Magali Barbier

Bio: Magali Barbier is an academic researcher from University of Toulouse. The author has contributed to research in topics: Partial-order planning & Robot. The author has an hindex of 4, co-authored 12 publications receiving 76 citations.

A distributed architecture for supervision of autonomous multi-robot missions

Abstract: Realizing long-term autonomous missions involving teams of heterogeneous robots is a challenge. It requires mechanisms to make robots react to disturbances or failures that will arise during the mission, while trying to successfully achieve the mission in cooperation. This paper presents HiDDeN, a distributed deliberative architecture that manages the execution of a hierarchical plan. This plan has initially been computed offline, ensuring some military operational constraints of the mission. Each robot's supervisor then executes its own part of the plan, and reacts to failures using a hierarchical repair approach. This hierarchical repair has been designed with the sake of ensuring operational constraints, while reducing the need of communication between robots, as communication may be intermittent or even nonexistent when the robots operate in completely separate environments. HiDDeN's robustness and scalability is evaluated with simulations. Experiments with an autonomous helicopter and an autonomous underwater vehicle have been realized and are presented as the defining point of our contribution.

HiPOP: Hierarchical Partial-Order Planning.

Abstract: This paper describes a new planner, HiPOP (Hierarchical Partial-Order Planner), which is domain-configurable and uses POP techniques to create hierarchical time-flexible plans. HiPOP takes as inputs a description of a domain, a problem, and some optional userdefined search-control knowledge. This additional knowledge takes the form of a set of abstract actions with optional methods to achieve them. HiPOP uses this knowledge to enrich the output by providing a hierarchical time-flexible partial-order plan that follows the given methods. We show in this paper how to use this additional knowledge in a POP algorithm and provide results on a domain with a strong hierarchy of actions. We compare our approach with other temporal planners on this

HiDDeN: Cooperative plan execution and repair for heterogeneous robots in dynamic environments

Abstract: This paper presents HiDDeN, a high-level distributed architecture for multi-robot cooperation. HiDDeN aims at controlling a team of heterogeneous robots in environments with uncertain communications. It relies on a mission plan defined as an instantiated HTN, i.e. a hierarchical decomposition of robots' tasks. This hierarchical structure also benefits to plan repair operations in case of failure detections. This repair is made as local as possible, in order to avoid unnecessary communications between robots.

Hybrid planning and distributed iterative repair for multi-robot missions with communication losses

Abstract: This paper presents a planning and execution architecture suited for the initial planning, the execution and the on-board repair of a plan for a multi-robot mission. The team as a whole must accomplish its mission while dealing with online events such as robots breaking down, new objectives for the team, late actions and intermittent communications. We have chosen a “plan then repair” approach where an initial plan is computed offline and updated online whenever disruptive events happen. We have defined an hybrid planner that mixes Partial Order Planning (POP) with a Hierarchical Task Network (HTN)-based modelling of actions. This planner, called HiPOP for Hierarchical Partial-Order Planner, computes plans with temporal flexibility (thus easing its execution) and abstract actions (thus easing the repair process). It uses a symbolic representation of the world and has been extended with geometrical reasoning to adapt to multi-robots missions. Plans are executed in a distributed way: each robot is responsible of executing its own actions, and to propagate delays in its local plan, taking benefit from the temporal flexibility of the plan. When an inconsistency or a failure arises, a distributed repair algorithm based on HiPOP is used to repair the plan, by iteratively removing actions in the plan in order to amend the global plan. This repair is done onboard one of the robot of the team, and takes care of partial communication. The whole architecture has been evaluated through several benchmarks, statistical simulations, and field experiments involving 8 robots.

Using hybrid planning for plan reparation

Abstract: In this work we propose a plan repair algorithm designed to be used in a real-life setting for a team of autonomous robots. This algorithm is built on top of a hybrid planner. This planner mixes partial order planning and hierarchical planning. This allows the creation of a plan with temporal flexibility while using human knowledge to improve the search process. Simulation shows that repairing increases the number of solved problems or at least reduces the number of plans explored. The algorithm uses the same hierarchical knowledge as the underlying planner, thus needing no more human modelling to properly run. We show that using this knowledge can help the reparation, even when some half-executed abstract actions are present in the plan.

