Abdelbaki Bouguerra

Monitoring the execution of robot plans using semantic knowledge

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.

Handling uncertainty in semantic-knowledge based execution monitoring

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 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.

Semantic Knowledge-Based Execution Monitoring for Mobile Robots

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.

Autonomous Transport Vehicles: Where We Are and What Is Missing

In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies.

Automatic relational scene representation for safe robotic manipulation tasks

In this paper, we propose a new approach for automatically building symbolic relational descriptions of static configurations of objects to be manipulated by a robotic system. The main goal of our work is to provide advanced cognitive abilities for such robotic systems to make them more aware of the outcome of their actions. We describe how such symbolic relations are automatically extracted for configurations of box-shaped objects using notions from geometry and static equilibrium in classical mechanics. We also present extensive simulation results as well as some real-world experiments aimed at verifying the output of the proposed approach.

An autonomous robotic system for load transportation

This paper presents an overview of an autonomous robotic system for material handling. The system is being developed by extending the functionalities of traditional AGVs to be able to operate reliably and safely in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires runtime object detection and tracking. Another requirement to be fulfilled by the system is the ability to generate trajectories dynamically, which is uncommon in industrial AGV systems.

Support relation analysis and decision making for safe robotic manipulation tasks

In this article, we describe an approach to address the issue of automatically building and using high-level symbolic representations that capture physical interactions between objects in static co ...

Probabilistic relational scene representation and decision making under incomplete information for robotic manipulation tasks

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.

Navigation in human-robot and robot-robot interaction using optimization methods

Human-robot interaction and robot-robot interaction and cooperation in shared spatial areas is a challenging field of research regarding safety, stability and performance. In this paper the collision avoidance between human and robot by extrapolation of human intentions and a suitable optimization of tracking velocities is discussed. Furthermore for robot-robot interactions in a shared area traffic rules and artificial force potential fields and their optimization by market-based approach are applied for obstacle avoidance. For testing and verification, the navigation strategy is implemented and tested in simulation of more realistic vehicles. Extensive simulation experiments are performed to examine the improvement of the traditional potential field (PF) method by the MBO strategy.

Multi-Robot Navigation Using Market-Based Optimization

This paper deals with artificial force potential fields for obstacle avoidance and their optimization by a market-based approach in scenarios where several robots are acting in a shared area. Specifically, the potential field method is enhanced by fuzzy logic, traffic rules, and market-based optimization (MBO). Fuzzy rules are used to deform repulsive potential fields in the vicinity of obstacles to produce smoother motions around them. Traffic rules are used to deal with situations where robots are crossing each other. MBO, on the other hand, is used to strengthen or weaken repulsive potential fields generated due to the presence of other robots. For testing and verification, the navigation strategy is implemented and tested in simulation of more realistic vehicles. Issues while implementing this method and limitations of this navigation strategy are also discussed. Extensive simulation experiments are performed to examine the improvement of the traditional potential field (PF) method by the MBO strategy.