An Optimal Control Approach for the Data Harvesting Problem

We propose a new method for trajectory planning to solve the data harvesting problem. In a two-dimensional mission space, $N$ mobile agents are tasked with the collection of data generated at $M$ stationary sources and delivery to a base aiming at minimizing expected delays. An optimal control formulation of this problem provides some initial insights regarding its solution, but it is computationally intractable, especially in the case where the data generating processes are stochastic. We propose an agent trajectory parameterization in terms of general function families which can be subsequently optimized on line through the use of Infinitesimal Perturbation Analysis (IPA). Explicit results are provided for the case of elliptical and Fourier series trajectories and some properties of the solution are identified, including robustness with respect to the data generation processes and scalability in the size of an event set characterizing the underlying hybrid dynamic system

A global optimal control methodology and its application to a mobile robot model

A global optimal control algorithm is developed and applied to an omni-directional mobile robot model. The aim is to search and find the most intense signal source among other signal sources in the operation region of the robot. In other words, the control problem is to find the global extremum point when there are local extremas. The locations of the signal sources are unknown and it is assumed that the signal magnitudes are maximum at the sources and their magnitudes are decreasing away from the sources. The distribution characteristics of the signals are unknown, i.e. the gradients of the signal distribution functions are unknown. The control algorithm also doesn't need any position measurement of the robot itself. Only the signal magnitude should be measured via a sensor mounted on the robot. The simulation study shows the performance of the controller.Publisher's Versio

An Intelligent Framework for Energy-Aware Mobile Computing Subject to Stochastic System Dynamics

abstract: User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy. Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Modern solutions of aerial ion field distributions

UK: Робота присвячена опису алгоритму аероіонного розповсюдження від штучних джерел аероіонізації у приміщеннях з комбінованою розрахунковою площиною та можливість його реалізації на мобільних пристроях. Запропонований алгоритм дозволяє моделювання розподіл аероіонного поля у графічному вигляді для зазначених приміщень, визначати зони аероіонного комфорту і дискомфорту і на основі цього виконувати управляючі впливи на картину аероіонного розподілення у заданому середовищі. Управління аероіонним розподіленням ґрунтується на використанні геометричної моделі розподілення від’ємних аероіонів і здійснюється шляхом оптимального розміщення джерел аероіонного випромінювання в заданій робочій зоні. Дана система реалізована на мобільних засобах з операційною системою Android. EN: The paper considers the simulation of distribution of dispersed aerial ions in space from artificial sources of aerial ionization using the Android Studio software environment. The algorithm for determining and providing optimal aerial ion distribution from artificial aerial ionization sources in space from one or more aerial ionizers is proposed. Ensuring of optimal aerial ion distribution is based on the geometric model of distribution of dispersed aerial ions and is supported by optimal placement of aerial ion radiation sources in a given working area. The peculiarity of the proposed algorithm is the calculation of aerial ion distribution for the combined breathing zone, when a horizontal zone of breathing becomes an inclined one. The proposed algorithm is the basis for software of the automated system for calculating the optimal aerial ion mode of the working environment. The proposed software system is a closed system and performs information, control and auxiliary functions. The software system comprises two modules: a module of input parameters and a modeling module, which allows to simulate the aerial ion distribution in space from artificial sources of aerial ionization. The module of input parameters serves to initialize the input parameters, such as the length and width of the calculated zone, the type, capacity and number of ionizers. The simulator module calculates the aerial ion distribution of one and more sources of aerial ion distribution in a given plane at two modes: at given (fixed) coordinates of the source and in an interactive mode when a user is able to freely adjust (move the screen) the location of the sources. The output block of the results serves to output the calculation results: graphically and numerically, on the screen of the device. The data transfer unit allows to get simulation results of isolines on a plane to be generated as a report and corrected by mail online. The purpose of this work is to develop an algorithm for determining aerial ion distribution on a combined calculated plane for software on the Android Studio basis. The paper presents the algorithm for determining the aerial on distribution in the working zones with a combined breathing zone, which is implemented using the Java programming language in the Android Studio (AS) development environment. The software module is developed in Android Studio for the Android (version 4.2 - 5.0) operating system The developed system is mobile and allows a user to use it at any time from any mobile device with the installed Android version. This advantage of the mobile system is a scientific novelty in this problem area. The system guarantees the automation of the process of aerial ion devices placement effectively, resulting in placement of working places in the most favorable locations for work

