| 1. |
- Bouguerra, Abdelbaki, et al.
(författare)
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Handling uncertainty in semantic-knowledge based execution monitoring
- 2007
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Ingår i: IEEE/RSJ international conference on intelligent robots and systems, IROS 2007 San Diego, CA, 2007. - New York : IEEE. - 9781424409129 ; , s. 437-443
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Konferensbidrag (refereegranskat)abstract
- Executing plans by mobile robots, in real world environments, faces the challenging issues of uncertainty and environment dynamics. Thus, execution monitoring is needed to verify that plan actions are executed as expected. Semantic domain-knowledge has lately been proposed as a source of information to derive and monitor implicit expectations of executing actions. For instance, when a robot moves into a room asserted to be an office, it would expect to see a desk and a chair. We propose to extend the semantic knowledge-based execution monitoring to take uncertainty in actions and sensing into account when verifying the expectations derived from semantic knowledge. We consider symbolic probabilistic action models, and show how semantic knowledge is used together with a probabilistic sensing model in the monitoring process of such actions. Our approach is illustrated by showing test scenarios run in an indoor environment using a mobile robot.
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| 2. |
- Lundh, Robert, et al.
(författare)
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Automatic configuration of multi-robot systems : planning for multiple steps
- 2008
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Ingår i: Proceeding of the 2008 conference on ECAI 2008. - Amsterdam : IOS Press. - 9781586038915 ; , s. 616-620
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Konferensbidrag (refereegranskat)abstract
- We consider multi-robot systems where robots need to cooperate tightly by sharing functionalities with each other. There are methods for automatically configuring a multi-robot system for tight cooperation, but they only produce a single configuration. In this paper, we show how methods for automatic configuration can be integrated with methods for task planning in order to produce a complete plan were each step is a configuration. We also consider the issues of monitoring and replanning in this context, and we demonstrate our approach on a real multi-robot system, the PEIS-Ecology
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| 3. |
- Lundh, Robert, et al.
(författare)
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Dynamic self-configuration of an ecology of robots
- 2007
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Ingår i: IEEE/RSJ international conference on intelligent robots and systems, 2007. IROS 2007. - NEW YORK : IEEE. - 9781424409129 ; , s. 3403-3409
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Konferensbidrag (refereegranskat)abstract
- There is a tendency today toward the study of distributed systems consisting of many heterogeneous, networked, cooperating robotic devices. We refer to a system of this type as an ecology of robots. We call functional configuration of this ecology a way to allocate and connect functionalities among its robots. In general, the same ecology can perform different tasks by using different configuration. Moreover, the same task can often be solved using different configurations, and which is the best one depends on the available resources. This potential flexibility of a robot ecology is reduced by the fact that, in most current approaches, configurations are pre-programmed by hand. In this paper, we propose a plan-based approach to automatically generate a preferred configuration of a robot ecology given a task, environment, and set of resources. In contrast to previous approaches, the state of the ecology isautomatically acquired at planning time, and it is monitored during execution in order to reconfigure if a functionality fails. We illustrate these ideas on a specific instance of an ecology of robots, called PEIS Ecology. We also show an experiment run on our PEIS Ecology testbed, in which a robot needs to reconfigure when the original configuration fails.
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| 4. |
- Bidot, Julien, 1977-, et al.
(författare)
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Geometric backtracking for combined task and motion planning in robotic systems
- 2017
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Ingår i: Artificial Intelligence. - : Elsevier. - 0004-3702 .- 1872-7921. ; 247, s. 229-265
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Tidskriftsartikel (refereegranskat)abstract
- Planners for real 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 to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse.
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| 5. |
- Bidot, Julien, et al.
(författare)
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Geometric backtracking for combined task and path planning in robotic systems
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Annan publikation (övrigt vetenskapligt/konstnärligt)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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| 6. |
- Bouguerra, Abdelbaki, et al.
(författare)
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Active execution monitoring using planning and semantic knowledge
- 2007
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Ingår i: Proc. of the ICAPS Workshop on Planning and Plan Execution for Real-World Systems, Providence, RI, 2007. ; , s. 9-15
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)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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| 7. |
- Bouguerra, Abdelbaki, et al.
(författare)
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Monitoring the execution of robot plans using semantic knowledge
- 2008
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Ingår i: Robotics and Autonomous Systems. - Amsterdam : North-Holland Publishing Co. - 0921-8890 .- 1872-793X. ; 56:11, s. 942-954
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Tidskriftsartikel (refereegranskat)abstract
- Even the best laid plans can fail, and robot plans executed in real world domains tend to do so often. The ability of a robot to reliably monitor the execution of plans and detect failures is essential to its performance and its autonomy. In this paper, we propose a technique to increase the reliability of monitoring symbolic robot plans. We use semantic domain knowledge to derive implicit expectations of the execution of actions in the plan, and then match these expectations against observations. We present two realizations of this approach: a crisp one, which assumes deterministic actions and reliable sensing, and uses a standard knowledge representation system (LOOM); and a probabilistic one, which takes into account uncertainty in action effects, in sensing, and in world states. We perform an extensive validation of these realizations through experiments performed both in simulation and on real robots.
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| 8. |
- Bouguerra, Abdelbaki, 1974-
(författare)
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Robust execution of robot task-plans : a knowledge-based approach
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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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| 9. |
- Bouguerra, Abdelbaki, et al.
(författare)
-
Semantic knowledge-based execution monitoring for mobile robots
- 2007
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Ingår i: 2007 IEEE international conference on robotics and automation (ICRA). - 1424406013 ; , s. 3693-3698
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Konferensbidrag (refereegranskat)abstract
- We describe a novel intelligent execution monitoring approach for mobile robots acting in indoor environments such as offices and houses. Traditionally, monitoring execution in mobile robotics amounted to looking for discrepancies between the model-based predicted state of executing an action and the real world state as computed from sensing data. We propose to employ semantic knowledge as a source of information to monitor execution. The key idea is to compute implicit expectations, from semantic domain information, that can be observed at run time by the robot to make sure actions are executed correctly. We present the semantic knowledge representation formalism, and how semantic knowledge is used in monitoring. We also describe experiments run in an indoor environment using a real mobile robot
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| 10. |
- Bouguerra, Abdelbaki, et al.
(författare)
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Situation assessment for sensor-based recovery planning
- 2006
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Ingår i: 17th European Conference on Artificial Intelligence (ECAI). - : IOS Press. - 1586036424 ; , s. 673-677
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Konferensbidrag (refereegranskat)abstract
- We present an approach for recovery from perceptual failures, or more precisely anchoring failures. Anchoring is the problem of connecting symbols representing objects to sensor data corresponding to the same objects. The approach is based on using planning, but our focus is not on the plan generation per se. We focus on the very important aspect of situation assessment and how it is carried out for recovering from anchoring failures. The proposed approach uses background knowledge to create hypotheses about world states and handles uncertainty in terms of probabilistic belief states. This work is relevant both from the perspective of developing the anchoring framework, and as a study in plan-based recovery from epistemic failures in mobile robots. Experiments on a mobile robot are shown to validate the applicability of the proposed approach.
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