Non-Prehensile Manipulation of Cuboid Objects Using a CatenaryRobot (IROS + RAL)

Gustavo A. Cardona, Diego S. D’Antonio, Cristian-Ioan Vasile, and David Saldaña

Abstract: Manipulating objects with quadrotors has been widely studied in the literature, but the majority of those approaches assume quadrotors and loads are previously attached. This setup requires human intervention that is not always achievable or desirable in practice. Furthermore, most of the robot configurations consider rods, manipulators, magnets, and cables modeled as rigid links attached to predefined places on objects. In contrast, we are interested in manipulating objects that are not specifically designed to interact with quadrotors, e.g., no predefined connections, and that do not require humans to set up. In this paper, we control a catenary robot composed of a cable and two quadrotors attached to its ends. Our robot is tasked with moving cuboid objects (boxes) on a planar surface. We design a controller that allows the catenary robot to place the cable in a specific area on the box to perform dragging or rolling. We validate our control design in simulation and with real robots, where we show them rolling and dragging boxes to track desired trajectories.

Automata-based Optimal Planning with Relaxed Specifications

Disha Kamale, Eleni Karyofylli, Cristian-Ioan Vasile

Abstract: In this paper, we introduce an automata-based framework for planning with relaxed specifications. User relaxation preferences are represented as weighted finite state edit systems that capture permissible operations on the specification, substitution and deletion of tasks, with complex constraints on ordering and grouping. We propose a three-way product automaton construction method that allows us to compute minimal relaxation policies for the robots using shortest path algorithms. The three-way automaton captures the robot’s motion, specification satisfaction, and available relaxations at the same time. Additionally, we consider a bi-objective problem that balances temporal relaxation of deadlines within specifications with changing and deleting tasks. Finally, we present the runtime performance and a case study that highlights different modalities of our framework.

Event-Triggered Control for Weight-Unbalanced Directed Robot Networks

Juan D. Pabon, G.A. Cardona, N. I. Ospina, J. M. Calderon, and E. Mojica-Nava

Abstract: In this paper, we develop an event-triggered control strategy for a weighted-unbalanced directed homogeneous robot network. We present some guarantees for synchronization of a robot network when all robots have access to the reference and also when a limited number of robots have access. The proposed event-triggered control is able to reduce and avoid the periodic updating of the signals. Unlike some current control methods, we prove stability for both cases making use of a logarithmic norm, which extends the possibilities of the control law to be applied to a wide range of directed graphs, in contrast to other works where the event-triggered control can be only implemented over strongly connected and weight-balanced digraphs. We test the performance of our algorithm by carrying out experiments both in simulation and in a real team of robots.

Finding Near-Optimal Configurations for Flying Modular Structures

Bruno Gabrich, David Saldaña, and Mark Yim

Abstract: Flying Modular Structures offer a versatile mechanism that can change the arrangement of constituent actuators according to task requirements. In this work, we extend a modular aerial platform that can expand its actuation capabilities depending on the configuration. Each module is composed of a quadrotor in a cage that can rigidly connect with other modules. The quadrotor is connected with the cage by a revolute joint that allows it to rotate with respect to the cage. Modules located in the structure are either parallel or perpendicular to one another. The task specification defines forces and moments needed during the execution. We propose two search methods to find a configuration that can satisfy the specification. The first approach consists of an exhaustive search that yields optimal structure configurations by exploring the whole search space. The second approach proposes a heuristic based on subgroup search, reducing the problem complexity from exponential to linear. We validate our proposed algorithms with several simulations. Our results show that the proposed heuristic is computationally efficient and finds a near-optimal configuration even for flying modular structures composed of a large number of modules.

Papers in IROS 2021