Safe, Remote-Access Swarm Robotics Research on the Robotarium

This paper describes the development of the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-agent research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium as well as connects these to the particular considerations one must take when making complex hardware remotely accessible. In particular, safety must be built in already at the design phase without overly constraining which coordinated control programs the users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.Comment: 13 pages, 7 figures, 3 code samples, 72 reference

Safe, Remote-Access Swarm Robotics Research on the Robotarium

This paper describes the development of the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-agent research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium as well as connects these to the particular considerations one must take when making complex hardware remotely accessible. In particular, safety must be built in already at the design phase without overly constraining which coordinated control programs the users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees

Clustering-Based Robot Navigation and Control

In robotics, it is essential to model and understand the topologies of configuration spaces in order to design provably correct motion planners. The common practice in motion planning for modelling configuration spaces requires either a global, explicit representation of a configuration space in terms of standard geometric and topological models, or an asymptotically dense collection of sample configurations connected by simple paths, capturing the connectivity of the underlying space. This dissertation introduces the use of clustering for closing the gap between these two complementary approaches. Traditionally an unsupervised learning method, clustering offers automated tools to discover hidden intrinsic structures in generally complex-shaped and high-dimensional configuration spaces of robotic systems. We demonstrate some potential applications of such clustering tools to the problem of feedback motion planning and control. The first part of the dissertation presents the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. We reinterpret this classical method for unsupervised learning as an abstract formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions, by relating the continuous space of configurations to the combinatorial space of trees. Based on this new abstraction and a careful topological characterization of the associated hierarchical structure, a provably correct, computationally efficient hierarchical navigation framework is proposed for collision-free coordinated motion design towards a designated multirobot configuration via a sequence of hierarchy-preserving local controllers. The second part of the dissertation introduces a new, robot-centric application of Voronoi diagrams to identify a collision-free neighborhood of a robot configuration that captures the local geometric structure of a configuration space around the robot’s instantaneous position. Based on robot-centric Voronoi diagrams, a provably correct, collision-free coverage and congestion control algorithm is proposed for distributed mobile sensing applications of heterogeneous disk-shaped robots; and a sensor-based reactive navigation algorithm is proposed for exact navigation of a disk-shaped robot in forest-like cluttered environments. These results strongly suggest that clustering is, indeed, an effective approach for automatically extracting intrinsic structures in configuration spaces and that it might play a key role in the design of computationally efficient, provably correct motion planners in complex, high-dimensional configuration spaces

Long-duration robot autonomy: From control algorithms to robot design

The transition that robots are experiencing from controlled and often static working environments to unstructured and dynamic settings is unveiling the potential fragility of the design and control techniques employed to build and program them, respectively. A paramount of example of a discipline that, by construction, deals with robots operating under unknown and ever-changing conditions is long-duration robot autonomy. In fact, during long-term deployments, robots will find themselves in environmental scenarios which were not planned and accounted for during the design phase. These operating conditions offer a variety of challenges which are not encountered in any other discipline of robotics. This thesis presents control-theoretic techniques and mechanical design principles to be employed while conceiving, building, and programming robotic systems meant to remain operational over sustained amounts of time. Long-duration autonomy is studied and analyzed from two different, yet complementary, perspectives: control algorithms and robot design. In the context of the former, the persistification of robotic tasks is presented. This consists of an optimization-based control framework which allows robots to remain operational over time horizons that are much longer than the ones which would be allowed by the limited resources of energy with which they can ever be equipped. As regards the mechanical design aspect of long-duration robot autonomy, in the second part of this thesis, the SlothBot, a slow-paced solar-powered wire-traversing robot, is presented. This robot embodies the design principles required by an autonomous robotic system 1in order to remain functional for truly long periods of time, including energy efficiency, design simplicity, and fail-safeness. To conclude, the development of a robotic platform which stands at the intersection of design and control for long-duration autonomy is described. A class of vibration-driven robots, the brushbots, are analyzed both from a mechanical design perspective, and in terms of interaction control capabilities with the environment in which they are deployed.Ph.D

