| 1. |
- Bidot, Julien, et al.
(author)
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Geometric backtracking for combined task and path planning in robotic systems
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Other publication (other academic/artistic)abstract
- Planners for real, possibly complex, robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach in which state-based forward-chaining task planning is tightly coupled with sampling-based motion planning and other forms of geometric reasoning. We focus on the problem of geometric backtracking which arises when a planner needs to reconsider geometric choices, like grasps and poses, that were made for previous actions, in order to satisfy geometric preconditions of the current action. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the systematic exploration of the space of geometric states. In order to deal with that, we introduce heuristics based on the collisions between the robot and movable objects detected during geometric backtracking and on kinematic relations between actions. We also present a complementary approach based on propagating explicit constraints which are automatically generated from the symbolic actions to be evaluated and from the kinematic model of the robot. We empirically evaluate these dierent approaches. We demonstrate our planner on a real advanced robot, the DLR Justin robot, and on a simulated autonomous forklift.
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| 2. |
- Bouguerra, Abdelbaki, et al.
(author)
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Active execution monitoring using planning and semantic knowledge
- 2007
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In: Proc. of the ICAPS Workshop on Planning and Plan Execution for Real-World Systems, Providence, RI, 2007. ; , s. 9-15
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Conference paper (other academic/artistic)abstract
- To cope with the dynamics and uncertainty inherent in real world environments, autonomous mobile robots need to perform execution monitoring for verifying that their plans are executed as expected. Domain semantic knowledge has lately been proposed as a source of information to derive and monitor implicit expectations of executing actions. For instance, when the robot moves into an office, it would expect to see a desk and a chair. Such expectations are checked using the immediately available perceptual information. We propose to extend the semantic knowledge-based execution monitoring to handle situations where some of the required information is missing. To this end, we use AI sensor-based planning to actively search for such information. We show how verifying execution expectations can be formulated and solved as a planning problem involving sensing actions. Our approach is illustrated by showing test scenarios run in an indoor environment using a mobile robot.
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| 3. |
- Bouguerra, Abdelbaki, 1974-
(author)
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Robust execution of robot task-plans : a knowledge-based approach
- 2008
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Doctoral thesis (other academic/artistic)abstract
- Autonomous mobile robots are being developed with the aim of accomplishing complex tasks in different environments, including human habitats as well as less friendly places, such as distant planets and underwater regions. A major challenge faced by such robots is to make sure that their actions are executed correctly and reliably, despite the dynamics and the uncertainty inherent in their working space. This thesis is concerned with the ability of a mobile robot to reliably monitor the execution of its plans and detect failures. Existing approaches for monitoring the execution of plans rely mainly on checking the explicit effects of plan actions, i.e., effects encoded in the action model. This supposedly means that the effects to monitor are directly observable, but that is not always the case in a real-world environment. In this thesis, we propose to use semantic domain-knowledge to derive and monitor implicit expectations about the effects of actions. For instance, a robot entering a room asserted to be an office should expect to see at least a desk, a chair, and possibly a PC. These expectations are derived from knowledge about the type of the room the robot is entering. If the robot enters a kitchen instead, then it should expect to see an oven, a sink, etc. The major contributions of this thesis are as follows. • We define the notion of Semantic Knowledge-based Execution Monitoring SKEMon, and we propose a general algorithm for it based on the use of description logics for representing knowledge. • We develop a probabilistic approach of semantic Knowledge-based execution monitoring to take into account uncertainty in both acting and sensing. Specifically, we allow for sensing to be unreliable and for action models to have more than one possible outcome. We also take into consideration uncertainty about the state of the world. This development is essential to the applicability of our technique, since uncertainty is a pervasive feature in robotics. • We present a general schema to deal with situations where perceptual information relevant to SKEMon is missing. The schema includes steps for modeling and generating a course of action to actively collect such information. We describe approaches based on planning and greedy action selection to generate the information-gathering solutions. The thesis also shows how such a schema can be applied to respond to failures occurring before or while an action is executed. The failures we address are ambiguous situations that arise when the robot attempts to anchor symbolic descriptions (relevant to a plan action) in perceptual information. The work reported in this thesis has been tested and verified using a mobile robot navigating in an indoor environment. In addition, simulation experiments were conducted to evaluate the performance of SKEMon using known metrics. The results show that using semantic knowledge can lead to high performance in monitoring the execution of robot plans.
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| 4. |
- Cirillo, Marcello
(author)
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Planning in Inhabited Environments : Human-Aware Task Planning and Activity Recognition
- 2010
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Doctoral thesis (other academic/artistic)abstract
- Promised some decades ago by researchers in artificial intelligence and robotics as an imminent breakthrough in our everyday lives, a robotic assistant that could work with us in our home and our workplace is a dream still far from being fulfilled. The work presented in this thesis aims at bringing this future vision a little closer to realization. Here, we start from the assumption that an efficient robotic helper should not impose constraints on users' activities, but rather perform its tasks unobtrusively to fulfill its goals and to facilitate people in achieving their objectives. Also, the helper should be able to consider the outcome of possible future actions by the human users, to assess how those would affect the environment with respect to the agent's objectives, and to predict when its support will be needed. In this thesis we address two highly interconnected problems that are essential for the cohabitation of people and service robots: robot task planning and human activity recognition. First, we present human-aware planning, that is, our approach to robot high-level symbolic reasoning for plan generation. Human-aware planning can be applied in situations where there is a controllable agent, the robot, whose actions we can plan, and one or more uncontrollable agents, the human users, whose future actions we can only try to predict. In our approach, therefore, the knowledge of the users' current and future activities is an important prerequisite. We define human-aware as a new type of planning problem, we formalize the extensions needed by a classical planner to solve such a problem, and we present the implementation of a planner that satisfies all identified requirements. In this thesis we explore also a second issue, which is a prerequisite to the first one: human activity monitoring in intelligent environments. We adopt a knowledge driven approach to activity recognition, whereby a constraint-based domain description is used to correlate sensor readings to human activities. We validate our solutions to both human-aware planning and activity recognition both theoretically and experimentally, describing a number of explanatory examples and test runs in a real environment.
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| 5. |
- Lagriffoul, Fabien, 1977-
(author)
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Combining Task and Motion Planning
- 2016
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Doctoral thesis (other academic/artistic)abstract
- This thesis addresses the problem of automatically computing, given a high-level goal description, a sequence of actions and motion paths for one or several robots to achieve that goal. Also referred to as CTAMP (Combining Task And Motion Planning), this problem may seem trivial at first glance, since efficient solutions have been found for its two underlying problems, namely task planning and motion planning. However, further consideration reveals that combining task and motion planning, in many cases, is not straightforward. We have identified two important issues which are addressed in this thesis.The first issue originates in the fact that symbolic actions can be geometrically instantiated in multiple ways. Choosing a geometric instance for each action is not trivial, because a “wrong” choice may compromise the feasibility of subsequent actions. To address this issue, in the first part of the thesis we propose a mechanism for backtracking over geometric choices in the context of a partial symbolic plan. This process may greatly increase the complexity of CTAMP. Therefore, we also present a constraint-based approach for pruning out geometric configurations which violate a number of geometric constraints imposed by the action sequence, and by the kinematic models of robots. This approach has been tested with success on the real humanoid robotic platform Justin in the context of the GeRT1 project.The second issue results from the necessity to interleave symbolic and geometric computations for taking geometric constraints into account at the symbolic level. Indeed, the symbolic search space forms an abstraction of the physical world, hence geometric constraints such as objects occlusions or kinematic constraints are not represented. However, interleaving both search processes is not a workable approach for large problem instances, because the resulting search space is too large. In the second part of the thesis, we propose a novel approach for decoupling symbolic and geometric search spaces, while keeping the symbolic level aware of geometric constraints. Culprit detection mechanisms are used for computing explanations for geometric failures, and these explanations are leveraged at the symbolic level for pruning the search space through inference mechanisms. This approach has been extensively tested in simulation, on different types of single and multiple robot systems.
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| 6. |
- Lundh, Robert, et al.
(author)
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An algorithm for generating configurations of groups of robots
- 2007
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Reports (other academic/artistic)abstract
- This work is about the use of artificial intelligence (AI) planning techniques to automatically configure cooperation among robots. We consider groups of autonomous robots in which robots can help each other by offering information producing resources and functionalities. A configuration in this context, is a way to allocate and connect functionalities among robots. In general, different configurations can be used to solve the same task, depending on the current situation. Configuration generation is the problem of automatically generating a configuration for some specific purpose given a set of robotic devices possessing dfferent functionalities. In this paper, we consider an existing configuration planner both from a theoretical point of view (soundness, completeness, and optimality), and an empirical point of view (scalability). We also present a technique to improve the scalability of the configuration planner.
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| 7. |
- Lundh, Robert, 1978-
(author)
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Plan-Based Configuration of a Group of Robots
- 2006
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Licentiate thesis (other academic/artistic)abstract
- Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door opening. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door to cross. If she cannot see the door, she cannot safely push the parcel through the door opening. What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help then we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other. Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil can view both Pippi and the door at the same time, he can guide pippi through the door, enabling her to deliver the parcel to you. This work is about societies of autonomous robots in which robots can help each other by offering information-producing functionalities. A functional configuration is a way to allocate and connect functionalities among robots. In general, different configurations can be used to solve the same task, depending on the current situation. For the work on configurations, we have three steps. The first step is to formally define the idea of functional configuration. Second, to show how configurations can be automatically generated and executed. The third step is to address the problem of when and how to change a configuration in response to changing conditions. In this licenciate thesis we report initial work that focus on the two first steps: the third step is subject of future work. We propose a formal definition of functional configurations, and we propose an approach based on artificial intelligence (AI) planning techniques to automatically generate a preferred configuration for a given task, environment, and set of resources. To illustrate these ideas, we describe an experimental system where these are implemented, and show two example of it in which two robots mutually help each other to address tasks. In the first example they help each other to cross a door, and in the second example they carry a bar together.
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| 8. |
- Lundh, Robert, 1978-
(author)
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Robots that help each other : self-configuration of eistributed robot systems
- 2009
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Doctoral thesis (other academic/artistic)abstract
- Imagine the following situation. You give your favorite robot, named Pippi, the task to fetch a heavy parcel that just arrived at your front door. While pushing the parcel back to you, she must travel through a door. Unfortunately, the parcel she is pushing is blocking her camera, giving her a hard time to see the door. If she cannot see the door, she cannot safely push the parcel through it. What would you as a human do in a similar situation? Most probably you would ask someone for help, someone to guide you through the door, as we ask for help when we need to park our car in a tight parking spot. Why not let the robots do the same? Why not let robots help each other? Luckily for Pippi, there is another robot, named Emil, vacuum cleaning the floor in the same room. Since Emil has a video camera and can view both Pippi and the door at the same time, he can estimate Pippi's position relative to the door and use this information to guide Pippi through the door by wireless communication. In that way he can enable Pippi to deliver the parcel to you. The goal of this thesis is to endow robots with the ability to help each other in a similar way. More specifically, we consider distributed robot systems in which: (1) each robot includes modular functionalities for sensing, acting and/or processing; and (2) robots can help each other by offering those functionalities. A functional configuration of such a system is any way to allocate and connect functionalities configuration among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In the above example, Emil is offering a perceptual functionality to Pippi. In a different situation, Emil could offer his motion functionality to help Pippi push a heavier parcel. In this thesis, we propose an approach to automatically generate, at run time, a functional configuration of a distributed robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. In order to handle tasks that require more than one step (i.e., one configuration) to be accomplished, we also show how methods for automatic configuration can be integrated with methods for task planning to produce a complete plan were each step is a configuration. For the scenario above, generating a complete plan before the execution starts enables Pippi to know before hand if she will be able to get the parcel or not. We also propose an approach to merge configurations, which enables concurrent execution of configurations, thus reducing execution time. We demonstrate the applicability of our approach on a specific type of distributed robot system, called Peis-Ecology, and show experiments in which configurations and sequences of configurations are automatically generated and executed on real robots. Further, we give an experiment where merged configurations are created and executed on simulated robots.
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| 9. |
- Pettersson, Ola, 1972-
(author)
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Model-free execution monitoring in behavior-based mobile robotics
- 2004
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Doctoral thesis (other academic/artistic)abstract
- In the near future, autonomous mobile robots are expected to assist us by performing service tasks in many different areas, including transportation, cleaning, mining, or agriculture. In order to manage these tasks in a changing and partially unpredictable environment, without the help of humans, the robot must have the ability to plan its actions and to execute them robustly and in a safe way. Since the real world is dynamic and not fully predictable, the robot must also have the ability to detect when the execution does not proceed as planned, and to correctly identify the causes of the failure. An execution monitoring system is a system that allows the robot to detect and classify these failures. Most current approaches to execution monitoring in robotics are based on the idea of predicting the outcomes of the robot's actions by using some sort of model, and comparing the predicted outcomes with the observed ones. In contrary, this thesis explores the use of model-free approaches to execution monitoring, that is, approaches that do not use predictive models. These approaches solely observe the actual execution of the robot, and detect certain patterns that indicate a problem. In this thesis, we show that pattern recognition techniques can be applied to realize model-free execution monitoring by classifying observed behavioral patterns into normal or faulty behaviors. We investigate the use of several such techniques, and verify their utility in a number of experiments involving the navigation of a mobile robot in indoor environments. Statistical measures are used to compare the results given from several realistic simulations. Our approach has also been successfully tested on a real robot navigating in an office environment. Interesting, this test has shown that we can train a model-free execution monitor in simulation, and then use it in a real robot.
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