| 1. |
- Andersson, Olov, 1979-, et al.
(författare)
-
Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization
- 2016
-
Ingår i: IEEE International Conference on Robotics and Automation (ICRA), 2016. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4597-4604
-
Konferensbidrag (refereegranskat)abstract
- Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.
|
|
| 2. |
- Conte, Gianpaolo, 1974-, et al.
(författare)
-
High Accuracy Ground Target Geo-Location Using Autonomous Micro Aerial Vehicle Platforms
- 2008
-
Ingår i: Proceedings of the AIAA Guidance, Navigation, and Control Conference (GNC). - : AIAA. - 9781563479458
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a method for high accuracy ground target localization using a Micro Aerial Vehicle (MAV) equipped with a video camera sensor. The proposed method is based on a satellite or aerial image registration technique. The target geo-location is calculated by registering the ground target image taken from an on-board video camera with a geo- referenced satellite image. This method does not require accurate knowledge of the aircraft position and attitude, therefore it is especially suitable for MAV platforms which do not have the capability to carry accurate sensors due to their limited payload weight and power resources. The paper presents results of a ground target geo-location experiment based on an image registration technique. The platform used is a MAV prototype which won the 3rd US-European Micro Aerial Vehicle Competition (MAV07). In the experiment a ground object was localized with an accuracy of 2.3 meters from a ight altitude of 70 meters.
|
|
| 3. |
- Doherty, Patrick, et al.
(författare)
-
A Collaborative Framework for 3D Mapping using Unmanned Aerial Vehicles
- 2016
-
Ingår i: PRIMA 2016: Principles and Practice of Multi-Agent Systems. - Cham : Springer Publishing Company. - 9783319448312 ; , s. 110-130
-
Konferensbidrag (refereegranskat)abstract
- This paper describes an overview of a generic framework for collaboration among humans and multiple heterogeneous robotic systems based on the use of a formal characterization of delegation as a speech act. The system used contains a complex set of integrated software modules that include delegation managers for each platform, a task specification language for characterizing distributed tasks, a task planner, a multi-agent scan trajectory generation and region partitioning module, and a system infrastructure used to distributively instantiate any number of robotic systems and user interfaces in a collaborative team. The application focusses on 3D reconstruction in alpine environments intended to be used by alpine rescue teams. Two complex UAV systems used in the experiments are described. A fully autonomous collaborative mission executed in the Italian Alps using the framework is also described.
|
|
| 4. |
- Doherty, Patrick, 1957-, et al.
(författare)
-
A UAV search and rescue scenario with human body detection and geolocalization
- 2007
-
Ingår i: Proceedings of the 20th Australian Joint Conference on Artificial Intelligence (AI). - : Springer Berlin/Heidelberg. - 9783540769262
-
Konferensbidrag (refereegranskat)abstract
- The use of Unmanned Aerial Vehicles (UAVs) which can operate autonomously in dynamic and complex operational environments is becoming increasingly more common. The UAVTech Lab, is pursuing a long term research endeavour related to the development of future aviation systems which try and push the envelope in terms of using and integrating high-level deliberative or AI functionality with traditional reactive and control components in autonomous UAV systems. In order to carry on such research, one requires challenging mission scenarios which force such integration and development. In this paper, one of these challenging emergency services mission scenarios is presented. It involves search and rescue for injured civilians by UAVs. In leg I of the mission, UAVs scan designated areas and try to identify injured civilians. In leg II of the mission, an attempt is made to deliver medical and other supplies to identified victims. We show how far we have come in implementing and executing such a challenging mission in realistic urban scenarios.
|
|
| 5. |
- Doherty, Patrick, 1957-, et al.
(författare)
-
Hastily formed knowledge networks and distributed situation awareness for collaborative robotics
- 2021
-
Ingår i: Autonomous Intelligent Systems. - : Springer. - 2730-616X. ; 1:1
-
Tidskriftsartikel (refereegranskat)abstract
- In the context of collaborative robotics, distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans. We describe this as the creation of Hastily Formed Knowledge Networks (HFKNs). The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans. The information collected ranges from low-level sensor data to high-level semantic knowledge, the latter represented in part as RDF Graphs. The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents. This is done through the distributed synchronization of RDF Graphs shared between agents. High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members. The system is empirically validated and complexity results of the proposed algorithms are provided. Additionally, a field robotics case study is described, where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.
|
|
| 6. |
- Doherty, Patrick, et al.
