Health Aware Stochastic Planning For Persistent Package Delivery Missions Using Quadrotors

In persistent missions, taking system’s health and capability degradation into account is an essential factor to predict and avoid failures. The state space in health-aware planning problems is often a mixture of continuous vehicle-level and discrete mission-level states. This in particular poses a challenge when the mission domain is partially observable and restricts the use of computationally expensive forward search methods. This paper presents a method that exploits a structure that exists in many health-aware planning problems and performs a two-layer planning scheme. The lower layer exploits the local linearization and Gaussian distribution assumption over vehicle-level states while the higher layer maintains a non-Gaussian distribution over discrete mission-level variables. This two-layer planning scheme allows us to limit the expensive online forward search to the mission-level states, and thus predict system’s behavior over longer horizons in the future. We demonstrate the performance of the method on a long duration package delivery mission using a quadrotor in a partially-observable domain in the presence of constraints and health/capability degradation

Adaptive Multi-Vehicle Area Coverage Optimization System and Method

A mission planning system for determining an optimum use of a plurality of vehicles in searching a predefined geographic area (PGA). A discretizer subsystem may be used for sensing the capabilities of each vehicle to produce a point set defining a number of points within the PGA that the vehicles must traverse to completely search the PGA. A task allocator subsystem may determine an optimum division of the PGA into different subregions to be handled by specific ones of the vehicles, thus to minimize an overall time needed to search the PGA. A path optimizer subsystem may determine an optimum path through a particular vehicle\u27s assigned subregion to minimize the time needed for each specific vehicle to traverse its associated subregion

Scalable accelerated decentralized multi-robot policy search in continuous observation spaces

This paper presents the first ever approach for solving continuous-observation Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and their semi-Markovian counterparts, Dec-POSMDPs. This contribution is especially important in robotics, where a vast number of sensors provide continuous observation data. A continuous-observation policy representation is introduced using Stochastic Kernel-based Finite State Automata (SK-FSAs). An SK-FSA search algorithm titled Entropy-based Policy Search using Continuous Kernel Observations (EPSCKO) is introduced and applied to the first ever continuous-observation Dec-POMDP/Dec-POSMDP domain, where it significantly outperforms state-of-the-art discrete approaches. This methodology is equally applicable to Dec-POMDPs and Dec-POSMDPs, though the empirical analysis presented focuses on Dec-POSMDPs due to their higher scalability. To improve convergence, an entropy injection policy search acceleration approach for both continuous and discrete observation cases is also developed and shown to improve convergence rates without degrading policy quality.Boeing Compan

Vehicle Base Station

A system to load and unload material from a vehicle comprises a vehicle base station and an assembly to autonomously load and unload material from the vehicle

MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems

Cyber-Physical Systems (CPSs) refer to engineering platforms that rely on the inte- gration of physical systems with control, computation, and communication technologies. Autonomous vehicles are instances of CPSs that are rapidly growing with applications in many domains. Due to the integration of physical systems with computational sens- ing, planning, and learning in CPSs, hardware-in-the-loop experiments are an essential step for transitioning from simulations to real-world experiments. This paper proposes an architecture for rapid prototyping of CPSs that has been developed in the Aerospace Controls Laboratory at the Massachusetts Institute of Technology. This system, referred to as MAR-CPS (Measurable Augmented Reality for Prototyping Cyber-Physical Systems), includes physical vehicles and sensors, a motion capture technology, a projection system, and a communication network. The role of the projection system is to augment a physical laboratory space with 1) autonomous vehicles' beliefs and 2) a simulated mission environ- ment, which in turn will be measured by physical sensors on the vehicles. The main focus of this method is on rapid design of planning, perception, and learning algorithms for au- tonomous single-agent or multi-agent systems. Moreover, the proposed architecture allows researchers to project a simulated counterpart of outdoor environments in a controlled, indoor space, which can be crucial when testing in outdoor environments is disfavored due to safety, regulatory, or monetary concerns. We discuss the issues related to the design and implementation of MAR-CPS and demonstrate its real-time behavior in a variety of problems in autonomy, such as motion planning, multi-robot coordination, and learning spatio-temporal fields.Boeing Compan

Real Time Mission Planning

The different advantageous embodiments provide a system comprising a number of computers, a graphical user interface, first program code stored on the computer, and second program code stored on the computer. The graphical user interface is executed by a computer in the number of computers. The computer is configured to run the first program code to define a mission using a number of mission elements. The computer is configured to run the second program code to generate instructions for a number of assets to execute the mission and monitor the number of assets during execution of the mission

Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations

Robust environment perception is essential for decision-making on robots operating in complex domains. Intelligent task execution requires principled treatment of uncertainty sources in a robot's observation model. This is important not only for low-level observations (e.g., accelerom-eter data), but also for high-level observations such as semantic object labels. This paper formalizes the concept of macro-observations in Decentralized Partially Observable Semi-Markov Decision Processes (Dec-POSMDPs), allowing scalable semantic-level multi-robot decision making. A hierarchical Bayesian approach is used to model noise statistics of low-level classifier outputs, while simultaneously allowing sharing of domain noise characteristics between classes. Classification accuracy of the proposed macro-observation scheme, called Hierarchical Bayesian Noise Inference (HBNI), is shown to exceed existing methods. The macro-observation scheme is then integrated into a Dec-POSMDP planner, with hardware experiments running onboard a team of dynamic quadrotors in a challenging domain where noise-agnostic filtering fails. To the best of our knowledge, this is the first demonstration of a real-time, convolutional neural net-based classification framework running fully onboard a team of quadrotors in a multi-robot decision-making domain.Boeing Compan

Real Time Mission Planning

The different advantageous embodiments provide a system comprising a number of computers, a graphical user interface, first program code stored on the computer, and second program code stored on the computer. The graphical user interface is executed by a computer in the number of computers. The computer is configured to run the first program code to define a mission using a number of mission elements. The computer is configured to run the second program code to generate instructions for a number of assets to execute the mission and monitor the number of assets during execution of the mission