ENG 1001G-012: Composition and Language

Koordiniranim gibanjem više mobilnih robota umjesto jednoga može se postići značajno povećanje brzine, točnosti i učinkovitosti izvođenja zadataka u velikom broju primjena. Primjeri takvih primjena mogu se naći u inteligentnim transportnim sustavima, zadacima prekrivanja prostora, kooperativnom prijenosu tereta, logističkim sustavima, proizvodnim pogonima itd. Sigurno gibanje mobilnih robota u formaciji konvoj u inteligentnim transportnim sustavima moguće je ako se zadovolji stabilnost konvoja. Stabilnost konvoja modela razlike brzine postignuta je odgođenim povratnim informacijama koje uključuje razliku trenutačne i odgođene udaljenosti između mobilnih robota i razliku trenutačne i odgođene brzine mobilnog robota. Postizanje izvršenja zadataka prekrivanja prostora i kooperativnog prijenosa tereta u najkraćem mogućem vremenu vrlo je izazovan problem. Riješen je planiranjem referentne vremenski optimalne trajektorije formacije mobilnih robota u zakrivljenom koordinatnom sustavu pomoću algoritma optimalnog skaliranja u vremenu, koji vremenski optimalno izmjenjuje najveća i najmanja ubrzanja formacije. Koordinirano gibanje više mobilnih robota u istom radnom prostoru u logističkim sustavima i proizvodnim pogonima ostvareno je primjenom pristupa razdvojenog planiranja gibanja uz unaprijed definirane putanje mobilnih robota i dodavanjem početnog vremena kašnjenja izračunanim predefiniranim trajektorijama mobilnih robota. Slijeđenje zadanih trajektorija za mobilne robote s diferencijalnim pogonom postignuto je algoritmom s nepromjenjivim koeficijentima zasnovanim na kinematičkom modelu mobilnog robota, nelinearnoj dinamici pogreške slijeđenja i Lyapunovljevoj teoriji stabilnosti.Nowadays, the use of mobile robots has become increasingly important for many purposes: medical services, civil transport, domestic work, military, commercial cleaning, sales of consumer goods, agricultural and forestry work, browsing hard to reach and dangerous areas for human, digging ore, construction work, loading/unloading and manipulating materials, outer space and underwater research, space supervision, entertainment etc. New challenges in the field of mobile robotics include controlling a group of mobile robots in an efficient way. Inspiration is found in nature where many animal communities apply cooperative behaviour patterns to achieve a common goal. The most common arguments for the application of a group of mobile robots in comparison to only one mobile robot are increase in speed and accuracy and efficiency of performing tasks. A group of mobile robots is able to execute tasks impossible for a single mobile robot, e.g. transporting or repositioning large objects. Also, a group of mobile robots is more robust to failure than a single mobile robot (one mobile robot can take over the tasks of another mobile robot in case of failure). Since mobile robots can have a variety of roles, the same group of mobile robots can be employed for many different objectives, e.g. intelligent transport systems, areas coverage, cooperative transportation, logistics, etc. In order to achieve the benefits of multi-robot mobile systems it is necessary to solve additional problems in terms of control, which are not present in systems with a single mobile robot, e.g. communication between robots, distribution of tasks, assigning priorities, taking into account kinematic and dynamic constraints of all the mobile robots in the group to successfully plan feasible paths and trajectories for all of them, etc. A mobile robot is a mechanical system and as such is subject to motion equations that follow the laws of physics. Therefore, for each movement, in accordance with kinematic and dynamic constraints, there must be at least one set of input values that affect the motion. Kinematic constraints of mobile robots are the result of limitations in movement of drive wheels and drive configurations. Dynamic constraints of mobile robots refer to limiting the permitted velocity and acceleration, and they are caused by the actuator limitations. Motion control of mobile robots is a complex process. It includes working task planning, reference path and trajectory planning, and tracking the reference trajectory. The focus of this thesis is put on multi-robot land mobile systems and applications in intelligent traffic systems, areas coverage, cooperative transportation of large objects and coordination in the common working environment. All algorithms presented in this thesis are first tested in Matlab® and then experimentally validated on real robots. Experiments were performed using robot soccer platform at the Department of Control and Computer Engineering, Faculty of Electrical Engineering and Computing, University of Zagreb, which is ideal for testing various