Environment Characterization for Non-Recontaminating Frontier-Based Robotic Exploration

This paper addresses the problem of obtaining a concise description of a physical environment for robotic exploration. We aim to determine the number of robots required to clear an environment using non-recontaminating exploration. We introduce the medial axis as a configuration space and derive a mathematical representation of a continuous environment that captures its underlying topology and geometry. We show that this representation provides a concise description of arbitrary environments, and that reasoning about points in this representation is equivalent to reasoning about robots in physical space. We leverage this to derive a lower bound on the number of required pursuers. We provide a transformation from this continuous representation into a symbolic representation. Finally, we present a generalized pursuit-evasion algorithm. Given an environment we can compute how many pursuers we need, and generate an optimal pursuit strategy that will guarantee the evaders are detected with the minimum number of pursuers.Singapore-MIT Alliance for Research and Technology Center (Future Urban Mobility Project)United States. Air Force Office of Scientific Research (Award FA9550-08-1-0159)National Science Foundation (U.S.) (Award CNS-0715397)National Science Foundation (U.S.) (Award CCF-0726514)National Science Foundation (U.S.) (Grant 0735953

Roadmap-Based Techniques for Modeling Group Behaviors in Multi-Agent Systems

Simulating large numbers of agents, performing complex behaviors in realistic environments is a difficult problem with applications in robotics, computer graphics and animation. A multi-agent system can be a useful tool for studying a range of situations in simulation in order to plan and train for actual events. Systems supporting such simulations can be used to study and train for emergency or disaster scenarios including search and rescue, civilian crowd control, evacuation of a building, and many other training situations. This work describes our approach to multi-agent systems which integrates a roadmap-based approach with agent-based systems for groups of agents performing a wide range of behaviors. The system that we have developed is highly customizable and allows us to study a variety of behaviors and scenarios. The system is tunable in the kinds of agents that can exist and parameters that describe the agents. The agents can have any number of behaviors which dictate how they react throughout a simulation. Aspects that are unique to our approach to multi-agent group behavior are the environmental encoding that the agents use when navigating and the extensive usage of the roadmap in our behavioral framework. Our roadmap-based approach can be utilized to encode both basic and very complex environments which include multi- level buildings, terrains and stadiums. In this work, we develop techniques to improve the simulation of multi-agent systems. The movement strategies we have developed can be used to validate agent movement in a simulated environment and evaluate building designs by varying portions of the environment to see the effect on pedestrian flow. The strategies we develop for searching and tracking improve the ability of agents within our roadmap-based framework to clear areas and track agents in realistic environments. The application focus of this work is on pursuit-evasion and evacuation planning. In pursuit-evasion, one group of agents, the pursuers, attempts to find and capture another set of agents, the evaders. The evaders have a goal of avoiding the pursuers. In evacuation planning, the evacuating agents attempt to find valid paths through potentially complex environments to a safe goal location determined by their environmental knowledge. Another group of agents, the directors may attempt to guide the evacuating agents. These applications require the behaviors created to be tunable to a range of scenarios so they can reflect real-world reactions by agents. They also potentially require interaction and coordination between agents in order to improve the realism of the scenario being studied. These applications illustrate the scalability of our system in terms of the number of agents that can be supported, the kinds of realistic environments that can be handled, and behaviors that can be simulated

Sampling-Based Threat Assessment Algorithms for Intersection Collisions Involving Errant Drivers

This paper considers the decision-making problem for a vehicle crossing a road intersection in the presence of other, potentially errant, drivers. This problem is considered in a game-theoretic framework, where the errant drivers are assumed to be capable of causing intentional collisions. Our approach is to simulate the possible behaviors of errant drivers using RRT-Reach, a modi ed application of rapidly-exploring random trees. A novelty in RRT-Reach is the use of a dual exploration-pursuit mode, which allows for e cient approximation of the errant reachability set for some xed time horizon. Through simulation and experimental results with a small autonomous vehicle, we demonstrate that this threat assessment algorithm can be used in real-time to minimize the risk of collision

Sensor-Based Topological Coverage And Mapping Algorithms For Resource-Constrained Robot Swarms

