| 1. |
- Almeida, Diogo, 1991-, et al.
(författare)
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Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2017
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Ingår i: Warehouse Picking Automation Workshop 2017.
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Konferensbidrag (populärvet., debatt m.m.)abstract
- In this work we summarize the solution developed by Team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition simulated a warehouse automation scenario and it was divided in two tasks: a picking task where a robot picks items from a shelf and places them in a tote and a stowing task which is the inverse task where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting from a high level overview of our system and later delving into details of our perception pipeline and our strategy for manipulation and grasping. The solution was implemented using a Baxter robot equipped with additional sensors.
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| 2. |
- Almeida, Diogo, 1991-, et al.
(författare)
-
Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2020
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Ingår i: Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment. - Cham : Springer Nature. ; , s. 53-62
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter we summarize the solution developed by team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition, which simulated a warehouse automation scenario, was divided into two parts: a picking task, where the robot picks items from a shelf and places them into a tote, and a stowing task, where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting with a high-level overview of the system, delving later into the details of our perception pipeline and strategy for manipulation and grasping. The hardware platform used in our solution consists of a Baxter robot equipped with multiple vision sensors.
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| 3. |
- Andersson, Klas, 1968-
(författare)
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Improving Fixed Wing UAV Endurance, by Cooperative Autonomous Soaring
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The ever-expanding use and development of smaller UAVs (Unmanned Aerial Vehicles) has highlighted an increasing demand for extended range and endurance for this type of vehicles. In this thesis, the development of a concept and system for autonomous soaring of cooperating unmanned aerial vehicles is presented. The purpose of the developed system is to extend endurance by harvesting energy available in the atmosphere in the form of thermal updrafts, in a similar way that some birds and manned gliders do. By using this “free” energy, considerable improvements in maximum achievable endurance can be realized under a wide variety of atmospherical and weather conditions. The work included theoretical analysis, simulations, and finally flight test- ing of the soaring controller and the system. The system was initially devel- oped as a single-vehicle concept and thereafter extended into a system consist- ing of two cooperating gliders. The purpose of the extension to cooperation, was to further improve the performance of the system by increasing the ability to locate the rising air of thermal updrafts. The theoretical analysis proved the soaring algorithm’s thermal centering controller to be stable. The trials showed the concept of autonomous soaring to function as expected from the simulations. Further it revealed that, by applying the idea, extensive performance gains can be achieved under a fairly wide variety of conditions. The cooperative soaring, likewise, functioned as anticipated and the glid- ers found, cooperated, and climbed together in updrafts. This represents the first and presumably only time cooperative autonomous soaring in this way, has been successfully demonstrated in flight. To draw further conclusions on the advantages of cooperative soaring additional flight trials would, however, be beneficial. Possible issues and limitations were highlighted during the trials and a number of potential improvements were identified. As a part of the work, trials were conducted to verify the viability to implement the system into “real world” operational scenarios. As a proof of concept this was done by tasking the autonomous gliders to perform data/communications relay missions for other UAV systems sending imagery to the ground-station from beyond line of sight (BLOS). The outcome of the trials was positive and the concept appeared to be well suited for these types of missions. The comms relay system was further developed into a hybrid system where the optimal location concerning relay performance was autonomously sought out, after-which the attentiveness then switched to autonomous thermal soaring in the vicinity of this ideal relay position. The hybrid system was tested in simulation and partially flight tested.