Assured Autonomy in Multiagent Systems with Safe Learning

Autonomous multiagent systems is an area that is currently receiving increasing attention in the communities of robotics, control systems, and machine learning (ML) and artificial intelligence (AI). It is evident today, how autonomous robots and vehicles can help us shape our future. Teams of robots are being used to help identify and rescue survivors in case of a natural disaster for instance. There we understand that we are talking minutes and seconds that can decide whether you can save a person's life or not. This example portrays not only the value of safety but also the significance of time, in planning complex missions with autonomous agents. This thesis aims to develop a generic, composable framework for a multiagent system (of robots or vehicles), which can safely carry out time-critical missions in a distributed and autonomous fashion. The goal is to provide formal guarantees on both safety and finite-time mission completion in real time, thus, to answer the question: “how trustworthy is the autonomy of a multi-robot system in a complex mission?” We refer to this notion of autonomy in multiagent systems as assured or trusted autonomy, which is currently a very sought-after area of research, thanks to its enormous applications in autonomous driving for instance. There are two interconnected components of this thesis. In the first part, using tools from control theory (optimal control), formal methods (temporal logic and hybrid automata), and optimization (mixed-integer programming), we propose multiple variants of (almost) realtime planning algorithms, which provide formal guarantees on safety and finite-time mission completion for a multiagent system in a complex mission. Our proposed framework is hybrid, distributed, and inherently composable, as it uses a divide-and-conquer approach for planning a complex mission, by breaking it down into several sub-tasks. This approach enables us to implement the resulting algorithms on robots with limited computational power, while still achieving close to realtime performance. We validate the efficacy of our methods on multiple use cases such as autonomous search and rescue with a team of unmanned aerial vehicles (UAVs) and ground robots, autonomous aerial grasping and navigation, UAV-based surveillance, and UAV-based inspection tasks in industrial environments. In the second part, our goal is to translate and adapt these developed algorithms to safely learn actions and policies for robots in dynamic environments, so that they can accomplish their mission even in the presence of uncertainty. To accomplish this goal, we introduce the ideas of self-monitoring and self-correction for agents using hybrid automata theory and model predictive control (MPC). Self-monitoring and self-correction refer to the problems in autonomy where the autonomous agents monitor their performance, detect deviations from normal or expected behavior, and learn to adjust both the description of their mission/task and their performance online, to maintain the expected behavior and performance. In this setting, we propose a formal and composable notion of safety and adaptation for autonomous multiagent systems, which we refer to as safe learning. We revisit one of the earlier use cases to demonstrate the capabilities of our approach for a team of autonomous UAVs in a surveillance and search and rescue mission scenario. Despite portraying results mainly for UAVs in this thesis, we argue that the proposed planning framework is transferable to any team of autonomous agents, under some realistic assumptions. We hope that this research will serve several modern applications of public interest, such as autopilots and flight controllers, autonomous driving systems (ADS), autonomous UAV missions such as aerial grasping and package delivery with drones etc., by improving upon the existing safety of their autonomous operation

KINE[SIS]TEM'17 From Nature to Architectural Matter

Kine[SiS]tem – From Kinesis + System. Kinesis is a non-linear movement or activity of an organism in response to a stimulus. A system is a set of interacting and interdependent agents forming a complex whole, delineated by its spatial and temporal boundaries, influenced by its environment. How can architectural systems moderate the external environment to enhance comfort conditions in a simple, sustainable and smart way? This is the starting question for the Kine[SiS]tem’17 – From Nature to Architectural Matter International Conference. For decades, architectural design was developed despite (and not with) the climate, based on mechanical heating and cooling. Today, the argument for net zero energy buildings needs very effective strategies to reduce energy requirements. The challenge ahead requires design processes that are built upon consolidated knowledge, make use of advanced technologies and are inspired by nature. These design processes should lead to responsive smart systems that deliver the best performance in each specific design scenario. To control solar radiation is one key factor in low-energy thermal comfort. Computational-controlled sensor-based kinetic surfaces are one of the possible answers to control solar energy in an effective way, within the scope of contradictory objectives throughout the year.FC

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors