In the reporting period, we had substantial progress on O1. We employ control barrier functions (CBF) for ensuring the satisfaction of transient and spatiotemporal constraints. In particular, we propose a method for their distributed implementation for an STL fragment, we consider their application to leader-follower networks and propose nonsmooth CBFs for decision making. Followed by we devised a distributed control scheme to effectively manage multiple constraints. Besides, we developed a novel PPC scheme for formation control with scaling and orientation adjustment, and a PPC scheme for leader-follower networks subject to a class of STL constraints. Moreover, we proposed model predictive control (MPC) to account for STL and coupled state constraints. Notably, we introduced corridor MPC, which was specifically designed to inspect satellite structures.
Building up on the results from O1, we derived results on task decomposition and planning for leader-follower (O2). We proposed for the decomposition of a global STL formula for a leader-follower network a convex optimization problem to compute local subtasks. We also developed a sampling-based algorithm for the generation of trajectories satisfying an STL task that couples several cooperating agents. Moreover, we proposed solutions to the problem of leader-follower multi-agent task scheduling under time constraints. We also proposed a decentralized control approach to facilitate implicit coordination among vehicles. By employing distributed model predictive control (MPC) for STL tasks, we provided a practical approach to overcome the exponential complexity associated with such tasks. Moreover, we established a method for converting STL formulas into assume-guarantee contracts, thereby enabling the satisfaction of global requirements through local contracts.
For O3, we obtained results on the hybrid control of leader-follower multi-agent systems (MAS). We propose a control scheme to robustly guarantee the fixed-time satisfaction of dependent STL tasks for coupled MAS in a least violating way in the presence of undesired violation effects of neighboring agents. Particularly, the robust performance of the task satisfactions can be adjusted in a user-specified way. Furthermore, we successfully synthesized controllers for nested STL tasks, accommodating general nonlinear systems. Additionally, we introduced a funnel-based control methodology specifically designed to address conflicting hard and soft STL constraints.
Towards O4, we derived necessary and sufficient conditions on the leader-follower graph topology under which the target formation together with the prescribed performance guarantees can be fulfilled.
For O5, we demonstrate the effectiveness and practical relevance of our developed methods by applying them to platooning (intelligent transportation) and collaborative manipulation. For the platooning application, we expressed the splitting, merging and distance maintenance tasks in one STL-task and designed a feedback controller using CBFs for systems with limited actuation capabilities. Additionally, we introduced methods for coordinating multiple platoons and robustly managing platoons under STL constraints. Moreover, we designed a low-level controller for a manipulator system handling an object under given STL specifications and unknown dynamics, which we experimentally verified using two robotic arms handling an object. We also investigated human-robot collaboration where a human takes the role of the leader agent and the robot the role of the follower agent.