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| 4. |
- Andersson, Olov, 1979-
(författare)
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Methods for Scalable and Safe Robot Learning
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to enter real-world public spaces and homes. However, robot behavior is still usually engineered for narrowly defined scenarios. To manually encode robot behavior that works within complex real world environments, such as busy work places or cluttered homes, can be a daunting task. In addition, such robots may require a high degree of autonomy to be practical, which imposes stringent requirements on safety and robustness. \setlength{\parindent}{2em}\setlength{\parskip}{0em}The aim of this thesis is to examine methods for automatically learning safe robot behavior, lowering the costs of synthesizing behavior for complex real-world situations. To avoid task-specific assumptions, we approach this from a data-driven machine learning perspective. The strength of machine learning is its generality, given sufficient data it can learn to approximate any task. However, being embodied agents in the real-world, robots pose a number of difficulties for machine learning. These include real-time requirements with limited computational resources, the cost and effort of operating and collecting data with real robots, as well as safety issues for both the robot and human bystanders.While machine learning is general by nature, overcoming the difficulties with real-world robots outlined above remains a challenge. In this thesis we look for a middle ground on robot learning, leveraging the strengths of both data-driven machine learning, as well as engineering techniques from robotics and control. This includes combing data-driven world models with fast techniques for planning motions under safety constraints, using machine learning to generalize such techniques to problems with high uncertainty, as well as using machine learning to find computationally efficient approximations for use on small embedded systems.We demonstrate such behavior synthesis techniques with real robots, solving a class of difficult dynamic collision avoidance problems under uncertainty, such as induced by the presence of humans without prior coordination. Initially using online planning offloaded to a desktop CPU, and ultimately as a deep neural network policy embedded on board a 7 quadcopter.
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| 5. |
- Anisi, David A., 1977-, et al.
(författare)
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Algorithms for the connectivity constrained unmanned ground vehicle surveillance problem
- 2009
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Ingår i: European Control Conference (ECC). - Budapest, Hungary : EUCA.
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Konferensbidrag (refereegranskat)abstract
- The Connectivity Constrained UGV Surveillance Problem (CUSP) considered in this paper is the following. Given a set of surveillance UGVs and a user defined area to be covered, find waypoint-paths such that; 1) the area is completely surveyed, 2) the time for performing the search is minimized and 3) the induced information graph is kept recurrently connected. It has previously been shown that the CUSP is NP-hard. This paper presents four different heuristic algorithms for solving the CUSP, namely, the Token Station Algorithm, the Stacking Algorithm, the Visibility Graph Algorithm and the Connectivity Primitive Algorithm. These algorithms are then compared by means of Monte Carlo simulations. The conclusions drawn are that the Token Station Algorithm provides the most optimal solutions, the Stacking Algorithm has the lowest computational complexity, while the Connectivity Primitive Algorithm provides the best trade-off between optimality and computational complexity for larger problem instances.
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| 6. |
- Anisi, David A., 1977-, et al.
(författare)
-
Communication constrained multi-UGV surveillance
- 2008
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Ingår i: IFAC World Congress. - Seoul, Korea.
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of connectivity constrained surveillance of a given polyhedral area with obstacles using a group of Unmanned Ground Vehicles (UGVs). The considered communication restrictions may involve both line-of-sight constraints and limited sensor range constraints. In this paper, the focus is on dynamic information graphs, G, which are required to be kept recurrently connected. The main motivation for introducing this weaker notion of connectivity is security and surveillance applications where the sentry vehicles may have to split temporary in order to complete the given mission efficiently but are required to establish contact recurrently in order to exchange information or to make sure that all units are intact and well-functioning. From a theoretical standpoint, recurrent connectivity is shown to be sufficient for exponential convergence of consensus filters for the collected sensor data.
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| 7. |
- Anisi, David A., et al.
(författare)
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Cooperative Minimum Time Surveillance With Multiple Ground Vehicles
- 2010
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Ingår i: IEEE Transactions on Automatic Control. - 0018-9286 .- 1558-2523. ; 55:12, s. 2679-2691
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we formulate and solve two different minimum time problems related to unmanned ground vehicle (UGV) surveillance. The first problem is the following. Given a set of surveillance UGVs and a polyhedral area, find waypoint-paths for all UGVs such that every point of the area is visible from a point on a path and such that the time for executing the search in parallel is minimized. Here, the sensors' field of view are assumed to have a limited coverage range and be occluded by the obstacles. The second problem extends the first by additionally requiring the induced information graph to be connected at the time instants when the UGVs perform the surveillance mission, i.e., when they gather and transmit sensor data. In the context of the second problem, we also introduce and utilize the notion of recurrent connectivity, which is a significantly more flexible connectivity constraint than, e.g., the 1-hop connectivity constraints and use it to discuss consensus filter convergence for the group of UGVs.
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| 8. |
- Anisi, David A., et al.
(författare)
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Cooperative Surveillance Missions with Multiple Unmanned Ground Vehicles (UGVs)
- 2008
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Ingår i: 47TH IEEE CONFERENCE ON DECISION AND CONTROL, 2008 (CDC 2008). - 9781424431243 ; , s. 2444-2449
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Konferensbidrag (refereegranskat)abstract
- This paper proposes an optimization based approach to multi-UGV surveillance. In particular, we formulate both the minimum time- and connectivity constrained surveillance problems, show NP-hardness of them and propose decomposition techniques that allow us to solve them efficiently in an algorithmic manner. The minimum time formulation is the following. Given a set of surveillance UGVs and a polyhedral area, find waypoint-paths for all UGVs such that every point of the area is visible from a point on a path and such that the time for executing the search in parallel is minimized. Here, the sensor's field of view are assumed to be occluded by the obstacles and limited by a maximal sensor range. The connectivity constrained formulation extends the first by additionally requiring that the information graph induced by the sensors is connected at the time instants when the UGVs stop to perform the surveillance task. The second formulation is relevant to situation when mutual visibility is needed either to transmit the sensor data being gathered, or to protect the team from hostile persons trying to approach the stationary UGVs.
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| 9. |
- Anisi, David A., 1977-, et al.
(författare)
-
Minimum time multi-UGV surveillance
- 2008
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Ingår i: OPTIMIZATION AND COOPERATIVE CONTROL STRATEGIES. - Berlin : Springer Verlag. - 9783540880622 ; , s. 31-45
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of concurrent task- and path planning for a number of surveillance Unmanned Ground Vehicles (UGVs) such that a user defined area of interest is covered by the UGVs' sensors in minimum time. We first formulate the problem, and show that it is in fact a generalization of the Multiple Traveling Salesmen Problem (MTSP), which is known to be NP-hard. We then propose a solution that decomposes the problem into three subproblems. The first is to find a maximal convex covering of the search area. Most results on static coverage use disjoint partitions of the search area, e.g. triangulation, to convert the continuous sensor positioning problem into a discrete one. However, by a simple example, we show that a highly overlapping set of maximal convex sets is better suited for minimum time coverage. The second subproblem is a combinatorial assignment and ordering of the sets in the cover. Since Tabu search algorithms are known to perform well on various routing problems, we use it as a part of our proposed solution. Finally, the third subproblem utilizes a particular shortest path sub-routine in order to find the vehicle paths, and calculate the overall objective function used in the Tabu search. The proposed algorithm is illustrated by a number of simulation examples.
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| 10. |
- Anisi, David A., et al.
(författare)
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Safe receding horizon control of an aerial vehicle
- 2006
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Ingår i: PROCEEDINGS OF THE 45TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14. - : IEEE. - 9781424401703 ; , s. 57-62
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of designing a real time high performance controller and trajectory generator for air vehicles. The control objective is to use information about terrain and enemy threats to fly low and avoid radar exposure on the way to a given target. The proposed algorithm builds on the well known approach of Receding Horizon Control (RHC) combined with a terminal cost, calculated from a graph representation of the environment. Using a novel safety maneuver, and under an assumption on the maximal terrain inclination, we are able to prove safety as well as task completion. The safety maneuver is incorporated in the short term optimization, which is performed using Nonlinear Programming (NLP). Some key characteristics of the trajectory planner are highlighted through simulations.
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