A Dynamical System Approach for Resource-Constrained Mobile Robotics

The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system

Persistent Monitoring of Events with Stochastic Arrivals at Multiple Stations

This paper introduces a new mobile sensor scheduling problem, involving a single robot tasked with monitoring several events of interest that occur at different locations. Of particular interest is the monitoring of transient events that can not be easily forecast. Application areas range from natural phenomena ({\em e.g.}, monitoring abnormal seismic activity around a volcano using a ground robot) to urban activities ({\em e.g.}, monitoring early formations of traffic congestion using an aerial robot). Motivated by those and many other examples, this paper focuses on problems in which the precise occurrence times of the events are unknown {\em a priori}, but statistics for their inter-arrival times are available. The robot's task is to monitor the events to optimize the following two objectives: {\em (i)} maximize the number of events observed and {\em (ii)} minimize the delay between two consecutive observations of events occurring at the same location. The paper considers the case when a robot is tasked with optimizing the event observations in a balanced manner, following a cyclic patrolling route. First, assuming the cyclic ordering of stations is known, we prove the existence and uniqueness of the optimal solution, and show that the optimal solution has desirable convergence and robustness properties. Our constructive proof also produces an efficient algorithm for computing the unique optimal solution with $O(n)$ time complexity, in which $n$ is the number of stations, with $O(\log n)$ time complexity for incrementally adding or removing stations. Except for the algorithm, most of the analysis remains valid when the cyclic order is unknown. We then provide a polynomial-time approximation scheme that gives a $(1+\epsilon)$-optimal solution for this more general, NP-hard problem

RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX

To enable the computation of effective randomized patrol routes for single- or multi-robot teams, we present RoSSO, a Python package designed for solving Markov chain optimization problems. We exploit machine-learning techniques such as reverse-mode automatic differentiation and constraint parametrization to achieve superior efficiency compared to general-purpose nonlinear programming solvers. Additionally, we supplement a game-theoretic stochastic surveillance formulation in the literature with a novel greedy algorithm and multi-robot extension. We close with numerical results for a police district in downtown San Francisco that demonstrate RoSSO's capabilities on our new formulations and the prior work.Comment: 7 pages, 4 figures, 3 tables, submitted to the 2024 IEEE International Conference on Robotics and Automation. See https://github.com/conhugh/RoSSO for associated codebas

Efficient Mission Planning for Robot Networks in Communication Constrained Environments

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation. First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes

A Multi-Vehicle Cooperative Localization Approach for an Autonomy Framework

Offensive techniques produced by technological advancement present opportunities for adversaries to threaten the operational advantages of our joint and allied forces. Combating these new methodologies requires continuous and rapid development towards our own set of \game-changing technologies. Through focused development of unmanned systems and autonomy, the Air Force can strive to maintain its technological superiority. Furthermore, creating a robust framework capable of testing and evaluating the principles that define autonomy allows for the exploration of future capabilities. This research presents development towards a hybrid reactive/deliberative architecture that will allow for the testing of the principles of task, cognitive, and peer flexibility. Specifically, this work explores peer flexibility in multi-robot systems to solve a localization problem using the Hybrid Architecture for Multiple Robots (HAMR) as a basis for the framework. To achieve this task a combination of vehicle perception and navigation tools formulate inferences on an operating environment. These inferences are then used for the construction of Factor Graphs upon which the core algorithm for localization implements iSAM2, a high performing incremental matrix factorization method. A key component for individual vehicle control within the framework is the Unified Behavior Framework (UBF), a behavior-based control architecture which uses modular arbitration techniques to generate actions that enable actuator control. Additionally, compartmentalization of a World Model is explored through the use of containers to minimize communication overhead and streamline state information. The design for this platform takes on a polymorphic approach for modularity and robustness enabling future development

Local Navigation Strategies for a Team of Robots

Whenever a mobile robot has to deal with an environment that is totally or partially unknown or dynamically changing, local navigation strategies are very important for the robot to successfully achieve its goals. Unfortunately, local navigation algorithms that have been proposed in the literature offer poor performance (or even fail) whenever the geometry of the free space in which the robot is requested to operate increases its complexity. In this paper, we deal with a team composed of many robots, and we show how robots navigating within an unknown environment with local communication capabilities (only line-of-sight communication is allowed) can cooperate by helping each other to achieve their own goals

Applications of DEC-MDPs in multi-robot systems

International audienceOptimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, teams of patrolling robots, rescue-robot colonies, mine-clearance robots, et cetera.In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We rst describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decen- tralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs

INTEROPERABILITY FOR MODELING AND SIMULATION IN MARITIME EXTENDED FRAMEWORK

This thesis reports on the most relevant researches performed during the years of the Ph.D. at the Genova University and within the Simulation Team. The researches have been performed according to M&S well known recognized standards. The studies performed on interoperable simulation cover all the environments of the Extended Maritime Framework, namely Sea Surface, Underwater, Air, Coast & Land, Space and Cyber Space. The applications cover both the civil and defence domain. The aim is to demonstrate the potential of M&S applications for the Extended Maritime Framework, applied to innovative unmanned vehicles as well as to traditional assets, human personnel included. A variety of techniques and methodology have been fruitfully applied in the researches, ranging from interoperable simulation, discrete event simulation, stochastic simulation, artificial intelligence, decision support system and even human behaviour modelling