Autonomous Robot Control Via Autonomy Levels

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Autonomous Robot Control Via Autonomy Levels Autonomous Robot Control via Autonomy Levels Lawrence A.M. Bush and Andrew Wang Autonomous systems need to exhibit intelligent In the future, unmanned systems will behaviors while performing complex operations. » gain decision-making intelligence that These systems will be deployed in clusters to enables them to autonomously operate in clusters to perform collaborative tasks. perform collaborative missions with human For successful field deployment of unmanned systems, supervisors. Autonomous systems will take on operators will need confidence that autonomous decision expanded roles, requiring higher-order decision- making leads to optimal behaviors in uncertain environ- making capabilities supporting autonomous ments. Adjustable autonomy technologies, concepts, and simulation environments evaluating teaming behaviors mission planning, resource allocation, route will enable researchers to develop these systems. Net- planning, and scheduling and execution of work and sensing advances have created the opportunity coordinated tasks. for increased mission performance, but at the expense of greater complexity in sensor coordination and analysis. Current unmanned systems are typically teleoperated and are labor intensive, relying on human operators and their decision-making capabilities to perform mission tasks. Today, mission and sensing complexity that are man- aged through increased automation of lower-level func- tions (e.g., mechanical-system controls) help operators focus on higher-level decisions. The lower-order decision- making algorithms under development include those for waypoint following and collision detection and avoidance. Some of these algorithms have been incorporated in oper- ational platforms. Deployment A team of unmanned aerial and ground vehicles might be deployed in a natural disaster relief scenario as depicted in Figure 1. In this example, a major earthquake has dam- aged buildings, roads, and bridges, and disrupted commu- nication, power, and water distribution services. A nuclear 60 LINCOLN LABORATORY JOURNAL n VOLUME 22, NUMBER 2, 2017 LAWRENCE A.M. BUSH AND ANDREW WANG energy facility also requires an immediate response. Relief platform route plans for optimum survey coverage, and convoys need to deliver supplies throughout the affected scheduling algorithms would determine flight or road area. A team of autonomous aerial and ground scouts plans for autonomous scout vehicles to follow. The exe- supervised by operators in a mission logistics vehicle is cution manager algorithm would see that the mission is dispatched to survey the damage. The algorithms need to performed and goals are met. determine the safest path for the relief convoy to travel to It is the job of the logistics planner algorithm to reach its destination in the minimum amount of time. choose the actual sequence of waypoints so that it bal- Figure 2 presents an architecture for functions that a ances and reduces the risk among each component of the multiagent autonomous team would need to perform in mission. However, to make well-informed decisions, the this scenario. Human mission operators in the logistics planner will need the scouts to gather additional data on vehicle would enter high-level goals, system constraints, areas the logistics vehicle may cross in the future. The and policies into the system. Resource allocation algo- scout dispatcher algorithm determines where to send rithms would be employed to develop a system composi- the scouts, given the plans currently considered by the tion based on the mission objectives and the appropriate logistics planner algorithms. The execution of each plan available resources, including which platforms to deploy carries with it some risk uncertainty, which is trans- and their sensor payloads, processors, and communica- formed into map uncertainty. In other words, the scout tion capabilities. Planning algorithms would develop dispatcher determines map locations that contribute Autonomous aerial scouts Supply convoy Cross cueing and tasking Autonomous ground scout Mission logistics vehicle X Destination X Destination FIGURE 1. In this earthquake relief scenario, a convoy needs to deliver supplies to those in need by the safest path. A team of aerial and ground scouts supervised by operators in a mission logistics vehicle is dispatched to survey the damage and provide real-time route safety information. VOLUME 22, NUMBER 2, 2017 n LINCOLN LABORATORY JOURNAL 61 AUTONOMOUS ROBOT CONTROL VIA AUTONOMY LEVELS most to uncertainty. It then tasks scouts to survey these and technologies are the subjects of current research areas to disambiguate candidate plans. Each scout’s to optimize planning in uncertain environments. As planner accepts