Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies

Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation. Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety. In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors. We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions

対象物体と指配置のコンフィグレーション空間を用いた不確かさを扱える効率的なケージング計画

学位の種別:課程博士University of Tokyo(東京大学

Skeleton based cage generation guided by harmonic fields

International audienceWe propose a novel user-assisted cage generation tool. We start from a digital character and its skeleton, and create a coarse control cage for its animation. Our method requires minimal interaction to select bending points on the skeleton, and computes the corresponding cage automatically. The key contribution is a volumetric field defined in the interior of the character and embedding the skeleton. The integral lines of such field are used to propagate cutting surfaces from the interior of the character to its skin, and allow us to robustly trace non-planar cross sections that adapt to the local shape of the character. Our method overcomes previous approaches that rely on the popular (but tedious and limiting) cutting planes. We validated our software on a variety of digital characters. Our final cages are coarse yet entirely compliant with the structure induced by the underlying skeleton, enriched with the semantics provided by the bending points selected by the user. Automatic placement of bending nodes for a fully automatic caging pipeline is also supported

Distributed cooperation of multiple robots under operational constraints via lean communication

Η αυτόνομη λειτουργία των ρομπότ εντός περίπλοκων χώρων εργασίας αποτελεί ένα επίκαιρο θέμα έρευνας και η αυτόνομη πλοήγηση είναι αναμφισβήτητα ένα θεμελιώδες κομμάτι αυτής. Επιπλέον, καθώς οι εργασίες που τα ρομπότ καλούνται να εκπληρώσουν αυξάνονται σε πολυπλοκότητα μέρα με τη μέρα, η χρήση πολύ-ρομποτικών συστημάτων, τα οποία εμφανίζουν γενικά υψηλότερη ευρωστία και ευελιξία, αυξάνεται προοδευτικά. Ως εκ τούτου, τα προβλήματα αυτόνομης πλοήγησης που πρέπει να επιλυθούν γίνονται όλο και πιο απαιτητικά, αυξάνοντας την ανάγκη για πιο αποτελεσματικά και σθεναρά σχήματα σχεδιασμού πορείας και κίνησης

Cooperation in Swarms of Robots without Communication

Swarm robotics aims to use a large group of relatively simple robots to solve tasks that can hardly be achieved by a single robot in the group. Compared to single robot systems with increased capability, a swarm robotic system may have advantages in robustness, flexibility and scalability. However, designing cooperative behaviors for a swarm robotic system is a challenging problem, especially when the robots may not have communication capabilities and thus only know local information. For a swarm of miniature mobile robots that cannot communicate explicitly, this thesis studies fully decentralized solutions of two problems. For the problem of cooperative transport, the thesis presents a strategy to push an object that is large enough to occlude the robots' perception of the goal of the transportation. For the problem of pattern formation, the thesis investigates algorithms based on the Brazil nut effect that can organize the swarm of robots into an annular formation. These problems are studied using physics-based computer simulations as well as experimental implementations based on the e-puck robotic platform. The simplicity of the solutions make them suitable for applications that require the individual robots to be as simple as possible. Example application scenarios could be micro robot swarms working in the human body

Dexterous grasping of novel objects from a single view

In this thesis, a novel generative-evaluative method was proposed to solve the problem of dexterous grasping of the novel object with a single view. The generative model is learned from human demonstration. The grasps generated by the generative model are used to train the evaluative model. Two novel evaluative network architectures are proposed. The evaluative model is a deep evaluative network that is trained in the simulation. The generative-evaluative method is tested in a real grasp data set with 49 previously unseen challenging objects. The generative-evaluative method achieves a success rate of 78% that outperforms the purely generative method, that has a success rate of 57%. The thesis provides insights into the strengths and weaknesses of the generative-evaluative method by comparing different deep network architectures

Bimanual Interaction with Clothes. Topology, Geometry, and Policy Representations in Robots

