Decentralized Multi-Agent Planning for Multirotors:a Fully online and Communication Latency Robust Approach

There are many industrial, commercial and social applications for multi-agent planning for multirotors such as autonomous agriculture, infrastructure inspection and search and rescue. Thus, improving on the state-of-the-art of multi-agent planning to make it a viable real-world solution is of great benefit. In this work, we propose a new method for multi-agent planning in a static environment that improves our previous work by making it fully online as well as robust to communication latency. The proposed framework generates a global path and a Safe Corridor to avoid static obstacles in an online fashion (generated offline in our previous work). It then generates a time-aware Safe Corridor which takes into account the future positions of other agents to avoid intra-agent collisions. The time-aware Safe Corridor is given with a local reference trajectory to an MIQP (Mixed-Integer Quadratic Problem)/MPC (Model Predictive Control) solver that outputs a safe and optimal trajectory. The planning frequency is adapted to account for communication delays. The proposed method is fully online, real-time, decentralized, and synchronous. It is compared to 3 recent state-of-the-art methods in simulations. It outperforms all methods in robustness and safety as well as flight time. It also outperforms the only other state-of-the-art latency robust method in computation time

Real-time motion planning and decision-making for a group of differential drive robots under connectivity constraints using robust MPC and mixed-integer programming

This work is concerned with the problem of planning trajectories and assigning tasks for a Multi-Agent System (MAS) comprised of differential drive robots. We propose a multirate hierarchical control structure that employs a planner based on robust Model Predictive Control (MPC) with mixed-integer programming (MIP) encoding. The planner computes trajectories and assigns tasks for each element of the group in real-time, while also guaranteeing the communication network of the MAS to be robustly connected at all times. Additionally, we provide a data-based methodology to estimate the disturbances sets required by the robust MPC formulation. The results are demonstrated with experiments in two obstacle-filled scenariosComment: Submitted to Advanced Robotics special issue on Online Motion Planning and Model Predictive Contro

A flexible coupling approach to multi-agent planning under incomplete information

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-012-0569-7Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, and the Valencian Prometeo project 2008/051.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). A flexible coupling approach to multi-agent planning under incomplete information. 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An agent system to support student teams working online

Online learning is now a reality, with distributed learning and blended learning becoming more widely used in Higher Education. Novel ways in which undergraduate and postgraduate learning material can be presented are being developed, and methods for helping students to learn online are needed, especially if we require them to collaborate with each other on learning activities. Agents to provide a supporting role for students have evolved from Artificial Intelligence research, and their strength lies in their ease of operation over networks as well as their ability to act in response to stimuli. In this paper an application of a software agent is described, aimed at supporting students working on team projects in the online learning environment. Online teamwork is problematical for a number of reasons, such as getting acquainted with team members, finding out about other team members’ abilities, agreeing who should do which tasks, communications between team members and keeping up to date with progress that has been made on the project. Software agents have the ability to monitor progress and to offer advice by operating in the background, acting autonomously when the need arises. An agent prototype has been developed in Prolog to perform a limited set of functions to support students. Team projects have a planning, doing and completing stage, all of which require them to have some sort of agent support. This agent at present supports part of the planning stage, by prompting the students to input their likes, dislikes and abilities for a selection of task areas defined for the project. The agent then allocates the various tasks to the students according to predetermined rules. The results of a trial carried out using teams working on projects, on campus, indicate that students like the idea of using this agent to help with allocating tasks. They also agreed that agent support of this type would probably be helpful to both students working on team projects with face to face contact, as well as for teams working solely online. Work is ongoing to add more functionality to the agent and to evaluate the agent more widely

Scalable Multiagent Coordination with Distributed Online Open Loop Planning

We propose distributed online open loop planning (DOOLP), a general framework for online multiagent coordination and decision making under uncertainty. DOOLP is based on online heuristic search in the space defined by a generative model of the domain dynamics, which is exploited by agents to simulate and evaluate the consequences of their potential choices. We also propose distributed online Thompson sampling (DOTS) as an effective instantiation of the DOOLP framework. DOTS models sequences of agent choices by concatenating a number of multiarmed bandits for each agent and uses Thompson sampling for dealing with action value uncertainty. The Bayesian approach underlying Thompson sampling allows to effectively model and estimate uncertainty about (a) own action values and (b) other agents' behavior. This approach yields a principled and statistically sound solution to the exploration-exploitation dilemma when exploring large search spaces with limited resources. We implemented DOTS in a smart factory case study with positive empirical results. We observed effective, robust and scalable planning and coordination capabilities even when only searching a fraction of the potential search space