as inputs these areas and a time limit shown in Figure 2, one strategy is to equip functional for reporting results on each area. The executive must modules with risk-assessment capabilities. This strat- also receive the current risk estimate or “belief” for the egy would allow adjustment of the system’s autonomy relevant area. The planner runs an adaptive sampling levels according to individual risk acceptance. At any algorithm that is trained to calculate the flight path time, an operator can monitor autonomy algorithm that achieves the highest expected information gain decisions, augment or modify algorithm inputs, or take within the time allotted. As sensor measurements are over full manual control of selected logistics vehicles. analyzed and shared, the belief update module incor- Another strategy is the incorporation of rigorous porates them into the risk belief, and at the end of a verification processes within the autonomous system sensing task, the scout reports the updated risk belief algorithm architecture. Algorithm results or plan fea- to the logistics executive. sibility would be verified against operator risk accep- For successful system field deployment, operators tance as well as mission resource costs and system need confidence that autonomous decision making performance or autonomous behavior expectations. If leads to optimal behaviors, especially when carried conditions are not met, the system may request new out in uncertain environments. A number of concepts plans or request/task subgoals to reduce uncertainty, ARCAL algorithms Risk monitoring Logistics executive Scout executive Proposed Tasked Logistics plans Scout area Scout planner dispatcher planner Adjustable autonomy Risk Belief Scout Road-map info Risk-belief update belief planner map update Situational awareness Logistics vehicle commands Sensor measurements Operator Mission Scout interface vehicle vehicle Mission Scout goals vehicle commands FIGURE 2. Within the generalized multiagent autonomy architecture of the Autonomous Robot Control via Autonomy Levels (ARCAL) system, the logistics executive contains several submodules. Two of them are the high-level logistics planner and the low-level road-map planner, each containing a risk-assessment functionality that operates on the risk- belief map. Together, these submodules determine the course of action for the logistics vehicle. The logistics planner accepts mission goals from the operator and generates sequences of waypoints, producing a high-level road map that will achieve the mission goals. Then, the road-map planner finds the actual path taken between waypoints. 62 LINCOLN LABORATORY JOURNAL n VOLUME 22, NUMBER 2, 2017 LAWRENCE A.M. BUSH AND ANDREW WANG including tasking additional scout runs for surveillance of decisions: what future actions are optimal and how information, satellite imagery, or other sensor data. can the human operator best be engaged? Both of these The performance of unmanned systems in collab- decisions depend on risk estimates and mission objec- orative tasks has not been thoroughly tested in uncertain tives, with risk explicitly incorporated in the planning environments. In fact, such testing requires entirely new process. Given the mission’s logistical plan, risks posed methods. Accurate behavioral simulation and metrics along each step of the plan are probabilities integrated (currently unavailable) are both vital to completing a over each mission goal. The configuration and distri- successful mission. The Autonomous Robot Control via butions of these risks should inform optimal human Autonomy Levels (ARCAL) project seeks to establish a engagement. An adjustable autonomy architecture robust planning architecture for collaborative, multi- optimizes the risk assessment and mission planning vehicle autonomous systems by testing system perfor- process to provide situational awareness (SA), keeping mance in uncertain environments. the human involved at the appropriate level of detail for each mission component. Autonomous Robot Control via Autonomy Levels ARCAL’s contribution to adjustable autonomy is to ARCAL brings together researchers and engineers encode risk throughout the decision-making process. from MIT campus and Lincoln Laboratory. Research- In practice, scout aerial vehicles and other sensors can ers from the Model-based Embedded and Robotic Sys- improve risk awareness throughout the mission. Scouts tems (MERS) Group at MIT have developed risk-based are specifically deployed to improve risk mapping and adjustable autonomy and task-directed adaptive sensing refine decision making. Algorithms guide scouts toward systems—two fundamental components of ARCAL—that high-value information that will help identify the low- can autonomously coordinate multivehicle missions with risk pathways for future
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