A practical search with Voronoi distributed autonomous marine swarms

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2022.The search for underwater threats in littoral regions is a problem that has been researched for nearly a century. However, recent developments in autonomy and robotics have made this issue more complex. The advent of capable autonomous underwater vehicles presents a 21st century flare to this traditional problem. These vehicles can be smaller, quieter, and expendable. Therefore, new methods and tactics used to detect and track these vehicles are needed. The use of a swarm of marine robots can increase the likelihood of uncovering these threats. This thesis provides various Voronoi partition-based methods to autonomously control a swarm of identically capable autonomous surface vessels in a limited coverage and tracking problem. These methods increase the probability of interdiction of an adversary vehicle crossing a defined region. The results achieved from Monte Carlo simulations demonstrate how different protocols of swarm movement can improve detection probability as compared to a stationary swarm provided the detection capability does not change. The swarm control algorithms are employed on Clearpath Heron USVs to validate the autonomy algorithms

Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm

During the last two decades, various models have been proposed for fish collective motion. These models are mainly developed to decipher the biological mechanisms of social interaction between animals. They consider very simple homogeneous unbounded environments and it is not clear that they can simulate accurately the collective trajectories. Moreover when the models are more accurate, the question of their scalability to either larger groups or more elaborate environments remains open. This study deals with learning how to simulate realistic collective motion of collective of zebrafish, using real-world tracking data. The objective is to devise an agent-based model that can be implemented on an artificial robotic fish that can blend into a collective of real fish. We present a novel approach that uses Quality Diversity algorithms, a class of algorithms that emphasise exploration over pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the state-of-the-art MAP-Elites algorithm for high dimensional search space. Results show that Quality Diversity algorithms not only outperform classic evolutionary reinforcement learning methods at the macroscopic level (i.e. group behaviour), but are also able to generate more realistic biomimetic behaviours at the microscopic level (i.e. individual behaviour).Comment: 8 pages, 4 figures, 1 tabl

Cooperative Multi Agent Search and Coverage in Uncertain Environments

In this dissertation, the cooperative multi agent search and coverage problem in uncertain environments is investigated. Each agent individually plans its desired trajectory. The agents exchange their positions and their sensors’ measurement with their neighbouring agents through a communication channel in order to maintain the cooperation objective. Different aspects of multi agent search and coverage problem are investigated. Several models for uncertain environments are proposed and the updating rules for the probability maps are provided. Each of this models is appropriate for a specific type of problems. The cooperative search mission is first converted to a decentralized multi agent optimal path planning problem, using rolling horizon dynamic programming approach which is a mid-level controller. To make cooperation between agents possible, two approximation methods are proposed to modify the objective function of agents and to take into the account the decision of other agents. The simulation results show the proposed methods can considerably increase the performance of mission without significantly increasing the computation burden. This approach is then extended for the case with known communication delay between mobile agents. The simulation results show the proposed methods can compensate for the effect of known communication delay between mobile agents. A Voronoi-based search strategy for a team of mobile agents with limited range sensors is also proposed which combines both mid-level and low-level controllers. The strategy includes the short-term objective of maximizing the uncertainty reduction in the next step, the long-term objective of distributing the agents in the environment with minimum overlap in their sensory domain, and the collision avoidance constraint. The simulation results show the proposed control law can reduce the value of uncertainty in the environment below any desired threshold. For the search and coverage problem, we first introduce a framework that includes two types of agents; search agents and coverage agents. The problem is formulated such that the information about the position of the targets is updated by the search agents. The coverage agents use this information to concentrate around the more important areas in the environment. The proposed cooperative search method, along with a well-known Centroidal Voronoi Configuration method for coverage, is used to solve the problem. The effectiveness of the proposed algorithm is demonstrated by simulation and experiment. We then introduce the “limited turn rate Voronoi diagram” and formulate the search and coverage problem as a multi-objective optimization problem with different constraints which is able to consider practical issues like minimum fuel consumption, refueling, obstacle avoidance, and collision avoidance. In this approach, there is only one type of agents which performs both search and coverage tasks. The “multi agent search and coverage problem” is formulated such that the “multi agent search problem” and “multi agent coverage problem” are special cases of this problem. The simulation results show the effectiveness of the proposed method