Coverage is widely known in the field of sensor networks as the task of deploying sensors to completely cover an environment with the union of the sensor footprints. Related to coverage is the task of exploration that includes guiding mobile robots, equipped with sensors, to map an unknown environment (mapping) or clear a known environment (searching and pursuit- evasion problem) with their sensors. This is an essential task for robot swarms in many robotic applications including environmental monitoring, sensor deployment, mine clearing, search-and-rescue, and intrusion detection. Utilizing a large team of robots not only improves the completion time of such tasks, but also improve the scalability of the applications while increasing the robustness to systems’ failure. Despite extensive research on coverage, mapping, and exploration problems, many challenges remain to be solved, especially in swarms where robots have limited computational and sensing capabilities. The majority of approaches used to solve the coverage problem rely on metric information, such as the pose of the robots and the position of obstacles. These geometric approaches are not suitable for large scale swarms due to high computational complexity and sensitivity to noise. This dissertation focuses on algorithms that, using tools from algebraic topology and bearing-based control, solve the coverage related problem with a swarm of resource-constrained robots. First, this dissertation presents an algorithm for deploying mobile robots to attain a hole-less sensor coverage of an unknown environment, where each robot is only capable of measuring the bearing angles to the other robots within its sensing region and the obstacles that it touches. Next, using the same sensing model, a topological map of an environment can be obtained using graph-based search techniques even when there is an insufficient number of robots to attain full coverage of the environment. We then introduce the landmark complex representation and present an exploration algorithm that not only is complete when the landmarks are sufficiently dense but also scales well with any swarm size. Finally, we derive a multi-pursuers and multi-evaders planning algorithm, which detects all possible evaders and clears complex environments

Deployment algorithms for multi-agent exploration and patrolling

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 80-85).Exploration and patrolling are central themes in distributed robotics. These deployment scenarios have deep fundamental importance in robotics, beyond the most obvious direct applications, as they can be used to model a wider range of seemingly unrelated deployment objectives. Deploying a group of robots, or any type of agent in general, to explore or patrol in dynamic or unknown environments presents us with some fundamental conceptual steps. Regardless of the problem domain or application, we are required to (a) understand the environment that the agents are being deployed in; (b) encode the task as a set of constraints and guarantees; and (c) derive an effective deployment strategy for the operation of the agents. This thesis presents a coherent treatment of these steps at the theoretical and practical level. First, we address the problem of obtaining a concise description of a physical environment for robotic exploration. Specifically, we aim to determine the number of robots required to be deployed to clear an environment using non-recontaminating exploration. We introduce the medial axis as a configuration space and derive a mathematical representation of a continuous environment that captures its underlying topology and geometry. We show that this representation provides a concise description of arbitrary environments, and that reasoning about points in this representation is equivalent to reasoning about robots in physical space. We leverage this to derive a lower bound on the number of required pursuers. We provide a transformation from this continuous representation into a symbolic representation. We then present a Markov-based model that captures a pickup and delivery (PDP) problem on a general graph. We present a mechanism by which a group of robots can be deployed to patrol the graph in order to fulfill specific service tasks. In particular, we examine the problem in the context of urban transportation, and establish a model that captures the operation of a fleet of taxis in response to incident customer arrivals throughout the city. We consider three different evaluation criteria: minimizing the number of transportation resources for urban planning; minimizing fuel consumption for the drivers; and minimizing customer waiting time to increase the overall quality of service. Finally, we present two deployment algorithms for multi-robot exploration and patrolling. The first is a generalized pursuit-evasion algorithm. Given an environment we can compute how many pursuers we need, and generate an optimal pursuit strategy that will guarantee the evaders are detected with the minimum number of pursuers. We then present a practical patrolling policy for a general graph. We evaluate our policy using real-world data, by comparing against the actual observed redistribution of taxi drivers in Singapore. Through large-scale simulations we show that our proposed deployment strategy is stable and improves substantially upon the default unmanaged redistribution of taxi drivers in Singapore.by Mikhail Volkov.S.M

TOGGLE PRM: A SIMULTANEOUS MAPPING OF CFREE AND COBSTACLE FOR USE IN PROBABILISTIC ROADMAP METHODS

Motion planning for robotic applications is difficult. This is a widely studied problem in which the best known deterministic solution is doubly exponential in the dimensionality of the problem. A class of probabilistic planners, called sampling-based planners, have shown much success in this area, but still show weakness for planning in difficult parts of the space, namely narrow passages. The problem space is made of two subsets - free space and collision space, representing valid and invalid robot positions. A general method for probabilistic planners is the probabilistic roadmap method (PRM) which maps only free space to find a solution. This thesis proposes a new strategy, Toggle PRM, for probabilistic roadmap planners, which simultaneously maps both free space and collision space in order to guide the solution more efficiently. All sampled robotic configurations are kept in two separate maps. When the connection attempts between configurations in one roadmap fail, the witness to the failure is retained as a configuration in the opposing roadmap. By mapping both spaces, sampling density in narrow passages is greatly increased. A theoretical and experimental analysis of Toggle PRM shows the independence from the volume of a narrow passage and the volume of the obstacles surrounding the passage for sampling, overcoming a previous challenge of probabilistic planning. Additionally, Toggle PRM has increased efficiency as compared to other common sampling techniques in various motion planning problems because of this improved sampling in narrow passages

