Multi-Agent Motion Planning and Object Transportation under High Level Goals

This paper presents a hybrid control framework for the motion planning of a multi-agent system including N robotic agents and M objects, under high level goals. In particular, we design control protocols that allow the transition of the agents as well as the transportation of the objects by the agents, among predefined regions of interest in the workspace. This allows us to abstract the coupled behavior of the agents and the objects as a finite transition system and to design a high-level multi-agent plan that satisfies the agents' and the objects' specifications, given as temporal logic formulas. Simulation results verify the proposed framework.Comment: To appear in the World Congress of the International Federation of Automatic Control (IFAC), Toulouse, France, July 201

Sampling-based Synthesis of Controllers for Multiple Agents under Signal Temporal Logic Specifications

openL’ampia applicazione dei robot nelle industrie e nella società ha portato alla necessità di prescrivere complessi compiti di alto livello ad agenti autonomi. Signal Temporal Logic (STL) è una logica temporale che consente di esprimere requisiti spazio-temporali e quantificare il livello di soddisfazione delle preferenze. Quando si pianifica considerando specifiche STL, la sfida principale è generare traiettorie che soddisfino le formule logiche e seguire le traiettorie così ottenute. Il progetto propone una soluzione per il problema di pianificazione del movimento di multipli agenti autonomi, soggetti a specifiche STL accoppiate. Partendo da uno scenario in cui sono coinvolti solo due agenti, un algoritmo basato sul campionamento, Coupled STL_RRT*, è progettato. L’approccio proposto, basato su RRT*, costruisce in modo distribuito due alberi nel dominio del tempo e dello stato accoppiati. Per ogni sistema dinamico, data una posizione iniziale, la strategia sviluppata trova la traiettoria probabilisticamente ottimale in termini di una funzione di costo che dipende dagli input di controllo richiesti. Prima di aggiungere nuovi stati all’albero corrispondente, l’algoritmo controlla se la formula logica non viene violata, assicurando quindi che la traiettoria finale, variabile nel tempo, soddisfi le specifiche spazio-temporali. La dinamica dell’agente autonomo è presa direttamente in considerazione e il concetto di raggiungibilità viene sfruttato per ottenere traiettorie ammissibili rispetto ai vincoli dinamici. L’algoritmo è quindi simulato, considerando un ambiente con ostacoli statici e diversi requisiti STL, specificati dall’utente. L’approccio viene poi esteso al caso di sistemi multi-agente con più di tre agenti. Come nel caso precedente, l’algoritmo costruisce un albero spazio-temporale per ciascun agente, assicurando che la traiettoria finale soddisfi i requisiti STL. La soluzione proposta è poi verificata in scenari simulati, considerando sistemi con 4 o 6 agenti.The wide application of robots in industries and society has brought the need to prescribe complex high-level tasks to autonomous agents. Signal Temporal Logic (STL) is a temporal logic that allows to express desired spatio-temporal requirements, while quantifying the satisfaction of the preferences. When planning under STL specifications, the main challenge is to generate trajectories that satisfy the logical formulas and to track those trajectories. The project proposes a solution for the motion planning problem of multiple autonomous agents, subject to coupled STL specifications. Starting from a scenario where only two agents are involved, a sampling-based algorithm, Coupled STL_RRT*, is designed. The proposed RRT*-based approach builds two trees in the coupled time and state domain in a distributed manner. For each dynamical system, given an initial position, the developed strategy finds a probabilistic optimal trajectory in terms of a cost function that depends on the required control inputs. Before adding new states to the corresponding tree, the algorithm checks if the logical formula is not violated, hence ensuring that the final time-varying trajectory satisfies the spatio-temporal specifications. The dynamics of the autonomous agent is directly taken into account and reachability is exploited to obtain a trajectory that is feasible with respect to the dynamic constraints. The algorithm is then simulated, considering an environment with static obstacles and different STL requirements, specified by the user. The approach is then extended to the case of multi-agent systems with more than three agents. As in the previous case, the algorithm builds a spatiotemporal tree for each agent, ensuring that the final trajectory satisfies the STL requirements. The proposed solution is then verified in simulated scenarios, considering 4-agents and 6-agents systems

Robust Decentralized Abstractions for Multiple Mobile Manipulators

This paper addresses the problem of decentralized abstractions for multiple mobile manipulators with 2nd order dynamics. In particular, we propose decentralized controllers for the navigation of each agent among predefined regions of interest in the workspace, while guaranteeing at the same time inter-agent collision avoidance and connectivity maintenance for a subset of initially connected agents. In that way, the motion of the coupled multi-agent system is abstracted into multiple finite transition systems for each agent, which are then suitable for the application of temporal logic-based high level plans. The proposed methodology is decentralized, since each agent uses local information based on limited sensing capabilities. Finally, simulation studies verify the validity of the approach.Comment: Accepted for publication in the IEEE Conference on Decision and Control, Melbourne, Australia, 201