D2CoPlan: A Differentiable Decentralized Planner for Multi-Robot Coverage

Centralized approaches for multi-robot coverage planning problems suffer from the lack of scalability. Learning-based distributed algorithms provide a scalable avenue in addition to bringing data-oriented feature generation capabilities to the table, allowing integration with other learning-based approaches. To this end, we present a learning-based, differentiable distributed coverage planner (D2COPL A N) which scales efficiently in runtime and number of agents compared to the expert algorithm, and performs on par with the classical distributed algorithm. In addition, we show that D2COPlan can be seamlessly combined with other learning methods to learn end-to-end, resulting in a better solution than the individually trained modules, opening doors to further research for tasks that remain elusive with classical methods

Distributed, Scalable And Resilient Information Acquisition For Multi-Robot Teams

Advances in robotic mobility and sensing technology have the potential to provide newcapabilities in a wide variety of information acquisition problems including environmental monitoring, structure inspection, localization and mapping of unknown environments, and search and rescue, amongst many others. In particular, teams composed of multiple robots have shown great potential in solving these problems, though it is challenging to design efficient algorithms that are distributed and scale well, and even more complex in hazardous or challenging environments. The purpose of this dissertation is to provide novel algorithms to the capabilities of multi-robot teams to gather information which are distributed, scalable, and resilient. The first part of the dissertation introduces the single-robot information acquisition problem, and focuses on algorithms that may be used for individual robots to plan their own trajectories. The methods presented here are search-based, meaning that an individual robot has a finite set of actions and is seeking to efficiently build a search tree over a known planning horizon. The first method presented details how to use the concept of algebraic redundancy and closeness to achieve a smooth trade-off of completeness in the exploration process, as an anytime planning algorithm. Next we show how a single robot can compute an admissible and consistent heuristic which guides the search towards the most informative regions of the state space, using the classic A* planning algorithm, drastically improving the search efficiency. The next chapter of the dissertation focuses on how to build on the single robot planning algorithms to create efficient algorithms for multi-robot teams, which operate in a distributed manner and scalable manner. The first method presented is coordinate descent, 5 otherwise known in the literature as sequential greedy assignment. This algorithm is implemented in a multi-robot target tracking hardware experiment. Next, we formulate an energy-aware multi-robot information acquisition problem, which allows for heterogeneity and captures trade-offs between information and energy expenditure. However, this results in a non-monotone objective function. Therefore we propose a new algorithm based on distributed local search, which achieves performance guarantees through a diminishing returns property known as submodularity. The final chapter focuses on hazardous or failure prone environments that necessitate resilience to a fixed number of failures in the multi-robot team. We provide a definition of resilience, and formulate a resilient information acquisition problem. We then propose the first algorithm that solves this problem through an online application of robust trajectory planning, and provide theoretical guarantees on its performance. We then present three unique applications of the resilient multi-robot information acquisition framework, including target tracking, occupancy grid mapping, and persistent surveillance which demonstrate the efficacy of our approach

Resilient Submodular Maximization For Control And Sensing

Fundamental applications in control, sensing, and robotics, motivate the design of systems by selecting system elements, such as actuators or sensors, subject to constraints that require the elements not only to be a few in number, but also, to satisfy heterogeneity or interdependency constraints (called matroid constraints). For example, consider the scenarios: - (Control) Actuator placement: In a power grid, how should we place a few generators both to guarantee its stabilization with minimal control effort, and to satisfy interdependency constraints where the power grid must be controllable from the generators? - (Sensing) Sensor placement: In medical brain-wearable devices, how should we place a few sensors to ensure smoothing estimation capabilities? - (Robotics) Sensor scheduling: At a team of mobile robots, which few on-board sensors should we activate at each robot ---subject to heterogeneity constraints on the number of sensors that each robot can activate at each time--- so both to maximize the robots\u27 battery life, and to ensure the robots\u27 capability to complete a formation control task? In the first part of this thesis we motivate the above design problems, and propose the first algorithms to address them. In particular, although traditional approaches to matroid-constrained maximization have met great success in machine learning and facility location, they are unable to meet the aforementioned problem of actuator placement. In addition, although traditional approaches to sensor selection enable Kalman filtering capabilities, they do not enable smoothing or formation control capabilities, as required in the above problems of sensor placement and scheduling. Therefore, in the first part of the thesis we provide the first algorithms, and prove they achieve the following characteristics: provable approximation performance: the algorithms guarantee a solution close to the optimal; minimal running time: the algorithms terminate with the same running time as state-of-the-art algorithms for matroid-constrained maximization; adaptiveness: where applicable, at each time step the algorithms select system elements based on both the history of selections. We achieve the above ends by taking advantage of a submodular structure of in all aforementioned problems ---submodularity is a diminishing property for set functions, parallel to convexity for continuous functions. But in failure-prone and adversarial environments, sensors and actuators can fail; sensors and actuators can get attacked. Thence, the traditional design paradigms over matroid-constraints become insufficient, and in contrast, resilient designs against attacks or failures become important. However, no approximation algorithms are known for their solution; relevantly, the problem of resilient maximization over matroid constraints is NP-hard. In the second part of this thesis we motivate the general problem of resilient maximization over matroid constraints, and propose the first algorithms to address it, to protect that way any design over matroid constraints, not only within the boundaries of control, sensing, and robotics, but also within machine learning, facility location, and matroid-constrained optimization in general. In particular, in the second part of this thesis we provide the first algorithms, and prove they achieve the following characteristics: resiliency: the algorithms are valid for any number of attacks or failures; adaptiveness: where applicable, at each time step the algorithms select system elements based on both the history of selections, and on the history of attacks or failures; provable approximation guarantees: the algorithms guarantee for any submodular or merely monotone function a solution close to the optimal; minimal running time: the algorithms terminate with the same running time as state-of-the-art algorithms for matroid-constrained maximization. We bound the performance of our algorithms by using notions of curvature for monotone (not necessarily submodular) set functions, which are established in the literature of submodular maximization. In the third and final part of this thesis we apply our tools for resilient maximization in robotics, and in particular, to the problem of active information gathering with mobile robots. This problem calls for the motion-design of a team of mobile robots so to enable the effective information gathering about a process of interest, to support, e.g., critical missions such as hazardous environmental monitoring, and search and rescue. Therefore, in the third part of this thesis we aim to protect such multi-robot information gathering tasks against attacks or failures that can result to the withdrawal of robots from the task. We conduct both numerical and hardware experiments in multi-robot multi-target tracking scenarios, and exemplify the benefits, as well as, the performance of our approach

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects