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Mission Operations and Autonomy: An Approach Toward Autonomous Systems
Abstract
Chapter 6 offers a systematic, thorough discussion on mission operations and autonomy. Section 6.1 introduces the background and 6.2 sets the context of the topic by introducing the basic concepts of mission operations, processes and procedures, and typical operation modes of planetary robotic systems. Section 6.3 discusses the first step in developing the mission operation software, that is, how to establish the on-ground and onboard software architecture for a given mission operation. The following three sections investigate the main design aspects or core technologies in mission operations: Section 6.4 discusses the planning and scheduling (P&S) techniques and representative design solutions that can enable high level of autonomy; Section 6.5 presents the technology that allows reconfiguration of autonomous software within mission operation; and Section 6.6 covers various tools and techniques for validation and verification of autonomous software. To demonstrate the practicality of the theoretical principles, Section 6.7 presents a design example of mission operation software for Mars rovers. The last Section 6.8 of the chapter describes some over-the-horizon R&D ideas in achieving autonomous operations and systems for future planetary robotic missions.
... The conceptual and preliminary design of systems exploring underground planetary caves brings different challenges, mainly related to autonomy and mobility subsystem capabilities. Autonomy impacts the overall operability of the system and how the human operators interact with it, as detailed in Ref. [1]. At the same time, the mobility subsystem characteristics define the amount of space the system can cover and its capacity to avoid or overcome obstacles [2,3]. ...
... [5,6]. The ECSS (European Cooperation for Space Standardization) standard defines four levels of autonomy on a scale from E1 (real-time control) to E4 (goaloriented operations) [1]. The mission toward the lava tubes should be highly autonomous, requiring at least level E3 (adaptive mission operations onboard). ...
... Figure 10 presents an artistic view of the hopper. The designed cave explorer weighs around 15 kg, with 2.5 kg of available payload, around 4 kg of propellant mass, and an expected power consumption of 100 W (Equation (9)) [1]. The m battery is evaluated from the estimation of Figure 6, considering a 100 W power consumption, 40% duty cycle, and a power density of 120 Wh/kg for a Lithium-Ion Battery [64]. ...
The lunar lava tubes are envisioned as possible hosting structures for a human base in the Moon’s equatorial regions, providing shelter from radiations, micrometeoroids, and temperature excursion. A first robotic mission is set to scout the habitability of these underground architectures in the near future. The communication inside these underground tunnels is heavily constrained; hence, the scouting system should rely on a high degree of autonomy. At the same time, the exploration system may encounter different types of terrain, requiring an adaptable mobility subsystem able to travel fast on basaltic terrain while avoiding considerable obstacles. This paper presents a cave explorer’s mission study and preliminary sizing targeting the lunar lava tubes. The study proposes using a hybrid mobility system with wheels and thrusters to navigate smoothly inside the lava tubes. The peculiar mobility system of the cave explorer requires an accurate study of the adaptability of its control capabilities with the change of mass for a given set of sensors and actuators. The combination of conceptual design techniques and control assessment gives the engineer a clear indication of the feasible design box for the studied system during the initial formulation phases of a mission. This first part of the study focuses on framing the stakeholders’ needs and identifying the required capabilities of the cave explorer. Furthermore, the study focuses on assessing a design box in terms of mass and power consumption for the cave explorer. Following different mission-level assessments, a more detailed design of the cave explorer is discussed, providing an initial design in terms of mass and power consumption. Finally, the objective shifts toward studying the performances of the guidance, navigation, and control (GNC) algorithms varying the mass of the cave explorer. The GNC significantly impacts the design box of the surface planetary system. Hence, investigating its limitations can indicate the feasibility of mass growth to accommodate, for example, more payload.
... AI planning is the branch of artificial intelligence that focuses on defining an action sequence for an agent to reach a specified goal. Space operations' software uses planning and scheduling algorithms to achieve a high level of autonomy for both goal-oriented autonomy onboard a spacecraft and planning activities in the ground segment [28]. Most of the work employing AI planning focused on enabling long-term autonomous operations, such as in [7,[28][29][30][31]. ...
... Space operations' software uses planning and scheduling algorithms to achieve a high level of autonomy for both goal-oriented autonomy onboard a spacecraft and planning activities in the ground segment [28]. Most of the work employing AI planning focused on enabling long-term autonomous operations, such as in [7,[28][29][30][31]. Some of those algorithms and frameworks were concretized in the Deep Space 1 mission [10,32] and in the Earth Observing 1 mission [33]. ...
... These parameters can derive from a database of components, previous studies, or a scale-up of existing systems. There is extensive literature on the rapid sizing of physical hardware for robotic space systems, as in [28,37,38]. This resource consumption can change or be refined during the different design phases. ...
During mission design, the concept of operations (ConOps) describes how the system operates during various life cycle phases to meet stakeholder expectations. ConOps is sometimes declined in a simple evaluation of the power consumption or data generation per mode. Different operational timelines are typically developed based on expert knowledge. This approach is robust when designing an automated system or a system with a low level of autonomy. However, when studying highly autonomous systems, designers may be interested in understanding how the system would react in an operational scenario when provided with knowledge about its actions and operational environment. These considerations can help verify and validate the proposed ConOps architecture, highlight shortcomings in both physical and functional design, and help better formulate detailed requirements. Hence, this study aims to provide a framework for the simulation and validation of operational scenarios for autonomous robotic space exploration systems during the preliminary design phases. This study extends current efforts in autonomy technology for planetary systems by focusing on testing their operability and assessing their performances in different scenarios early in the design process. The framework uses Model-Based Systems Engineering (MBSE) as the knowledge base for the studied system and its operations. It then leverages a Markov Decision Process (MDP) to simulate a set of system operations in a relevant scenario. It then outputs a feasible plan with the associated variation of a set of considered resources as step functions. This method was applied to simulate the operations of a small rover exploring an unknown environment to observe and sample a set of targets.
... The task planning software will define the overall behav ior of the two collaborative systems. It will define: (i) when to execute the different tasks, (ii) the order of the different waypoints to visit during the mission, (iii) the [17]. In the specific case of our study, we will apply HDDL [18], an extension of the PDDL [19]. ...
The desire of bringing back the man on the Moon is pushing forward the research of the autonomous systems that will help humans establish a permanent lunar base. Space agencies, companies, and universities accept the challenge of studying solutions to enable human exploration of the cosmos. This paper illustrates the work performed during the IGLUNA analogue mission, a testbed to demonstrate technologies for future lunar explorations. The CoRoDro team was in charge to build an exploration system for the lunar lava tubes, a possible shelter for humans in the equatorial zone of the Moon. In the lava tubes, the main challenge is the impossibility for humans to visually track and communicate with robotic systems. Two earth analogues were used to demonstrate a lunar rover's and a thruster propelled hopper's autonomous navigation operation: a small rover and a drone performing short flights to simulate the hopper. In the lava tube, the two systems will collaborate to analyze and provide a map of its internal structure. The hopper will provide a map thanks to its propelled flights to the rover that will avoid obstacles in the lava tube to reach high-interest areas for analyzing. This concept has been proved during the IGLUNA field campaign that occurred on top of Mount Pilatus (Switzerland). This paper is focusing on presenting the path planning and SLAM (Simultaneous Localization And Mapping) algorithms implemented on both robots. The drone relies on path planning algorithms to explore the unknown environment while the SLAM algorithms generate the map. Path-planning on the drone acquires the boundaries and establishes the covering path of the exploration area. The SLAM algorithms contain several modules that take into consideration the mobility capabilities of the rover to create a map that mentions obstacles and reachable areas for the rover. The map is shared with the rover and used to reach identified targets by the drone. Taking into account obstacles, targets, risks and rewards, an optimum global path is generated. SLAM and path planning algorithms on the rover permit it to update the map along its path, and to adjust locally its path if new obstacles, unseen by the drone, are detected. This work has been reviewed by experts from ESA, Space Innovation, Airbus; an analysis of its performance is presented in this paper. It has shown the interest of fully autonomous robotic missions in extreme and remote conditions, encountered both in space and on Earth.
... Autonomy is the ability of an agent to accomplish goals through rational decision making based on its knowledge and understanding of the world, itself, and the situation [110]. The need for onboard autonomous behavior in spacecraft is strongly influenced by communication latency between spacecraft and ground stations; even with continuous view of ground stations, satellites in geosynchronous Earth orbit (GEO) are subject to a communication latency of 240 ms while satellites in low Earth orbit (LEO) can go several hours without contact to ground stations. ...
The low-cost and short-lead time of small satellites has led to their use in science-based missions, earth observation, and interplanetary missions. Today, they are also key instruments in orchestrating technological demonstrations for On-Orbit Operations (O
3
) such as inspection and spacecraft servicing with planned roles in active debris removal and on-orbit assembly. This paper provides an overview of the robotics and autonomous systems (RASs) technologies that enable robotic O
3
on smallsat platforms. Major RAS topics such as sensing & perception, guidance, navigation & control (GN&C) microgravity mobility and mobile manipulation, and autonomy are discussed from the perspective of relevant past and planned missions.
Space Mission Control operates within a fundamentally risk-averse environment where any failure is simply not an option. This is where our journey began, with a primary focus on fostering change, enhancing existing practices, and driving innovation while maintaining an acceptable level of risk. Our overarching challenge revolved around finding ways to mitigate risks associated with introducing novel approaches into the control room.
Autonomous robotic systems are key to planetary exploration. A facility
including virtual/physical environments for validation and verification
is described.
Surface operations in distant bodies like Mars present a lot of challenges. Among them, lack of direct communications and a complex environment are the main drivers that push for more autonomy both on-ground and on-board. This paper presents a timeline, hierarchical, heuristically-based and domain-independent planner called QuijoteExpress oriented to solve highly constrained temporal problems.
One of the most important lessons learnt from more than
ten years of robotic exploration in Mars is the high value of
autonomy in terms of operations and science return.
