Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles

In the near future mobile robots, such as personal robots or mobile manipulators, will share the workspace with other robots and humans. We present a method for mission and motion planning that applies to small teams of robots performing a task in an environment with moving obstacles, such as humans. Given a mission specification written in linear temporal logic, such as patrolling a set of rooms, we synthesize an automaton from which the robots can extract valid strategies. This centralized automaton is executed by the robots in the team at runtime, and in conjunction with a distributed motion planner that guarantees avoidance of moving obstacles. Our contribution is a correct-by-construction synthesis approach to multi-robot mission planning that guarantees collision avoidance with respect to moving obstacles, guarantees satisfaction of the mission specification and resolves encountered deadlocks, where a moving obstacle blocks the robot temporally. Our method provides conditions under which deadlock will be avoided by identifying environment behaviors that, when encountered at runtime, may prevent the robot team from achieving its goals. In particular, (1) it identifies deadlock conditions; (2) it is able to check whether they can be resolved; and (3) the robots implement the deadlock resolution policy locally in a distributed manner. The approach is capable of synthesizing and executing plans even with a high density of dynamic obstacles. In contrast to many existing approaches to mission and motion planning, it is scalable with the number of moving obstacles. We demonstrate the approach in physical experiments with walking humanoids moving in 2D environments and in simulation with aerial vehicles (quadrotors) navigating in 2D and 3D environments.Boeing CompanyUnited States. Office of Naval Research. Multidisciplinary University Research Initiative. SMARTS (N00014-09-1051)United States. Office of Naval Research (N00014-12-1-1000)National Science Foundation (U.S.). Expeditions in Computer Augmented Program Engineerin

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Contributions to shared control and coordination of single and multiple robots

L’ensemble des travaux présentés dans cette habilitation traite de l'interface entre un d'un opérateur humain avec un ou plusieurs robots semi-autonomes aussi connu comme le problème du « contrôle partagé ».Le premier chapitre traite de la possibilité de fournir des repères visuels / vestibulaires à un opérateur humain pour la commande à distance de robots mobiles.Le second chapitre aborde le problème, plus classique, de la mise à disposition à l’opérateur d’indices visuels ou de retour haptique pour la commande d’un ou plusieurs robots mobiles (en particulier pour les drones quadri-rotors).Le troisième chapitre se concentre sur certains des défis algorithmiques rencontrés lors de l'élaboration de techniques de coordination multi-robots.Le quatrième chapitre introduit une nouvelle conception mécanique pour un drone quadrirotor sur-actionné avec pour objectif de pouvoir, à terme, avoir 6 degrés de liberté sur une plateforme quadrirotor classique (mais sous-actionné).Enfin, le cinquième chapitre présente une cadre général pour la vision active permettant, en optimisant les mouvements de la caméra, l’optimisation en ligne des performances (en terme de vitesse de convergence et de précision finale) de processus d’estimation « basés vision »

Task Partitioning for Distributed Assembly

This thesis addresses the problem of how to plan a strategy for a team of robots to cooperatively build a structure, henceforth referred to as the distributed assembly problem. The problem of distributed assembly requires a range of capabilities for successful completion of the task. These include accurate sensing and manipulation using a mobile robot, the ability to continuously adhere to precedence constraints on placements, and the ability to guarantee static stability at every stage of construction. The fundamental contribution of this work is to propose methods to address task allocation problems in the presence of constraints on task ordering. Algorithms are presented to partition 2- and 3-D assembly tasks into separate subtasks that satisfy local and global precedence constraints between the assembly components. The objective is to achieve a partitioning that minimizes completion time by minimizing the workload imbalance between the robots, and maximizes assembly parallelization. Towards this objective four approaches are presented. The first is an approach where each robot runs a simultaneous Dijkstra's Algorithm with its own root. The second approach incorporates online workload balancing and error correction by adding a communication scheme and a scanning robot equipped with a visual depth sensor. The third approach addresses the task partitioning using an algorithm inspired by Ant Colony Optimization. Finally, the problem of cooperative manipulation for tasks that require close coordination is addressed. All approaches are tested in both simulation and experiment.Ph.D., Mechanical Engineering -- Drexel University, 201

General Concepts for Human Supervision of Autonomous Robot Teams

For many dangerous, dirty or dull tasks like in search and rescue missions, deployment of autonomous teams of robots can be beneficial due to several reasons. First, robots can replace humans in the workspace. Second, autonomous robots reduce the workload of a human compared to teleoperated robots, and therefore multiple robots can in principle be supervised by a single human. Third, teams of robots allow distributed operation in time and space. This thesis investigates concepts of how to efficiently enable a human to supervise and support an autonomous robot team, as common concepts for teleoperation of robots do not apply because of the high mental workload. The goal is to find a way in between the two extremes of full autonomy and pure teleoperation, by allowing to adapt the robots’ level of autonomy to the current situation and the needs of the human supervisor. The methods presented in this thesis make use of the complementary strengths of humans and robots, by letting the robots do what they are good at, while the human should support the robots in situations that correspond to the human strengths. To enable this type of collaboration between a human and a robot team, the human needs to have an adequate knowledge about the current state of the robots, the environment, and the mission. For this purpose, the concept of situation overview (SO) has been developed in this thesis, which is composed of the two components robot SO and mission SO. Robot SO includes information about the state and activities of each single robot in the team, while mission SO deals with the progress of the mission and the cooperation between the robots. For obtaining SO a new event-based communication concept is presented in this thesis, that allows the robots to aggregate information into discrete events using methods from complex event processing. The quality and quantity of the events that are actually sent to the supervisor can be adapted during runtime by defining positive and negative policies for (not) sending events that fulfill specific criteria. This reduces the required communication bandwidth compared to sending all available data. Based on SO, the supervisor is enabled to efficiently interact with the robot team. Interactions can be initiated either by the human or by the robots. The developed concept for robot-initiated interactions is based on queries, that allow the robots to transfer decisions to another process or the supervisor. Various modes for answering the queries, ranging from fully autonomous to pure human decisions, allow to adapt the robots’ level of autonomy during runtime. Human-initiated interactions are limited to high-level commands, whereas interactions on the action level (e. g., teleoperation) are avoided, to account for the specific strengths of humans and robots. These commands can in principle be applied to quite general classes of task allocation methods for autonomous robot teams, e. g., in terms of specific restrictions, which are introduced into the system as constraints. In that way, the desired allocations emerge implicitly because of the introduced constraints, and the task allocation method does not need to be aware of the human supervisor in the loop. This method is applicable to different task allocation approaches, e. g., instantaneous or time-extended task assignments, and centralized or distributed algorithms. The presented methods are evaluated by a number of different experiments with physical and simulated scenarios from urban search and rescue as well as robot soccer, and during robot competitions. The results show that with these methods a human supervisor can significantly improve the robot team performance