mobile robot algorithms. It consists of a team of five radio-controlled micro robots of size 0.075 m cubed with differential drive. The playground is of size 2.2×1.8 m. Above the centre of the playground, Basler a301fc IEEE-1394 Bayer digital colour camera with resolution of 656×494 pixels and with maximal frame rate of 80 fps is mounted perpendicular to the playground. The height of the camera to the playground is 2.40 m. A wide angle 6 mm lens is used. Although robot soccer platform is very practical for experiments with formations of mobile robots, it has some technical limitations: (i) relatively large noise in the measured position and velocity of the robot; (ii) delay in the communication between the control computer and microprocessors of the mobile robots and (iii) delay in measurements due to vision (the time required to grab the image from the camera and the time required for image processing). Efficient motion of mobile robots (vehicles) in a convoy formation is very important in transportation of people and goods. The key research problem is ensuring the string stability. In the background of this study are traffic safety and adaptive cruise control system in modern vehicles. The adaptive cruise control system primarily aims to reduce the driver’s effort, which is achieved by controlling the velocity of vehicle according to a predefined control law. Typically, sensors such as radar and lidar are used to measure the relative distance and relative velocity between vehicles. The working principle of adaptive cruise control system is based on these informations. String stability refers to the stability of a series of ''interconnected'' vehicles. The attribute ''interconnected'' does not indicate the physical connection of the vehicles but vehicles moving in a convoy formation where every vehicle in the convoy follows the preceding vehicle at a safe distance. The behaviour of each vehicle in the convoy must be such that the oscillatory behaviour due to a change of velocity of the leading vehicle of the convoy does not increase towards the end of the convoy. Otherwise, unstable convoy may cause collisions between vehicles. In this thesis, a deterministic microscopic full velocity difference model is considered. In the case where the values of model parameters do not meet the requirement of string stability, full velocity difference model should be expanded with additional control signals in order to satisfy string stability. Two additional control signals are based on the difference of the current and the delayed (in a defined time in the past) relative distance between the vehicle, and the difference of the current and the delayed vehicle velocity. The proposed method for achieving string stability is based on the delayed-feedback control. The delayed-feedback control is one of the feasible methods of controlling unstable and chaotic motions. The string stability has been examined by ∞–norm of transfer function of distance between vehicles. The stability of the transfer function is examined using a delay-independent stability criterion, which significantly simplifies the stability test, since characteristic equation of transfer function is transcendental and has infinite number of roots. It should be noted that the adaptive cruise control system is studied in one-dimensional space, e.g. vehicles in the convoy moving on an infinitely long straight road. Using formations of mobile robots, it is possible to significantly increase efficiency of numerous tasks such as areas coverage or cooperative transportation of large objects. However, achieving minimal time of executing tasks is a very challenging problem. The formation of mobile robots observed in this study is most similar to the formation with a leading mobile robot, but instead of a leading mobile robot the reference point of the formation and its reference path are defined. The formation of mobile robots is defined in the curved coordinate system so mobile robots maintain distance in relation to a reference point formation in this coordinate system. The curvature of the coordinate system is actually instantaneous centre of curvature of the formation’s reference trajectory. This results in changing the formation shape during cornering. This has implications in possible applications. For example, a square formation of mobile robots cannot handle rectangular solid object as it moves in curved path. The dynamic model of the mobile robot takes into account intrinsic constraints originating from the robot actuator limits and extrinsic constraints resulting from the limited adhesion force between ground and wheels of the mobile robot such as wheel slip and tip over of the mobile robot. Both types of constraints are important for planning at high velocities. The problem of planning reference time-optimal trajectory for a formation of mobile robot is solved by decoupled approach which could be defined as a problem of planning reference time-optimal trajectories after predefined smooth G2 continuous path of the reference point of formation. First, a path of formation of mobile robots in workspace is found, and then a velocity profile in accordance with the specified criteria and dynamic constraints of mobile robots is calculated. It is assumed that the path of each mobile robot in the formation is feasible and that kinematic constraints of the mobile robot on the path are satisfied. G2 continuous path provides the ability to pass the mobile robot trajectory with a non-zero velocity, thus enabling fast movement of the mobile robot without stopping. G2 path consists of straight lines and clothoids. Planning reference time-optimal trajectory for the formation of mobile robots is based on the optimal time-scaling algorithm providing that formation always moves at its maximum or minimum accelerations. This means that at least one mobile robot of the formation moves at its maximum or minimum accelerations. Emphasis is placed on static formations of mobile robots. Also, one example of dynamic formation of the mobile robots was shown. When multiple mobile robots independently perform tasks in the same workspace, the key problem is the planning collision free coordinated motion of multiple mobile robots sharing the same workspace. This problem is solved using the decoupled approach. First step is to plan individual path of each mobile robot, e.g. predefined path, using methods for planning paths for individual mobile robots in workspace with static obstacles. Second step is to plan a velocity profile for each mobile robot on its predefined path, e.g. predefined trajectory. Third step is to modify predefined trajectory for each mobile robot making sure that collisions between mobile robots in the workspace are avoided. To successfully coordinate motion of multiple mobile robots, a common approach is to assign priority level to each of them. A mobile robot with highest priority takes into account only static obstacles while other mobile robots have to take into account also dynamic obstacles, which are mobile robots with higher priorities. Dynamic obstacles avoidance is based on avoiding time-obstacles in a collision map. Time-obstacles are constructed based on the time mobile robots would spend in possible area of collision. A lower priority mobile robot efficiently avoids time-obstacles, e.g. mobile robot with higher priority level, by inserting a calculated start-up delay time to its predefined trajectory along the predefined path. By applying the same principle to all lower priority robots, a collision free motion coordination of multiple mobile robots can be achieved. It should be noted that the predefined paths of mobile robots do not change during their movements. The change is only in the allocation of time of motion of mobile robots on their predefined paths. A proposed method is computationally fast and intuitive and ensures that always only one mobile robot can be in the possible area of collision. The input of the trajectory tracking algorithm is a feasible planned trajectory, where feasibility refers to the ability of a mobile robot to actually track the planned trajectory. This means that the planned trajectory respects kinematic and dynamic constraints of the mobile robot. The trajectory tracking algorithm is needed because in reality there are many sources of potential errors such as imperfections of a mobile robot model or external disturbances like uneven ground, delayed command control, an imperfect measurement of the state of mobile robots and so on. In this thesis, it is proposed a trajectory tracking algorithm with constant gains for nonholonomic mobile robot with differential drive, which is based on kinematic model of mobile robot, nonlinear dynamics of the tracking error and Lyapunov stability theory. The scientific contributions of the thesis are: 1. Algorithm for mobile robot control in the formation of a convoy that ensures string stability using the delayed-feedback control, based on the difference between current and delayed distance between mobile robots and the difference between current and delayed velocity of a mobile robot. 2. Algorithm for planning smooth time optimal trajectory for a formation of mobile robots with kinematic and dynamic constraints on predefined paths. 3. Algorithm for planning of collision free motion coordination of multiple mobile robots sharing the same workspace on predefined paths assigning initial delay time for predefined trajectories of mobile robots. 4. Algorithm for trajectory tracking for mobile robots with kinematic and dynamic constraints based on Lyapunov stability theory

Dual Control for Exploitation and Exploration (DCEE) in Autonomous Search

This paper proposes an optimal autonomous search framework, namely Dual Control for Exploration and Exploitation (DCEE), for a target at unknown location in an unknown environment. Source localisation is to find sources of atmospheric hazardous material release in a partially unknown environment. This paper proposes a control theoretic approach to this autonomous search problem. To cope with an unknown target location, at each step, the target location is estimated by Bayesian inference. Then a control action is taken to minimise the error between future robot position and the hypothesised future estimation of the target location. The latter is generated by hypothesised measurements at the corresponding future robot positions (due to the control action) with the current estimation of the target location as a prior. It shows that this approach can take into account both the error between the next robot position and the estimate of the target location, and the uncertainty of the estimate. This approach is further extended to the case with not only an unknown source location, but also an unknown local environment (e.g. wind speed and direction). Different from current information theoretic approaches, this new control theoretic approach achieves the optimal trade-off between exploitation and exploration in a unknown environment with an unknown target by driving the robot moving towards estimated target location while reducing its estimation uncertainty. This scheme is implemented using particle filtering on a mobile robot. Simulation and experimental studies demonstrate promising performance of the proposed approach. The relationships between the proposed approach, informative path planning, dual control, and classic model predictive control are discussed and compared

Resilience-driven planning and operation of networked microgrids featuring decentralisation and flexibility

High-impact and low-probability extreme events including both man-made events and natural weather events can cause severe damage to power systems. These events are typically rare but featured in long duration and large scale. Many research efforts have been conducted on the resilience enhancement of modern power systems. In recent years, microgrids (MGs) with distributed energy resources (DERs) including both conventional generation resources and renewable energy sources provide a viable solution for the resilience enhancement of such multi-energy systems during extreme events. More specifically, several islanded MGs after extreme events can be connected with each other as a cluster, which has the advantage of significantly reducing load shedding through energy sharing among them. On the other hand, mobile power sources (MPSs) such as mobile energy storage systems (MESSs), electric vehicles (EVs), and mobile emergency generators (MEGs) have been gradually deployed in current energy systems for resilience enhancement due to their significant advantages on mobility and flexibility. Given such a context, a literature review on resilience-driven planning and operation problems featuring MGs is presented in detail, while research limitations are summarised briefly. Then, this thesis investigates how to develop appropriate planning and operation models for the resilience enhancement of networked MGs via different types of DERs (e.g., MGs, ESSs, EVs, MESSs, etc.). This research is conducted in the following application scenarios: 1. This thesis proposes novel operation strategies for hybrid AC/DC MGs and networked MGs towards resilience enhancement. Three modelling approaches including centralised control, hierarchical control, and distributed control have been applied to formulate the proposed operation problems. A detailed non-linear AC OPF algorithm is employed to model each MG capturing all the network and technical constraints relating to stability properties (e.g., voltage limits, active and reactive power flow limits, and power losses), while uncertainties associated with renewable energy sources and load profiles are incorporated into the proposed models via stochastic programming. Impacts of limited generation resources, load distinction intro critical and non-critical, and severe contingencies (e.g., multiple line outages) are appropriately captured to mimic a realistic scenario. 2. This thesis introduces MPSs (e.g., EVs and MESSs) into the suggested networked MGs against the severe contingencies caused by extreme events. Specifically, time-coupled routing and scheduling characteristics of MPSs inside each MG are modelled to reduce load shedding when large damage is caused to each MG during extreme events. Both transportation networks and power networks are considered in the proposed models, while transporting time of MPSs between different transportation nodes is also appropriately captured. 3. This thesis focuses on developing realistic planning models for the optimal sizing problem of networked MGs capturing a trade-off between resilience and cost, while both internal uncertainties and external contingencies are considered in the suggested three-level planning model. Additionally, a resilience-driven planning model is developed to solve the coupled optimal sizing and pre-positioning problem of MESSs in the context of decentralised networked MGs. Internal uncertainties are captured in the model via stochastic programming, while external contingencies are included through the three-level structure. 4. This thesis investigates the application of artificial intelligence techniques to power system operations. Specifically, a model-free multi-agent reinforcement learning (MARL) approach is proposed for the coordinated routing and scheduling problem of multiple MESSs towards resilience enhancement. The parameterized double deep Q-network method (P-DDQN) is employed to capture a hybrid policy including both discrete and continuous actions. A coupled power-transportation network featuring a linearised AC OPF algorithm is realised as the environment, while uncertainties associated with renewable energy sources, load profiles, line outages, and traffic volumes are incorporated into the proposed data-driven approach through the learning procedure.Open Acces