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

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

Robotic Surveillance and Deployment Strategies

Autonomous mobile systems are becoming more common place, and have the opportunity to revolutionize many modern application areas. They include, but are not limited to, tasks such as search and rescue operations, ad-hoc mobile wireless networks and warehouse management; each application having its own complexities and challenging problems that need addressing. In this thesis, we explore and characterize two application areas in particular. First, we explore the problem of autonomous stochastic surveillance. In particular, we study random walks on a finite graph that are described by a Markov chain. We present strategies that minimize the first hitting time of the Markov chain, and look at both the single agent and multi-agent cases. In the single agent case, we provide a formulation and convex optimization scheme for the hitting time on graphs with travel distances. In addition, we provide detailed simulation results showing the effectiveness of our strategy versus other well-known Markov chain design strategies. In the multi-agent case, we provide the first characterization of the hitting time for multiple random walkers, which we denote the "group hitting time". We also provide a closed form solution for calculating the hitting time between specified nodes for both the single and multiple random walker cases. Our results allow for the multiple random walks to be different and, moreover, for the random walks to operate on different subgraphs. Finally, we use sequential quadratic programming to find the transition matrices that generate minimal "group hitting time".Second, we consider the problem of optimal coverage with a group of mobile agents. For a planar environment with an associated density function, this problem is equivalent to dividing the environment into optimal subregions such that each agent is responsible for the coverage of its own region. We study this problem for the discrete time and space case and the continuous time and space case. For the discrete time and space case, we present algorithms that provide optimal coverage control in a non-convex environment when each robot has only asynchronous and sporadic communication with a base station. We introduce the notion of coverings, a generalization of partitions, to do this. For the continuous time and space case, we present a continuous-time distributed policy which allows a team of agents to achieve a convex area-constrained partition in a convex workspace. This work is related to the classic Lloyd algorithm, and makes use of generalized Voronoi diagrams. For both cases we provide detailed simulation results and discuss practical implementation issues

Design and Validation of Cyber-Physical Systems Through Co-Simulation: The Voronoi Tessellation Use Case

This paper reports on the use of co-simulation techniques to build prototypes of co-operative autonomous robotic cyber-physical systems. Designing such systems involves a mission-specific planner algorithm, a control algorithm to drive an agent performing its task; and the plant model to simulate the agent dynamics. An application aimed at positioning a swarm of unmanned aerial vehicles (drones) in a bounded area, exploiting a Voronoi tessellation algorithm developed in this work, is taken as a case study. The paper shows how co-simulation allows testing the complex system at the design phase using models created with different languages and tools. The paper then reports on how the adopted co-simulation platform enables control parameters calibration, by exploiting design space exploration technology. The INTO-CPS co-simulation platform, compliant with the Functional Mock-up Interface standard to exchange dynamic simulation models using various languages, was used in this work. The different software modules were written in Modelica, C, and Python. In particular, the latter was used to implement an original variant of the Voronoi algorithm to tesselate a convex polygonal region, by means of dummy points added at appropriate positions outside the bounding polygon. A key contribution of this case study is that it demonstrates how an accurate simulation of a cooperative drone swarm requires modeling the physical plant together with the high-level coordination algorithm. The coupling of co-simulation and design space exploration has been demonstrated to support control parameter calibration to optimize energy consumption and convergence time to the target positions of the drone swarm. From a practical point of view, this makes it possible to test the ability of the swarm to self-deploy in space in order to achieve optimal detection coverage and allow unmanned aerial vehicles in a swarm to coordinate with each other