We have multiple papers accepted by ICRA and ACC 2021, congratulations to those lab members! See below for details (updating):
H-ModQuad: Modular Multi-Rotors with 4, 5, and 6 Controllable DOF (ICRA 2021)
Jiawei Xu*, Diego S. D’ Antonio, David Saldaña
Abstract: Traditional aerial vehicles are usually custom-designed for specific tasks. Although the vehicle is efficient, it might not be able to perform the task after a small change in the specification, e.g., increasing the payload. This applies to quadrotors, having a maximum payload and only four controllable degrees of freedom, limiting their adaptability to the task’s variations. We propose a versatile modular robotic system that can increase its payload and degrees of freedom by assembling heterogeneous modules; we call it H-ModQuad. It consists of cuboid modules, propelled by quadrotors with tilted propellers that can generate forces in different directions. By connecting different types of modules, an H-ModQuad can increase its controllable degrees of freedom from 4 to 5 and 6. We model the general structure and propose three controllers, one for each number of controllable degrees of freedom. We extend the concept of the actuation ellipsoid to find the best reference orientation that can maximize the performance of the structure. Our approach is validated with experiments using actual robots, showing the independence of the translation and orientation of a structure.
The Catenary Robot: Design and Control of a Cable Propelled by Two Quadrotors (ICRA 2021/RAL)
Diego S. D’ Antonio*, Gustavo A. Cardona, David Saldaña
Abstract: Transporting objects using aerial robots has been widely studied in the literature. Still, those approaches always assume that the connection between the quadrotor and the load is made in a previous stage. However, that previous stage usually requires human intervention, and autonomous procedures to locate and attach the object are not considered. Additionally, most of the approaches assume cables as rigid links, but manipulating cables requires considering the state when the cables are hanging. In this work, we design and control a catenary robot. Our robot is able to transport hook-shaped objects in the environment. The robotic system is composed of two quadrotors attached to the two ends of a cable. By defining the catenary curve with five degrees of freedom, position in 3-D, orientation in the z-axis, and span, we can drive the two quadrotors to track a given trajectory. We validate our approach with simulations and real robots. We present four different scenarios of experiments. Our numerical solution is computationally fast and can be executed in real-time.
Vision-Based Self-Assembly for Modular Quadrotor Structures (ICRA 2021)
Yehonathan Litman*, Neeraj Gandhi, Linh Thi Xuan Phan, David Saldaña
Abstract: Modular aerial robots can adapt their shape to suit a wide range of tasks, but developing efficient self-reconfiguration algorithms is still a challenge. Self-reconfiguration algorithms in the literature rely on high-accuracy global positioning systems which are not realistic for real-world applications. In this paper, we study self-reconfiguration algorithms using a combination of low-accuracy global positioning systems (e.g., GPS) and on-board relative positioning (e.g. visual sensing) for precise docking actions. We present three algorithms:
1) parallelized self-assembly sequencing that minimizes the number of serial “docking steps”;
2) parallelized self-assembly sequencing that minimizes total distance traveled by modules; and
3) parallelized self-reconfiguration that breaks an initial structure down as little as possible before assembling a new structure.
The algorithms take into account the constraints of the local sensors and use heuristics to provide a computationally efficient solution for the combinatorial problem. Our evaluation in 2-D and 3-D simulations shows that the algorithms scale with the number of modules and structure shape.
Resilient Task Allocation in Heterogeneous Multi-Robot Systems (ICRA 2021/RAL)
Siddharth Mayya*, Diego S. D’ Antonio, David Saldaña, Vijay Kumar
Abstract: This paper presents a resilient mechanism to allocate heterogeneous robots to tasks under difficult environmental conditions such as weather events or adversarial attacks. Our primary objective is to ensure that each task is assigned the requisite level of resources, measured as the aggregated capabilities of the robots allocated to the task. By keeping track of task performance deviations under external perturbations, our framework quantifies the extent to which robot capabilities (e.g., visual sensing or aerial mobility) are affected by environmental conditions. This enables an optimization-based framework to flexibly reallocate robots to tasks based on the most degraded capabilities within each task. In the face of resource limitations and adverse environmental conditions, our algorithm relaxes the resource constraints corresponding to some tasks, thus exhibiting a graceful degradation of performance. Simulated experiments in a multi-robot coverage and target tracking scenario demonstrate the efficacy of the proposed approach.
Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features. (ICRA 2021/RAL)
Xiao Li, Guy Rosman, Igor Gilitschenski, Cristian-Ioan Vasile, Jonathan A. DeCastro, Sertac Karaman, and Daniela Rus.
Abstract: In this work, we propose a novel approach for integrating rules into traffic agent trajectory prediction. Consideration of rules is important for understanding how people behave — yet, it cannot be assumed that rules are always followed. To address this challenge, we evaluate different approaches of integrating rules as inductive biases into deep learning-based prediction models. We propose a framework based on generative adversarial networks that uses tools from formal methods, namely signal temporal logic and syntax trees. This allows us to leverage information on rule obedience as features in neural networks and improves prediction accuracy without biasing towards lawful behavior. We evaluate our method on a real-world driving dataset and show improvement in performance over off-the-shelf predictors.
To appear on ICRA 2021/IEEE Robotics and Automation Letters.
Specifying User Preferences using Weighted Signal Temporal Logic (ACC/CSL)
Noushin Mehdipour, Cristian-Ioan Vasile, and Calin Belta
Abstract: We extend Signal Temporal Logic (STL) to enable the specification of importance and priorities. The extension, called Weighted STL (wSTL), has the same qualitative (Boolean) semantics as STL, but additionally defines weights associated with Boolean and temporal operators that modulate its quantitative semantics (robustness). We show that the robustness of wSTL can be defined as weighted generalizations of all known compatible robustness functionals (i.e., robustness scores that are recursively defined over formulae) that can take into account the weights in wSTL formulae. We utilize this weighted robustness to distinguish signals with respect to a desired wSTL formula that has subformulae with different importance or priorities and time preferences, and demonstrate its usefulness in problems with conflicting tasks where satisfaction of all tasks cannot be achieved. We also employ wSTL robustness in an optimization framework to synthesize controllers that maximize satisfaction of a specification with user specified preferences.
ACC/IEEE Control Systems Letters, December 2020.
DOI: 10.1109/LCSYS.2020.
A Control Architecture for Provably-Correct Autonomous Driving (ACC 2021)
Erfan Aasi, Cristian-Ioan Vasile, and Calin Belta
Abstract: This paper presents a novel two-level control architecture for a fully autonomous vehicle in a deterministic environment, which can handle traffic rules as specifications and low-level vehicle control with real-time performance. At the top level, we use a simple representation of the environment and vehicle dynamics to formulate a linear Model Predictive Control (MPC) problem. We describe the traffic rules and safety constraints using Signal Temporal Logic (STL) formulas, which are mapped to mixed integer-linear constraints in the optimization problem. The solution obtained at the top level is used at the bottom-level to determine the best control command for satisfying the constraints in a more detailed framework. At the bottom-level, specification-based runtime monitoring techniques, together with detailed representations of the environment and vehicle dynamics, are used to compensate for the mismatch between the simple models used in the MPC and the real complex models. We obtain substantial improvements over existing approaches in the literature in the sense of runtime performance and we validate the effectiveness of our proposed control approach in the simulator CARLA.
In American Control Conference (ACC), New Orleans, LA, USA, May 2021.
Robust Adaptive Synchronization of Interconnected Heterogeneous Quadrotors Transporting a Cable-Suspended Load (ICRA 2021)
Gustavo A. Cardona, Miguel Felipe Arevalo-Castiblanco, Duvan Tellez-castro, Juan Calderon, Eduardo Mojica-Nava
Abstract: We tackle the problem of multiple quadrotors transporting a cable-suspended point-mass load. The quadrotors are treated as a virtual leader-follower algorithm, where a multi-layer graph encapsulates the communication and physical interaction. On the one hand, communication stands for the approach of following the reference trajectory of a virtual leader. On the other hand, the load exerts a distributed tension force on each cable which is modeled as the well-known spring-damping system to each quadrotor establishing an interconnected dynamic. We assume cables are stretchable and have neglectable mass. Both objectives are accomplished through a Model Reference Adaptive Control approach with a robust modification that treats uncertainties and perturbations given by error in parameters, noise in the signal, and the wind drag forces. We prove stability based on the Lyapunov approach and the results are shown through simulation.