So far, only few full-scale experiments to foster autonomy
for planetary exploration have been conducted in Europe,
being the recently finished FASTER FP7 framework project
one of the most relevant. FASTER (Forward Acquisition of
Soil and Terrain data by Exploration Rover) aimed to enable
higher travel velocities and reduce the risk during the traverse.
It comprised two exploration rovers working cooperatively.
A high mobility, lightweight vehicle was used under
the control of the primary rover to scout the planned path,
gathering data to assess its trafficability. Data on terrain conditions
was shared between the rovers to build a global map.
In case of elevated risk due to poor trafficability, plan replanning
was conducted to avoid the dangerous zones, thus
enhancing overall mission safety.
One of the main scientific contributions of FASTER lies
on the application of autonomous planning and execution
under uncertainty for two collaborative rovers in a Mars scenario
where it is not possible to have humans in the loop.
This paper presents the results of the QuijoteExpress mission
planner and SanchoExpress executive to tackle these
problems.
QuijoteExpress is a novel platform-independent planner
that combines hierarchical (HTN) and temporal (Timelines)
planning. Taking advantage of its HTN representation capabilities,
it was possible to define FASTER complex behaviours
in a top-down hierarchical approach. The resulting
models and the plans produced are organized in hierarchical
structures, from high level, abstract goals such
as Traverse(origin; destination) to low level, highly detailed
commands such as TurnWheel(#degrees), easier
to analyse and verify by human operators. This is the first
time an APSI planner benefits from this level of representation,
similar or higher to that showed in very advanced planners
such as ASPEN. QuijoteExpress was able to produce
collaborative plans for both rovers with just little tuning of
the heuristic functions and with high levels of robustness under
uncertainty thanks to the generation of flexible starting
and ending times for each activity.
SanchoExpress is a ROS-based timeline executive that
can command in parallel multiple subsystems. It is tightly
coupled to QuijoteExpress, both sharing the same plan to
achieve an agile transition planning-execution-replanning.
SanchoExpress is divided in two components:
� Dispatcher: Selects the next activity to be executed and
timely send it to the corresponding subsystem.
� Monitor: Tracks the execution taking appropriate measures
in case of deviations with respect to the nominal
plan, typically reporting back to the planner.
SachoExpress is organized in a platform-independent
core plus a number of dedicated executives, one for each
subsystem of the domain. In FASTER, five subsystems were
identified: Primary Path Planner, Primary Locomotion, Primary
Antenna, Scout Locomotion and Scout Path Planner.
Each dedicated executive can encode platform-dependent
knowledge related to its subsystem including repair methods
for some errors, namely hazard detection and no-path-found
in the FASTER scenario. This feature allowed the executive
to fix the plan in different circumstances without the need
of the re-planner, which is more time consuming, therefore
contributing to a more robust execution of the plan.
The combined system was found to operate successfully
and reliably during the FASTER field trials enhancing the
systems autonomy and enabling complex behaviour to be
executed in a robust and fault tolerant manner.
EUROPA is a class library and tool set for building and ana-lyzing planners within a Constraint-based Temporal Planning paradigm. This paradigm has been successfully applied in a wide range of practical planning problems and has a legacy of success in NASA applications. EUROPA offers capabil-ities in 3 key areas of problem solving: (1) Representation; (2) Constraint-based Reasoning; and (3) Search. EUROPA is a means to integrate advanced planning, scheduling and constraint reasoning into an end-user application and is de-signed to be open and extendable to accommodate diverse and highly specialized problem solving techniques within a common design framework and around a common technol-ogy core. While EUROPA is a complete tool set, in this paper, we will mostly concentrate on its timeline-based plan repre-sentation and the least-commitment partial-order planning ap-proach operates on top of that representation.
This paper gives a preliminary overview on an on-going effort to build a planning system to support ESA mission planners in Long Term Planning for Mars Ex-press. The output of the tool is a pre-optimized skele-ton plan of the communication windows and spacecraft maintenance slots, that is used as a support in the di-alogue between science and operation team in early pre-planning of activities. To capture details of the do-main we have specialized the OMPS planning architec-ture to obtain a rapid prototyping tool that can support the knowledge engineering effort in this domain.
Most planning applications require reasoning about ac-tions that take time, consume or produce resources, de-pend on numbers to characterize, and that contain com-plex constraints over these elements. In the past several years, various attempts have been made to characterize the notion of Timelines as a key part of fielded plan-ning and scheduling applications. These attempts have not been satisfactory. The thesis of this paper is that the Timeline provides a set of services allowing the plan-ner to interact with the computed history of the state variables defined in the planning problem. The Time-line must be computed from partial plans that incom-pletely specify the history; that is, there may be parts of the planning horizon where the value of the variable is constrained, but not yet known. The paper examines how different assumptions on the commitments made by planners influence the computational complexity of determining these histories, and responding to queries. Why don't we really know what a Timeline is?
The paper addresses to the current discussion aimed at reach-ing an agreement for a common representation framework and language for modeling problems with timelines. This pa-per is grounded on the experience of the 10 years collabora-tion between ISTC-CNR and ESA/ESOC for timelines-based planning, scheduling and applications deployment. The pa-per introduces the key concepts of our timeline modeliza-tion called the TRF (Timeline Representation Framework) and describes how the TRF is implemented in the ESA APSI (Advanced Planning and Scheduling Initiative) software platform.
The community-based generation of content has been tremendously successful in the World Wide Web - people help each other by providing information that could be useful to others. We are trying to transfer this approach to robotics in order to help robots acquire the vast amounts of knowledge needed to competently perform everyday tasks. RoboEarth is intended to be a web community by robots for robots to autonomously share descriptions of tasks they have learned, object models they have created, and environments they have explored. In this paper, we report on the formal language we developed for encoding this information and present our approaches to solve the inference problems related to finding information, to determining if information is usable by a robot, and to grounding it on the robot platform.
In July 2004, CNES and ONERA signed an agreement to start a common research program on autonomy for space systems. Since then, a team of ONERA researchers and CNES engineers have been working together with the same objective: federate research activities on autonomy through a demonstration project. This program is composed of research studies on topics such as architecture, autonomous planning and diagnostic, software development and validation methods, and the results of these studies will be applied to the AGATA (Autonomy Generic Architecture Tests and Application) lab bench. The paper describes how space missions can be classified according to their autonomy requirements, to what extent a generic architecture can meet these requirements and how the AGATA lab bench will help to promote autonomy for new missions.
Autonomous Underwater Vehicles (AUVs) are an increasingly important tool for oceanographic research demonstrating their capabilities to sample the water column in depths far beyond what humans are capable of visiting, and doing so routinely and cost-effectively. However, control of these platforms has relied on fixed sequences for execution of pre-planned actions limiting their effectiveness for measuring dynamic and episodic ocean phenomenon. In this paper we present an agent architecture developed to overcome this limitation through on-board planning using Constraint-based Reasoning approaches. Preliminary versions of the architecture have been integrated and tested in simulation and at sea.
A service choreography is a distributed service composition in which services interact without a centralized control. Adequate adaptation strategies are required to face complex and ever-changing business processes, given the collaborative nature of choreographies. Choreographies should also be able to adapt to changes in its non-functional requirements, such as response time, and especially for large-scale choreographies, adaptation strategies need to be automated and scale well. However, the body of knowledge regarding choreography adaptation approaches has not yet been consolidated and systematically evaluated. By means of a systematic literature review, in which we examined seven scientific paper sources, we identified and analyzed the state-of-the-art in choreography adaptation. We found 24 relevant primary studies and grouped them into six categories: model-based, measurement-based, multi-agent-based, formal method-based, semantic reasoning-based, and proxy layer-based. We analyzed (i) how each strategy deals with different types of requirements, (ii) what their required degree of human intervention is, (iii) how the different studies considered scalability, (iv) what implementations are currently available, and (v) which choreography languages are employed. From the selected studies, we extracted key examples of choreography adaptation usage and analyzed the terminology they adopted with respect to dynamic adaptation. We found out that more attention has been devoted to functional requirements and automated adaptation; only one work performs scalability evaluation; and most studies present some sort of implementation and use a specific choreography notation.
In services composition the failure of a single service generates an error propagation in the others services involved, and therefore the failure of the system. Such failures often cannot be detected and corrected locally (single service), so it is necessary to develop architectures to enable diagnosis and correction of faults, both at individual (service) as global (composition levels). The middlewares, and particularly reflective middlewares, have been used as a powerful tool to cope with inherent heterogeneous nature of distributed systems in order to give them greater adaptability capacities. In this paper we propose a middleware architecture for the diagnosis of fully distributed service compositions called ARMISCOM, which is not coordinated by any global diagnoser. The diagnosis of faults is performed through the interaction of the diagnoser present in each service composition, and the repair strategies are developed through consensus of each repairer distributed equally in each service composition.
A large number of robotic software have been developed but cannot or can hardly interoperate with each other because of their dependencies on specific hardware or software platform is hard-wired into the code. Consequently, robotic software is hard and expensive to develop because there is little opportunity of reuse and because low-level details must be taken into account in early phases. Moreover, robotic experts can hardly develop their application without programming knowledge or the help of programming experts and robotic software is difficult to adapt to hardware or target-platform changes. In this paper we report on the development of RobotML, a Robotic Modeling Language that eases the design of robotic applications, their simulation and their deployment to multiple target execution platforms.
Service-based systems that are dynamically composed at runtime to provide complex, adaptive functionality are currently one of the main development paradigms in software engineering. However, the Quality of Service (QoS) delivered by these systems remains an important concern, and needs to be managed in an equally adaptive and predictable way. To address this need, we introduce a novel, tool-supported framework for the development of adaptive service-based systems called QoSMOS (QoS Management and Optimization of Service-based systems). QoSMOS can be used to develop service-based systems that achieve their QoS requirements through dynamically adapting to changes in the system state, environment, and workload. QoSMOS service-based systems translate high-level QoS requirements specified by their administrators into probabilistic temporal logic formulae, which are then formally and automatically analyzed to identify and enforce optimal system configurations. The QoSMOS self-adaptation mechanism can handle reliability and performance-related QoS requirements, and can be integrated into newly developed solutions or legacy systems. The effectiveness and scalability of the approach are validated using simulations and a set of experiments based on an implementation of an adaptive service-based system for remote medical assistance.
Future networked embedded systems will be complex deployments deeply integrated in the environment that they monitor. They will have to react to both user and environmental events, and this may require modifying their structure to handle the changing situations. In a number of domains including industrial environments, this modification of the system structure will need to take place in real time. This is a hard problem that will require novel and paradigmatic solutions involving cross-domain knowledge to build a middleware for enabling real-time interaction between nodes allowing time-bounded reconfiguration. For supporting real-time, this middleware must be vertically architected in a modular way from the network and operating system levels up to the application software and the real-time policies for achieving time-bounded behavior. This paper presents a middleware that addresses these characteristics supporting timely reconfiguration in distributed real-time systems based on services. Experimental results are shown to validate the middleware in an actual small-scale video prototype.
The Goal-Oriented Autonomous Controller (GOAC) is the envisaged result of a multi-institutional effort within the on-going Autonomous Controller R&D activ-ity funded by ESA ESTEC. The objective of this effort is to design, build and test a viable on-board controller to demonstrate key concepts in fully autonomous opera-tions for ESA missions. This three-layer architecture is an integrative effort to bring together four mature tech-nologies: for a functional layer, a verification and vali-dation system, a planning engine and a controller frame-work for planning and execution which uses the sense-plan-act paradigm for goal oriented autonomy. GOAC as a result will generate plans in situ, deterministically dispatch activities for execution, and recover from off-nominal conditions.
A number of new tactical planning and operations tools were deployed on the highly successful Mars Exploration Rover (MER) mission. Based on successes and lessons from the MER experience, a number of groups at NASA Ames and JPL have developed a platform for developing integrated operations tools, called Ensemble. Ensemble is a multi-mission toolkit for building activity planning and sequencing systems that is being deployed on extended operations for the MER mission, the 2007 Phoenix Mars Lander and the 2009 Mars Science Laboratory rover mission. Experience designing, building and operating the MER tools with our colleagues, studying the use of the MER tools from a Human/Computer Interaction perspective, and feedback from these three missions has lead us to take a somewhat different approach to designing and deploying applications with planning technology this time around. We believe these changes will make future applications even more efficient to use and easier to implement. This experience may be of use and interest to people working on similar kinds of applications, space related or not.
The increasing complexity of current software systems is encouraging the development of self-managed software architectures, i.e. systems capable of reconfiguring their structure at runtime to fulfil a set of goals. Several approaches have covered different aspects of their development, but some issues remain open, such as the maintainability or the scalability of self-management subsystems. Centralized approaches, like self-adaptive architectures, offer good maintenance properties but do not scale well for large systems. On the contrary, decentralized approaches, like self-organising architectures, offer good scalability but are not maintainable: reconfiguration specifications are spread and often tangled with functional specifications. In order to address these issues, this paper presents an aspect-oriented autonomic reconfiguration approach where: (1) each subsystem is provided with self-management properties so it can evolve itself and the components that it is composed of; (2) self-management concerns are isolated and encapsulated into aspects, thus improving its reuse and maintenance. Povzetek: Predstavljen je pristop s samo-preoblikovanjem programske arhitekture.
NASA's Earth Observing One Spacecraft (EO-1) has been adapted to host an advanced suite of onboard autonomy software designed to dramatically improve the quality and timeliness of science-data returned from remote-sensing missions. The Autonomous Sciencecraft Experiment (ASE) enables the spacecraft to autonomously detect and respond to dynamic scientifically interesting events observed from EO-1's low earth orbit. ASE includes software systems that perform science data analysis, mission planning, and run-time robust execution. In this article we describe the autonomy flight software, as well as innovative solutions to the challenges presented by autonomy, reliability, and limited computing resources.
Autonomous Underwater Vehicles (AUVs) are an increasingly important tool for oceanographic research demonstrating their capabilities to sample the water column in depths far beyond what humans are capable of visiting, and doing so routinely and cost-effectively. However, control of these platforms to date has relied on fixed sequences for execution of pre-planned actions limiting their effectiveness for measuring dynamic and episodic ocean phenomenon. In this paper we present an agent architecture developed to overcome this limitation through on-board planning using Constraint- based Reasoning. Preliminary versions of the architecture have been integrated and tested in simulation and at sea.
Rigorous system design requires the use of a single powerful component framework allowing the representation of the designed system at different detail levels, from application software to its implementation. A single framework allows the maintenance of the overall coherency and correctness by comparing different architectural solutions and their properties. The authors present the BIP (behavior, interaction, priority) component framework, which encompasses an expressive notion of composition for heterogeneous components by combining interactions and priorities. This allows description at different abstraction levels from application software to mixed hardware/software systems. A rigorous design flow that uses BIP as a unifying semantic model derives a correct implementation from an application software, a model of the target architecture, and a mapping. Implementation correctness is ensured by applying source-to-source transformations that preserve correctness of essential design properties. The design is fully automated and supported by a toolset including a compiler, the D-Finder verification tool, and model transformers. The authors present an autonomous robot case study to illustrate BIP's use as a modeling formalism as well as crucial aspects of the design flow for ensuring correctness.
This paper proposes a generic approach towards combining fuzzy logic and ontology-based deliberative reasoning to enable self-reconfigurability within a distributed system architecture. An Ontology-based rational agent uses outputs from a fuzzy inference system (reconfiguration layer) which passively monitors the performance of the lower-level sub-systems (application layer) in order to perform system reconfiguration. A reconfiguration is required to guarantee optimal performance within a complex robotics architecture when anomalous system and environmental changes take place. More importantly, this process of reconfiguration offers greater fault tolerance and reliability in novel scenarios as compared to isolated engineered systems. The current research work will apply the proposed framework to a visual navigation system for autonomous planetary rover missions. This demonstrates the method's success through an increase in system performance following a reconfiguration routine carried out within the application layer between two different types of visual navigation methods. Experimental analysis is carried out using real-world data, concluding that the proposed reconfigurable architecture gives superior performance against standard engineered techniques.
Various real-life problems, considered as problems within artificial intelligence (AI) and operational research (OR), are solved via the constraint satisfaction problem (CSP) formalism that provides a tool for modeling and handling knowledge, and is simple, general, expressive and efficient in the sense that it is able to represent multiple and various types of knowledge. The chapter begins by refining the definition of these CSPs as well as the associated basic notions. Then, it presents the application areas of CSPs illustrated with examples prior to exposing the variants and extensions of the CSP formalism.
Certain limitations exist in autonomous software and guidance, navigation, and control architectures developed for extraterrestrial planetary exploration rovers in regard to fault tolerance, changes in environment, and changes in rover capabilities. To address these limitations, this paper outlines a self-reconfiguring guidance, navigation, and control architecture, using an ontology-based rational agent to enable autonomous reconfiguration of mission goals, software architecture, software components, and the control of hardware components during the run time. This new architecture was evaluated through implementation onboard a rover and tested in challenging, Mars-like environments, both simulated and real world, and was found to be highly reliable, fault tolerant, and adaptable.
This paper presents a novel solution to the problem of autonomous task allocation for a self-organising satellite constellation in Earth orbit. The method allows satellites to cluster themselves above targets on the Earth???s surface. This is achieved using coupled selection equations (CSEs), a dynamical systems approach to combinatorial optimisation whose solution tends asymptotically towards a Boolean matrix describing the pairings of satellites and targets, which solves the relevant assignment problems. Satellite manoeuvres are actuated by an artificial potential field method which incorporates the CSE output. Three demonstrations of the method???s efficacy are given, first with equal numbers of satellites and targets, then with a satellite surplus, including agent failures, and finally with a fractionated constellation. Finally, a large constellation of 100 satellites is simulated to demonstrate the utility of the method in future swarm mission scenarios. The method provides efficient solutions with quick convergence, is robust to satellite failures, and hence appears suitable for distributed, on-board autonomy.
As part of the ESA/ESOC study “Linux & Multi Core Processor Technology for Simulators” [7], several concepts concerning performance optimization of SIMULUS based operational
simulators were introduced. Starting from the ossibility to explore parallelism and distributed execution of computational
loads (PDES approach) and continuing with the profiling, analysis
and focused optimization of specific simulation models our efforts and ideas converged to a Performance Optimization Framework
(POF). The present paper summarizes the way the idea of a Performance Optimization Framework can be applied in future
operational simulators.
ESOC is currently implementing the initial stages of the ESA Ground Operation System (EGOS). This is an ambitious program to standardize the infrastructure used throughout its ground segments in an effort to improve the reliability, cost effectiveness and interoperability. This paper focuses on the project to implement the EGOS Core Components, Core libraries and framework. These will provide the functional foundation on which applications and systems are built.
To explore new worlds, undertake science, and observe new phenomena, NASA must endeavor to develop increasingly sophisticated missions. Sensitive new instruments are constantly being developed, with ever increasing capability of collecting large quantities of data. The new science performed often requires multiple coordinating spacecraft to make simultaneous observations of phenomena. The new missions often require ground systems that are correspondingly more sophisticated. Nevertheless, the pressures to keep mission costs and logistics manageable increase as well. The new paradigms in spacecraft design that support the new science bring new kinds of mission operations concepts [165]. The ever-present competition for national resources and the consequent greater focus on the cost of operations have led NASA to utilize adaptive operations and move toward almost total onboard autonomy in certain mission classes [176, 195]. In NASA’s new space exploration initiative, there is emphasis on both human and robotic exploration. Even when humans are involved in the exploration, human tending of space assets must be evaluated carefully during mission definition and design in terms of benefit, cost, risk, and feasibility. Risk is a major factor supporting the use of unmanned craft: the loss of human life in two notable Shuttle disasters has delayed human exploration [160], and has led to a greater focus on the use of automation and robotic technologies where possible. For the foreseeable future, it is infeasible to use humans for certain kinds of exploration, e.g., exploring the asteroid belt, for which the concept autonomous nano technology swarm (ANTS) mission was posed – discussed in Chap. 10 – where uncrewed miniature spacecraft explore the asteroid belt. A manned mission for this kind of exploration would be prohibitively expensive and would pose unacceptable risks to human explorers due to the dangers of radiation among numerous other factors.
One of the goals of the Mars Science Laboratory (MSL) mission is to collect powderized samples from the interior of rocks in order to deliver these samples to onboard science instruments. This paper describes the algorithms and software used to control the drill, which is the component of the sample collection and delivery system that directly interacts with rocks to create and acquire powderized samples from their interior. This is the first time that autonomous drilling of rocks has ever been performed on another planet. One of the most important components of the algorithm used for drilling is a force feedback control system used to regulate the force applied to the rock during drilling. This algorithm and all of the other algorithms and software used to enable the process of robustly, efficiently, and autonomously drilling into rocks with a priori unknown and widely varying properties are described in detail in this paper. Results are shown from drilling rocks using the drill software on testbed hardware on Earth as part of the software development process. Results are also shown from the first holes drilled with the flight vehicle on Mars, thus successfully demonstrating the first extraterrestrial autonomous drilling of a rock.
Autonomous service robots will have to understand vaguely described tasks, such as âset the tableâ or âclean upâ. Performing such tasks as intended requires robots to fully, precisely, and appropriately parameterize their low-level control programs. We propose knowledge processing as a computational resource for enabling robots to bridge the gap between vague task descriptions and the detailed information needed to actually perform those tasks in the intended way. In this article, we introduce the KnowRob knowledge processing system that is specifically designed to provide autonomous robots with the knowledge needed for performing everyday manipulation tasks. The system allows the realization of âvirtual knowledge basesâ: collections of knowledge pieces that are not explicitly represented but computed on demand from the robot's internal data structures, its perception system, or external sources of information. This article gives an overview of the different kinds of knowledge, the different inference mechanisms, and interfaces for acquiring knowledge from external sources, such as the robot's perception system, observations of human activities, Web sites on the Internet, as well as Web-based knowledge bases for information exchange between robots. We evaluate the system's scalability and present different integrated experiments that show its versatility and comprehensiveness.
In this paper Takagi–Sugeno (T–S) fuzzy approach is used for control of a flexible joint robot (FJR). The control method in this paper is based on parallel distributed compensation approach (PDC). In this method, a state feedback controller is designed using a fuzzy model that consists of a set of linear fuzzy local models. These fuzzy local models are combined to get an overall fuzzy inference mechanism. In order to improve the efficiency of the system and to set the parameters of the controller the Hybrid-Taguchi genetic algorithm (HTGA) is used. The use of orthogonal arrays (OA) speeds up the convergence of the algorithm and the contribution of signal to noise (S/N) ratio will result in a more appropriate response. The stability of the controller is analyzed and finally it is implemented on the plant. The experimental results confirm the efficiency of the proposed intelligent method in controlling FJRs. Therefore, it can be considered as a proper method in controlling systems that are based on flexible robot arms.
Today software is the main enabler of many of the appliances and devices omnipresent in our daily life and important for our well being and work satisfaction. It is expected that the software works as intended, and that the software always and everywhere provides us with the best possible utility. This paper discusses the motivation, technical approach, and innovative results of the MUSIC project. MUSIC provides a comprehensive software development framework for applications that operate in ubiquitous and dynamic computing environments and adapt to context changes. Context is understood as any information about the user needs and operating environment which vary dynamically and have an impact on design choices. MUSIC supports several adaptation mechanisms and offers a model-driven application development approach supported by a sophisticated middleware that facilitates the dynamic and automatic adaptation of applications and services based on a clear separation of business logic, context awareness and adaptation concerns. The main contribution of this paper is a holistic, coherent presentation of the motivation, design, implementation, and evaluation of the MUSIC development framework and methodology.
During the last 18 months, the Intelligent Robotics Group (IRG) of NASA Ames has transitioned its rover software from a classic ad hoc system to a Service- Oriented Robotic Architecture (SORA). Under SORA, rover controller functionalities are encapsulated as a set of services. The services interact using two distinct modalities depending on the need: remote method in- vocation or data distribution. The system strongly re- lies on middleware that offers advanced functionalities while guaranteeing robustness. This architecture allows IRG to meet three critical space robotic systems requirements: flexibility, scala- bility and reliability. SORA was tested during summer 2007 at an analog lunar site: Haughton Crater (De- von Island, Canada). Two rovers were operated from a simulated habitat and remote ground control centers, allowing a full-scale evaluation of our system.
Architecting software systems according to the service-oriented paradigm and designing runtime self-adaptable systems are two relevant research areas in today's software engineering. In this paper, we address issues that lie at the intersection of these two important fields. First, we present a characterization of the problem space of self-adaptation for service-oriented systems, thus providing a frame of reference where our and other approaches can be classified. Then, we present MOSES, a methodology and a software tool implementing it to support QoS-driven adaptation of a service-oriented system. It works in a specific region of the identified problem space, corresponding to the scenario where a service-oriented system architected as a composite service needs to sustain a traffic of requests generated by several users. MOSES integrates within a unified framework different adaptation mechanisms. In this way it achieves greater flexibility in facing various operating environments and the possibly conflicting QoS requirements of several concurrent users. Experimental results obtained with a prototype implementation of MOSES show the effectiveness of the proposed approach.
Building smart environments with Robotic ecologies, comprising of distributed sensors, actuators and mobile robot devices facilitates and extends the nature and form of smart environments that can be developed, and reduces the complexity and cost of such solutions. While the potentials of such an approach makes robotic ecologies increasingly popular, many fundamental research questions remain open. One such question is how to make a robotic ecology self-adaptive, so as to adapt to changing conditions and evolving requirements, and consequently reduce the amount of preparation and pre-programming required for their deployment in real world applications. This paper presents a framework for the specification and the programming of robotic ecologies. The framework extends an existing agent system and integrates it with the pre-existing and dominant traditional robotic and middleware approach to the development of robotic ecologies. We illustrate how these technologies complement each other and offer a candidate technology to pursue adaptive robotic ecologies.
In the traditional approach to managing complex systems, planning and scheduling are two very distinct phases. However, in a wide variety of applications this strict separation is not possible or beneficial. During scheduling it is often necessary to make planning decisions (plan the setup of a machine); moreover planning decisions can benefit from scheduling information (choose a process plan depending on resource loads). HSTS (Heuristic Scheduling Testbed System) is a representation and problem solving framework that provides an integrated view of planning and scheduling. HSTS emphasizes the decomposition of a domain into state variables evolving over continuous time. This allows the description and manipulation of resources far more complex than it is possible in classical scheduling. The inclusion of time and resource capacity into the description of causal justifications allows a fine-grain integration of planning and scheduling and a better adaptation to problem and domain structure. HSTS puts special emphasis on leaving as much temporal flexibility as possible during the planning/scheduling process to generate better plan/schedules with less computation effort. Within the HSTS framework we have implemented several planning/scheduling systems. In the paper we describe an integrated planner and scheduler for short term scheduling of the Hubble Space Telescope. This system has demonstrated the ability to deal effectively with all of the important constraints of the domain. Experimental results show that executable schedules for Hubble can be built in a time compatible with operational needs. The paper also describes a methodology for job-shop scheduling problems. The methodology exploits the temporal flexibility provided by HSTS.
AUTONOMOUS GOAL SELECTION 10 6 DEPLOYMENT RESULTS 10 SOFTWARE VERSIONS 11 NASA's Mars Exploration Rovers' (MER) onboard Mo-bility Flight Software was designed to provide robust and flexible operation. The MER vehicles can be commanded 7 CoNCLUsIoN 13 directly, or given autonomous control over multiple aspects 8 ACKNOWLEDGEMENTS 13 of mobility: which motions to drive, measurement of actual interest (although this mode remains largely underused). Vehicle motion can be commanded using multiple layers of control: Motor Control, Direct Drive operations (Arc, Turn in Place), and Goal-based Driving (Goto Waypoint). Multiple layers of safety checks ensure vehicle performance: Com-mand limits (conamand timeout, time of day limit, software enable, activity constraints), Reactive checks (e.g., motor cur-rent limit, vehicle tilt limit), and Predictive checks (e.g., Step, Tilt, Roughness hazards). From January 2004 through October 2005, Spirit accu-mulated over 5080 meters and Opportunity 6000 meters of odometry, often covering more than 100 meters in a single day. In this paper we describe the software that has driven these rovers more than a combined 11,000 meters over the Martian surface, including its design and implementation, and summarize current mobility performance results from Mars.
An interval-based temporal logic is introduced together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between expressive power and the efficiency of its deductive engine. A notion of reference intervals is introduced which captures the temporal hierarchy implicit in many domains, and which can be used to precisely control the amount of deduction performed automatically by the system. Examples are provided for a database containing historical data, a database used for modeling processes and process interaction, and a database for an interactive system where the present moment is continually being updated.
This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints. In this framework, called temporal constraint satisfaction problem (TCSP), variables represent time points and temporal information is represented by a set of unary and binary constraints, each specifying a set of permitted intervals. The unique feature of this framework lies in permitting the processing of metric information, namely, assessments of time differences between events. We present algorithms for performing the following reasoning tasks: finding all feasible times that a given event can occur, finding all possible relationships between two given events, and generating one or more scenarios consistent with the information provided.
We distinguish between simple temporal problems (STPs) and general temporal problems, the former admitting at most one interval constraint on any pair of time points. We show that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time. For general TCSPs, we present a decomposition scheme that performs the three reasoning tasks considered, and introduce a variety of techniques for improving its efficiency. We also study the applicability of path consistency algorithms as preprocessing of temporal problems, demonstrate their termination and bound their complexities.