-agent systems under coupling constraints. Each agent is modeled with dynamics consisting of two terms: the first one models the coupling constraints and the other one is an additional bounded control input. We aim to design these inputs so that each agent meets an individual high-level specification given as a Metric Interval Temporal Logic (MITL). Furthermore, the connectivity of the initially connected agents, is required to be maintained. First, assuming a polyhedral partition of the workspace, a novel decentralized abstraction that provides controllers for each agent that guarantee the transition between different regions is designed. The controllers are the solution of a Robust Optimal Control Problem (ROCP) for each agent. Second, by utilizing techniques from formal verification, an algorithm that computes the individual runs which provably satisfy the highlevel tasks is provided. Finally, simulation results conducted in MATLAB environment verify the performance of the proposed framework.Index Terms-multi-agent systems, cooperative control, hybrid systems. Alexandros Nikou, Christos Verginis and Dimos V. Dimarogonas are with the ACCESS Linnaeus Center, School of Electrical Engineering, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden and with the KTH Center for Autonomous Systems. Email: {anikou, cverginis, dimos}@kth.se. Shahab Heshmati-alamdari is with the Control Systems Lab,

Design and Experimental Validation of Tube-based MPC for Timed-constrained Robot Planning

“…The first constraint denotes the fact that the robot should not intersect with any other RoI other than R i ,R j ; the second one, denotes the fact that the robot needs to remain in the workspace for all times. Define by q = e −ê the deviation between the real state of the system (4) and the nominal state of the system (5) with q(0) = e(0) −ê(0) = 0. The dynamics of the state q are given by:…”

Robust decentralised navigation of multi-agent systems with collision avoidance and connectivity maintenance using model predictive controllers

“…With this result established, we turn back to the remaining terms found in (I2) and, in particular, we focus on the integral: tk+1+Tp tk+Tp F i e 1,i (s), u 1,i (s) ds. We discern that the range of this integral has a length equal to the length of the interval where (14) of Assumption 6 holds. Integrating (14) over the interval [t k + T p , t k+1 + T p ], for the controls and states applicable in it we get:…”

“…We discern that the range of this integral has a length equal to the length of the interval where (14) of Assumption 6 holds. Integrating (14) over the interval [t k + T p , t k+1 + T p ], for the controls and states applicable in it we get:…”

“…The main focus of multi-agent systems is the design of decentralized control protocols in order to achieve global tasks, such as consensus [1][2][3][4], in which all the agents are required to converge to a specific point and formation [5][6][7][8][9][10][11], in which all the agents aim to form a predefined geometrical shape. At the same time, the agents might need to fulfill certain transient properties, such as network connectivity [12][13][14] and/or collision avoidance [15][16][17]. In parallel, another topic of research is multiagent navigation in both the robotics and the control communities, due to the need for autonomous control of multiple robotic agents in the same workspace.…”

Results from the Robocademy ITN: Autonomy, Disturbance Rejection and Perception for Advanced Marine Robotics

“…For more details on NMPC, please refer toAllgwer et al (2004);Eqtami et al (2013a,b);Logothetis et al (2018);Nikou et al ( , 2018 and the paper cited and papers quoted therein.…”