(författare)
-
HDRC3 - A Distributed Hybrid Deliberative/Reactive Architecture for Unmanned Aircraft Systems
- 2014
-
Ingår i: Handbook of Unmanned Aerial Vehicles. - Dordrecht : Springer Science+Business Media B.V.. - 9789048197064 - 9789048197071 ; , s. 849-952
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter presents a distributed architecture for unmanned aircraft systems that provides full integration of both low autonomy and high autonomy. The architecture has been instantiated and used in a rotorbased aerial vehicle, but is not limited to use in particular aircraft systems. Various generic functionalities essential to the integration of both low autonomy and high autonomy in a single system are isolated and described. The architecture has also been extended for use with multi-platform systems. The chapter covers the full spectrum of functionalities required for operation in missions requiring high autonomy. A control kernel is presented with diverse flight modes integrated with a navigation subsystem. Specific interfaces and languages are introduced which provide seamless transition between deliberative and reactive capability and reactive and control capability. Hierarchical Concurrent State Machines are introduced as a real-time mechanism for specifying and executing low-level reactive control. Task Specification Trees are introduced as both a declarative and procedural mechanism for specification of high-level tasks. Task planners and motion planners are described which are tightly integrated into the architecture. Generic middleware capability for specifying data and knowledge flow within the architecture based on a stream abstraction is also described. The use of temporal logic is prevalent and is used both as a specification language and as an integral part of an execution monitoring mechanism. Emphasis is placed on the robust integration and interaction between these diverse functionalities using a principled architectural framework. The architecture has been empirically tested in several complex missions, some of which are described in the chapter.
|
|
| 7. |
|
|
| 8. |
- Heintz, Fredrik, 1975-, et al.
(författare)
-
Bridging the Sense-Reasoning Gap Using DyKnow : A Knowledge Processing Middleware Framework
- 2007
-
Ingår i: Proceedings of the 30th Annual German Conference on Artificial Intelligence (KI). - Berlin, Heidelberg : Springer. - 9783540745648 ; , s. 460-463
-
Konferensbidrag (refereegranskat)abstract
- To achieve complex missions an autonomous unmanned aerial vehicle (UAV) operating in dynamic environments must have and maintain situational awareness. This can be achieved by continually gathering information from many sources, selecting the relevant information for current tasks, and deriving models about the environment and the UAV itself. It is often the case models suitable for traditional control, are not sufficient for deliberation. The need for more abstract models creates a sense-reasoning gap. This paper presents DyKnow, a knowledge processing middleware framework, and shows how it supports bridging the gap in a concrete UAV traffic monitoring application. In the example, sequences of color and thermal images are used to construct and maintain qualitative object structures. They model the parts of the environment necessary to recognize traffic behavior of tracked vehicles in real-time. The system has been implemented and tested in simulation and on data collected during flight tests.
|
|
| 9. |
- Heintz, Fredrik, 1975-, et al.
(författare)
-
From Images to Traffic Behavior - A UAV Tracking and Monitoring Application
- 2007
-
Ingår i: Proceedings of the 10th International Conference on Information Fusion (FUSION). - : IEEE conference proceedings. - 9780662458043 - 9780662478300
-
Konferensbidrag (refereegranskat)abstract
- An implemented system for achieving high level situation awareness about traffic situations in an urban area is described. It takes as input sequences of color and thermal images which are used to construct and maintain qualitative object structures and to recognize the traffic behavior of the tracked vehicles in real time. The system is tested both in simulation and on data collected during test flights. To facilitate the signal to symbol transformation and the easy integration of the streams of data from the sensors with the GIS and the chronicle recognition system, DyKnow, a stream-based knowledge processing middleware, is used. It handles the processing of streams, including the temporal aspects of merging and synchronizing streams, and provides suitable abstractions to allow high level reasoning and narrow the sense reasoning gap.
|
|
| 10. |
- Häger, Gustav, 1988-, et al.
(författare)
-
Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV
- 2016
-
Ingår i: Proceedings of the 12th International Symposium on Advances in Visual Computing. - Cham : Springer. - 9783319508344 - 9783319508351
-
Konferensbidrag (refereegranskat)abstract
- Visual object tracking performance has improved significantly in recent years. Most trackers are based on either of two paradigms: online learning of an appearance model or the use of a pre-trained object detector. Methods based on online learning provide high accuracy, but are prone to model drift. The model drift occurs when the tracker fails to correctly estimate the tracked object’s position. Methods based on a detector on the other hand typically have good long-term robustness, but reduced accuracy compared to online methods.Despite the complementarity of the aforementioned approaches, the problem of fusing them into a single framework is largely unexplored. In this paper, we propose a novel fusion between an online tracker and a pre-trained detector for tracking humans from a UAV. The system operates at real-time on a UAV platform. In addition we present a novel dataset for long-term tracking in a UAV setting, that includes scenarios that are typically not well represented in standard visual tracking datasets.
|
|