Twardon L. Bimanual Interaction with Clothes. Topology, Geometry, and Policy Representations in Robots. Bielefeld: Universität Bielefeld; 2019.If anthropomorphic robots are to assist people with activities of daily living, they must be able to handle all kinds of everyday objects, including highly deformable ones such as garments. The present thesis begins with a detailed problem analysis of robotic interaction with and perception of clothes. We show that handling items of clothing is very challenging due to their complex dynamics and the vast number of degrees of freedom. As a result of our analysis, we obtain a topological, geometric, and functional description of garments that supports the development of reduced object and task representations. One of the key findings is that the boundary components, which typically correspond with the openings, characterize garments well, both in terms of their topology and their inherent purpose, namely dressing. We present a polygon-based and an interactive method for identifying boundary components using RGB-D vision with application to grasping. Moreover, we propose Active Boundary Component Models (ABCMs), a constraint-based framework for tracking garment openings with point clouds. It is often difficult to maintain an accurate representation of the objects involved in contact-rich interaction tasks such as dressing assistance. Therefore, our policy optimization approach to putting a knit cap on a styrofoam head avoids modeling the details of the garment and its deformations. The experimental results suggest that a heuristic performance measure that takes into account the amount of contact established between the two objects is suitable for the task

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Computational Foundations for Safe and Efficient Human-Robot Collaboration in Assembly Cells

Human and robots have complementary strengths in performing assembly operations. Humans are very good at perception tasks in unstructured environments. They are able to recognize and locate a part from a box of miscellaneous parts. They are also very good at complex manipulation in tight spaces. The sensory characteristics of the humans, motor abilities, knowledge and skills give the humans the ability to react to unexpected situations and resolve problems quickly. In contrast, robots are very good at pick and place operations and highly repeatable in placement tasks. Robots can perform tasks at high speeds and still maintain precision in their operations. Robots can also operate for long periods of times. Robots are also very good at applying high forces and torques. Typically, robots are used in mass production. Small batch and custom production operations predominantly use manual labor. The high labor cost is making it difficult for small and medium manufacturers to remain cost competitive in high wage markets. These manufactures are mainly involved in small batch and custom production. They need to find a way to reduce the labor cost in assembly operations. Purely robotic cells will not be able to provide them the necessary flexibility. Creating hybrid cells where humans and robots can collaborate in close physical proximities is a potential solution. The underlying idea behind such cells is to decompose assembly operations into tasks such that humans and robots can collaborate by performing sub-tasks that are suitable for them. Realizing hybrid cells that enable effective human and robot collaboration is challenging. This dissertation addresses the following three computational issues involved in developing and utilizing hybrid assembly cells: - We should be able to automatically generate plans to operate hybrid assembly cells to ensure efficient cell operation. This requires generating feasible assembly sequences and instructions for robots and human operators, respectively. Automated planning poses the following two challenges. First, generating operation plans for complex assemblies is challenging. The complexity can come due to the combinatorial explosion caused by the size of the assembly or the complex paths needed to perform the assembly. Second, generating feasible plans requires accounting for robot and human motion constraints. The first objective of the dissertation is to develop the underlying computational foundations for automatically generating plans for the operation of hybrid cells. It addresses both assembly complexity and motion constraints issues. - The collaboration between humans and robots in the assembly cell will only be practical if human safety can be ensured during the assembly tasks that require collaboration between humans and robots. The second objective of the dissertation is to evaluate different options for real-time monitoring of the state of human operator with respect to the robot and develop strategies for taking appropriate measures to ensure human safety when the planned move by the robot may compromise the safety of the human operator. In order to be competitive in the market, the developed solution will have to include considerations about cost without significantly compromising quality. - In the envisioned hybrid cell, we will be relying on human operators to bring the part into the cell. If the human operator makes an error in selecting the part or fails to place it correctly, the robot will be unable to correctly perform the task assigned to it. If the error goes undetected, it can lead to a defective product and inefficiencies in the cell operation. The reason for human error can be either confusion due to poor quality instructions or human operator not paying adequate attention to the instructions. In order to ensure smooth and error-free operation of the cell, we will need to monitor the state of the assembly operations in the cell. The third objective of the dissertation is to identify and track parts in the cell and automatically generate instructions for taking corrective actions if a human operator deviates from the selected plan. Potential corrective actions may involve re-planning if it is possible to continue assembly from the current state. Corrective actions may also involve issuing warning and generating instructions to undo the current task