Control and optimization approaches for power management in energy-aware battery-powered systems

Thesis (Ph.D.)--Boston UniversityThis dissertation is devoted to the power management of energy-aware battery-powered systems (BPSs). Thanks to the popularization of wireless and mobile devices, BPSs are increasingly and widely used. However, the development of BPSs is hindered by the short lifetime of batteries and limited accessibility to charging sources. The first part of this dissertation focuses on the power management of BPSs based on an analytical non-ideal battery model, the Kinetic Battery Model (KBM). How to control discharge and recharge processes of the BPS to optimize the system performance is investigated. Problems for single-battery systems and multi-battery systems are studied. In the single-battery case, the calculus of variations approach gives analytical solutions to the cases with both fully and partially available rechargeability. The results are consistent with the ones derived under a different non-ideal battery model, demonstrating the validity of the solution to the general non-ideal battery systems. In the multi-battery systems, in order to maximize the minimum terminal residual energy among all batteries, the similar methodology is employed to show an optimal policy making equal terminal energy values of all batteries as long as such a policy is feasible, which simplifies the derivations of the solution. Furthermore, the KBM is introduced into a routing problem for lifetime maximization in wireless sensor networks (WSNs). The solution not only preserves the properties of the problem based on an ideal battery model but also shows the applicability of the KBM to large network problems. The second part of the dissertation is focused on BPV systems. First, the energy-aware behavior of electric vehicles (EVs) is studied by addressing two motion control problems of an EV, (a) cruising range maximization and (b) traveling time minimization, based on an EV power consumption model. Approximate controller structures are proposed such that the original optimal control problems are transformed into nonlinear parametric optimization problems, which are much easier to solve. Finally, motivated by the significant role of recharging in BPVs, the vehicle routing problem with energy constraints is investigated. Optimal routes and recharging times at charging stations are sought to minimize the total elapsed time for vehicles to reach the destination. For a single vehicle, a mixed-integer nonlinear programming (MINLP) problem is formulated. A decomposition method is proposed to transform the MINLP problem into two simpler problems respectively for the two types of decision variables. Based on this, a multi-vehicle routing problem is studied using a flow model, where traffic congestion effects are considered are included. Similar approaches to the single vehicle case decompose the coupling of the decision variables, thus making the problem easier to solve

Magnetworks: how mobility impacts the design of Mobile Networks

In this paper we study the optimal placement and optimal number of active relay nodes through the traffic density in mobile sensor ad-hoc networks. We consider a setting in which a set of mobile sensor sources is creating data and a set of mobile sensor destinations receiving that data. We make the assumption that the network is massively dense, i.e., there are so many sources, destinations, and relay nodes, that it is best to describe the network in terms of macroscopic parameters, such as their spatial density, rather than in terms of microscopic parameters, such as their individual placements. We focus on a particular physical layer model that is characterized by the following assumptions: i) the nodes must only transport the data from the sources to the destinations, and do not need to sense the data at the sources, or deliver them at the destinations once the data arrive at their physical locations, and ii) the nodes have limited bandwidth available to them, but they use it optimally to locally achieve the network capacity. In this setting, the optimal distribution of nodes induces a traffic density that resembles the electric displacement that will be created if we substitute the sources and destinations with positive and negative charges respectively. The analogy between the two settings is very tight and have a direct interpretation in wireless sensor networks

Interactive Planning and Sensing for Aircraft in Uncertain Environments with Spatiotemporally Evolving Threats

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil