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Papers by Abdelbaki Bouguerra

To cope with the dynamics and uncertainty inherent in real world environments, autonomous mobile ... more To cope with the dynamics and uncertainty inherent in real world environments, autonomous mobile robots need to per- form 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 mon- itor implicit expectations of executing actions. For instance, when the robot moves into an

European Conference on Artificial Intelligence, 2006

We present an approach for recovery from perceptual failures, or more precisely anchoring failure... more We present an approach for recovery from perceptual failures, or more precisely anchoring failures. Anchoring is the prob- lem of connecting symbols representing objects to sensor data corre- sponding to the same objects. The approach is based on using plan- ning, but our focus is not on the plan generation per se. We focus on the very important aspectof situation

Intelligenza Artificiale, 2005

SOMMARIO/ABSTRACT In this paper we report on the extension of the classical

Ai Communications, 2008

An autonomous robot using symbolic reasoning, sensing and acting in a real environment needs the ... more An autonomous robot using symbolic reasoning, sensing and acting in a real environment needs the ability to create and maintain the connection between symbols representing objects in the world and the corresponding perceptual representations given by its sensors. This connection has been named perceptual anchoring. In complex environments, anchoring is not always easy to establish: the situation may often be ambiguous as to which percept actually corresponds to a given symbol. In this paper, we extend perceptual anchoring to deal robustly with ambiguous situations by providing general methods for detecting them and recovering from them. We consider different kinds of ambiguous situations. We also present methods to recover from these situations based on automatically formulating them as conditional planning problems that then are solved by a planner. We illustrate our approach by showing experiments involving a mobile robot equipped with a color camera and an electronic nose.

Reasoning with Uncertainty in Robotics, 2005

In this paper we report on the integration of a high-level plan executor with a behavior-based ar... more In this paper we report on the integration of a high-level plan executor with a behavior-based architec- ture. The executor is designed to execute plans that solve problems in partially observable domains. We discuss the different modules of the overall architecture and how we made the different modules interact using a shared representation. We also give a detailed description of

In this paper we present an algorithm for planning in non- deterministic domains. Our algorithm C... more In this paper we present an algorithm for planning in non- deterministic domains. Our algorithm C-SHOP extends the successful classical HTN planner SHOP, by introducing new mechanisms to handle situations where there is incomplete and uncertain information about the state of the environment. Being an HTN planner, C-SHOP supports coding domain-dependent knowledge in a powerful way that describes how to

2014 IEEE International Conference on Robotics and Automation (ICRA), 2014

In this paper, we propose an approach for robotic manipulation systems to autonomously reason abo... more In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target application is to automate the task of unloading the content of shipping containers. Our goal is to capture possible support relations between objects in partially known static configurations. We employ support vector machines (SVM) to estimate the probability of a support relation between pairs of detected objects using features extracted from their geometrical properties and 3D sampled points of the scene. The set of probabilistic support relations is then used for reasoning about optimally selecting an object to be unloaded first. The proposed approach has been extensively tested and verified on data sets generated in simulation and from real world configurations.

Springer Tracts in Advanced Robotics, 2013

The main focus of this paper is to present a case study of a SLAM solution for Automated Guided V... more The main focus of this paper is to present a case study of a SLAM solution for Automated Guided Vehicles (AGVs) operating in real-world industrial environments. The studied solution, called Gold-fish SLAM, was implemented to provide localization estimates in dynamic industrial environments, where there are static landmarks that are only rarely perceived by the AGVs. The main idea of Gold-fish SLAM is to consider the goods that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks to compute online estimates of AGV poses. The solution is tested and verified in a factory of paper using an eight ton diesel-truck retrofitted with an AGV control system running at speeds up to 3 meters per second. The paper includes also a general discussion on how SLAM can be used in industrial applications with AGVs.

Lecture Notes in Computer Science, 2005

Intelligent agents acting in real world environments need to synthesize their course of action ba... more Intelligent agents acting in real world environments need to synthesize their course of action based on multiple sources of knowledge. They also need to generate plans that smoothly integrate actions from different domains. In this paper we present a generic approach to synthesize plans for solving planning problems involving multiple domains. The proposed approach performs search hierarchically by starting planning in one domain and considering subgoals related to the other domains as abstract tasks to be planned for later when their respective domains are considered. To plan in each domain, a domain-dependent planner can be used, making it possible to integrate different planners, possibly with different specializations. We outline the algorithm, and the assumptions underlying its functionality. We also demonstrate through a detailed example, how the proposed framework compares to planning in one global domain.

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007

Executing plans by mobile robots, in real world environments, faces the challenging issues of unc... more 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.

Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007

We describe a novel intelligent execution monitoring approach for mobile robots acting in indoor ... more 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.

Robotics and Autonomous Systems, 2008

Even the best laid plans can fail, and robot plans executed in real world domains tend to do so o... more 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.