Cooperative Heterogeneous Multi-Robot Systems: A Survey

Abstract: The emergence of the Internet of things and the widespread deployment of diverse computing systems have led to the formation of heterogeneous multi-agent systems (MAS) to complete a variety of tasks. Motivated to highlight the state of the art on existing MAS while identifying their limitations, remaining challenges, and possible future directions, we survey recent contributions to the field. We focus on robot agents and emphasize the challenges of MAS sub-fields including task decomposition, coalition formation, task allocation, perception, and multi-agent planning and control. While some components have seen more advancements than others, more research is required before effective autonomous MAS can be deployed in real smart city settings that are less restrictive than the assumed validation environments of MAS. Specifically, more autonomous end-to-end solutions need to be experimentally tested and developed while incorporating natural language ontology and dictionaries to automate complex task decomposition and leveraging big data advancements to improve perception algorithms for robotics.

A Robot Ontology for Urban Search and Rescue | NIST

Abstract: The goal of this Robot Ontology effort is to develop and begin to populate a neutral knowledge representation (the data structures) capturing relevant information about robots and their capabilities to assist in the development, testing, and certification of effective technologies for sensing, mobility, navigation, planning, integration and operator interaction within search and rescue robot systems. This knowledge representation must be flexible enough to adapt as the robot requirements evolve. As such, we have chosen to use an ontological approach to representing these requirements. This paper describes the Robot Ontology, how it fits in to the overall Urban Search and Rescue effort, how we will be proceeding in the future.

Review of mission planning for autonomous marine vehicle fleets

Abstract: The deployment of a fleet of autonomous marine vehicles (AMVs) allows for the parallelisation of missions, intervehicle support for longer deployment times, adaptability and redundancy to in situ mission changes, and effective use of the right vehicle for the right purpose. End users and operators of AMVs face challenges in planning complex missions due to the limitations of their vehicles, dynamic, operationally constrictive, and unstructured environments, and in minimising risks to equipment, the mission, and personnel. Automated mission planning for AMV fleets can be a tool to reduce the complexity of programming vehicle tasking, and to perform validity assessments for end user‐specified goals, allowing the operator to focus on risk assessment. We present a critical review of the current advances in automated planning for AMV fleets, investigating the limitations of available state‐of‐the‐art tools and providing a road map of the goals and challenges based on analysis of field reports and end user initiatives.

Multi-robot Systems, Virtual Reality and ROS: Developing a New Generation of Operator Interfaces

Abstract: This chapter describes a series of works developed in order to integrate ROS-based robots with Unity-based virtual reality interfaces. The main goal of this integration is to develop immersive monitoring and commanding interfaces, able to improve the operator’s situational awareness without increasing its workload. In order to achieve this, the available technologies and resources are analyzed and multiple ROS packages and Unity assets are applied, such as \(multimaster\_fkie\), \(rosbridge\_suite\), RosBridgeLib and SteamVR. Moreover, three applications are presented: an interface for monitoring a fleet of drones, another interface for commanding a robot manipulator and an integration of multiple ground and aerial robots. Finally, some experiences and lessons learned, useful for future developments, are reported.

The Distributed Adaptive Finite-Time Chattering Reduction Containment Control for Multiple Ocean Bottom Flying Nodes

Abstract: The ocean bottom flying node (OBFN) is a novel autonomous underwater vehicle (AUV) system which could explore the oil and gas resources in deep water. This paper investigates the distributed finite-time chattering reduction containment control problem for multiple OBFNs under directed communication topology. The model uncertainties and external disturbances are considered. By defining the containment error variables and selecting high-order sliding variable properly, a distributed finite-time containment control strategy is developed. The discontinuous sign function is contained in the derivative of the control protocol so as to eliminate the chattering phenomenon. An adaptive law is designed to make efficiency estimation and compensation for the upper bounds of model uncertainties and external disturbances. Combined with the graph theory and matrix theory, the Lyapunov method is utilized to demonstrate that the follower OBFNs could enter the convex hull formed by the leader OBFNs in finite time. Numerical simulation is provided to show the effectiveness of the proposed method.