Effects of Dynamically Weighting Autonomous Rules in a UAS Flocking Model

Within the U.S. military, senior decision-makers and researchers alike have postulated that vast improvements could be made to current Unmanned Aircraft Systems (UAS) Concepts of Operation through inclusion of autonomous flocking. Myriad methods of implementation and desirable mission sets for this technology have been identified in the literature; however, this thesis posits that specific missions and behaviors are best suited for autonomous military flocking implementations. Adding to Craig Reynolds\u27 basic theory that three naturally observed rules can be used as building blocks for simulating flocking behavior, new rules are proposed and defined in the development of an autonomous flocking UAS model. Simulation validates that missions of military utility can be accomplished in this method through incorporation of dynamic event- and time-based rule weights. Additionally, a methodology is proposed and demonstrated that iteratively improves simulated mission effectiveness. Quantitative analysis is presented on data from 570 simulation runs, which verifies the hypothesis that iterative changes to rule parameters and weights demonstrate significant improvement over baseline performance. For a 36 square mile scenario, results show a 100% increase in finding targets, a 40.2% reduction in time to find a target, a 4.5% increase in area coverage, with a 0% attribution rate due to collisions and near misses

Constraint Aware Behavior in Multi-Robot Systems

In this work we present a behavioral modeling framework that accounts for a battery constraint. This framework allows for a user to model robot teams of varying configuration performing com- mon robotic tasks such as exploration or going to user specified goals. The focus of this work is on how to model a constraint aware behavior and how assistance can be requested by and provided from a robot team. We show experimental results in simulated environments and identify trends that can be seen given a robot team configuration. We also discuss how this system can be adapted to different environments and different constraints. Our system can be setup to allow for differ- ent number of workers and helpers. The charging station, battery level and the behaviors of these agents can also be varied. We discuss the affect of these different policies on the performance of the workers. The performance is measured by the number of times the environment area is covered. In conclusion we would measure the performance based on the number of times the environment is covered by the agents

Multi-vehicle Framework for the Development of Robotic Games: the Marco Polo Case

This thesis presents a multi-vehicle platform and framework for robotics education and research. The framework has been designed primarily as a tool for teaching children about engineering in general and robotics in particular. The framework is composed of a unique combination of hardware components and software libraries that allow users to easily design and implement sophisticated robotics behaviors. Several example games are presented including ``Obstacle Course," ``Scavenger Hunt," ``Robot Jeopardy," and ``Marco Polo." This thesis also introduces ``Marco Polo" as a robotics problem that mimics the pursuit-evasion game often played by children in swimming pools. Specifically, the question of finding an optimal pursuit strategy under the condition of intermittent communication is addressed. Finally, a problem related to ``Marco Polo" involving a multi-agent sensor network optimally placed in an environment for the purpose of detecting and intercepting intruders is presented together with a proposed solution methodology and simulation and experimental results.School of Electrical & Computer Engineerin

A Task Hand-Off Framework for Multi-Robot Systems

Multi-robot systems have many uses such as cleaning, exploration, search and rescue. These robots operate under constraints such as communication, battery etc. In this thesis, we provide a method by which the robots can hand-off their current task to a new robot so that the given task can be continued without interruption. It is assumed that the task can be handed off to any other robot without losing the progress on the task. In the task hand-off framework, the robots complete as much of the task as possible before trying to replenish their resources (e.g., refuel). The robots must also make sure that the task is handed over to another robot before they go back to refuel. We demonstrate the task hand-off framework in the context of a battery constraint. The robots hand-off their current task once they are low on battery. The robots are divided into helpers and workers. The workers are the ones that perform the given task while the helpers wait at charging locations. Once a worker determines it is running out of battery it calls for help and switches behaviors with a helper. The new worker then takes over the task. This framework allows a user to model robot teams performing common robotic tasks such as exploration, coverage or any other task where the task can be easily handed-off without losing any progress on the task. We also present a simple priority based inter-robot contention resolution algorithm using motion replanning to avoid inter-robot collisions. Each robot is assigned a priority. Whenever the robots are close to each other, the lower priority robots halt and the highest priority robot replans a path around the robots by considering them as additional robots. We demonstrate the task hand-off framework approach using a physics based simulator that is built on top of a physics engine and also using physical hardware. The physical hardware consists of multiple iRobot Create robots with an onboard ASUS Netbook. We provide results from room 407 of the Harvey Bum Bright Building at Texas A&M University. We show that the tasks get completed faster with task hand-off than when task hand-off was not allowed