| 1. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Autonomous transport vehicles : where we are and what is missing
- 2015
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Ingår i: IEEE robotics & automation magazine. - 1070-9932 .- 1558-223X. ; 22:1, s. 64-75
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Gold-fish SLAM : An application of SLAM to localize AGVs
- 2014
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Ingår i: Field and Service Robotics. - Heidelberg : Springer. - 9783642406850 - 9783642406867 ; , s. 585-598
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Konferensbidrag (refereegranskat)abstract
- 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 3m/s. The paper includes also a general discussion on how SLAM can be used in industrial applications with AGVs. © Springer-Verlag Berlin Heidelberg 2014.
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| 3. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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Gold-fish SLAM : an application of SLAM to localize AGVs
- 2012
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Ingår i: Proceedings of the International Conference on Field and Service Robotics (FSR), July 2012..
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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 environ- ments. 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 sys- tem 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.
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| 4. |
- Bouguerra, Abdelbaki, 1974-, et al.
(författare)
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An autonomous robotic system for load transportation
- 2009
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Ingår i: 2009 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2009). - New York : IEEE conference proceedings. - 9781424427277 - 9781424427284 ; , s. 1563-1566
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Konferensbidrag (refereegranskat)abstract
- This paper presents an overview of an autonomous robotic material handling system. The goal of the system is to extend the functionalities of traditional AGVs to operate 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 that the system be able to detect and track object positions at runtime. Another requirement of the system is to be able to generate trajectories dynamically, which is uncommon in industrial AGV systems.
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| 5. |
- 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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| 6. |
- Mojtahedzadeh, Rasoul, 1977-, et al.
(författare)
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Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks
- 2014
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Ingår i: Robotics and Automation (ICRA), 2014 IEEE International Conference on. - : IEEE Robotics and Automation Society. - 9781479936854 ; , s. 5685-5690
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 7. |
- Mojtahedzadeh, Rasoul, 1977-, et al.
(författare)
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Support relation analysis and decision making for safe robotic manipulation tasks
- 2015
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Ingår i: Robotics and Autonomous Systems. - Amsterdam : Elsevier. - 0921-8890 .- 1872-793X. ; 71:SI, s. 99-117
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Tidskriftsartikel (refereegranskat)abstract
- 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 configurations. Our work targets robotic manipulation systems where objects need to be safely removed from piles that come in random configurations. We assume that a 3D visual perception module exists so that objects in the piles can be completely or partially detected. Depending on the outcome of the perception, we divide the issue into two sub-issues: 1) all objects in the configuration are detected; 2) only a subset of objects are correctly detected. For the first case, we use notions from geometry and static equilibrium in classical mechanics to automatically analyze and extract act and support relations between pairs of objects. For the second case, we use machine learning techniques to estimate the probability of objects supporting each other. Having the support relations extracted, a decision making process is used to identify which object to remove from the configuration so that an expected minimum cost is optimized. The proposed methods have been extensively tested and validated on data sets generated in simulation and from real world configurations for the scenario of unloading goods from shipping containers.
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