| 1. |
- Andersson, Olov, 1979-, et al.
(author)
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Deep Learning Quadcopter Control via Risk-Aware Active Learning
- 2017
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In: Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577357841 ; , s. 3812-3818
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Conference paper (peer-reviewed)abstract
- Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.
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| 2. |
- Andersson, Olov, 1979-, et al.
(author)
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Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
- 2015
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In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577356981 ; , s. 2497-2503
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Conference paper (peer-reviewed)abstract
- Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.
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| 3. |
- Andersson, Olov, 1979-, et al.
(author)
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Real-Time Robotic Search using Structural Spatial Point Processes
- 2020
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In: 35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019). - : Association For Uncertainty in Artificial Intelligence (AUAI). ; , s. 995-1005
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Conference paper (peer-reviewed)abstract
- Aerial robots hold great potential for aiding Search and Rescue (SAR) efforts over large areas, such as during natural disasters. Traditional approaches typically search an area exhaustively, thereby ignoring that the density of victims varies based on predictable factors, such as the terrain, population density and the type of disaster. We present a probabilistic model to automate SAR planning, with explicit minimization of the expected time to discovery. The proposed model is a spatial point process with three interacting spatial fields for i) the point patterns of persons in the area, ii) the probability of detecting persons and iii) the probability of injury. This structure allows inclusion of informative priors from e.g. geographic or cell phone traffic data, while falling back to latent Gaussian processes when priors are missing or inaccurate. To solve this problem in real-time, we propose a combination of fast approximate inference using Integrated Nested Laplace Approximation (INLA), and a novel Monte Carlo tree search tailored to the problem. Experiments using data simulated from real world Geographic Information System (GIS) maps show that the framework outperforms competing approaches, finding many more injured in the crucial first hours.
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| 4. |
- Andersson, Olov, 1979-, et al.
(author)
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Deep RL for Autonomous Robots: Limitations and Safety Challenges
- 2019
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Conference paper (peer-reviewed)abstract
- With the rise of deep reinforcement learning, there has also been a string of successes on continuous control problems using physics simulators. This has lead to some optimism regarding use in autonomous robots and vehicles. However, to successful apply such techniques to the real world requires a firm grasp of their limitations. As recent work has raised questions of how diverse these simulation benchmarks really are, we here instead analyze a popular deep RL approach on toy examples from robot obstacle avoidance. We find that these converge very slowly, if at all, to safe policies. We identify convergence issues on stochastic environments and local minima as problems that warrant more attention for safety-critical control applications.
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| 5. |
- Conte, Gianpaolo, 1974-, et al.
(author)
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An Integrated UAV Navigation System Based on Aerial Image Matching
- 2008
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In: IEEE Aerospace Conference 2008,2008. - : IEEE. - 9781424414871 - 9781424414888 ; , s. 3142-3151
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Conference paper (peer-reviewed)abstract
- The aim of this paper is to explore the possibility of using geo-referenced satellite or aerial images to augment an Unmanned Aerial Vehicle (UAV) navigation system in case of GPS failure. A vision based navigation system which combines inertial sensors, visual odometer and registration of a UAV on-board video to a given geo-referenced aerial image has been developed and tested on real flight-test data. The experimental results show that it is possible to extract useful position information from aerial imagery even when the UAV is flying at low altitude. It is shown that such information can be used in an automated way to compensate the drift of the UAV state estimation which occurs when only inertial sensors and visual odometer are used.
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| 6. |
- Conte, Gianpaolo, 1974-, et al.
(author)
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High Accuracy Ground Target Geo-Location Using Autonomous Micro Aerial Vehicle Platforms
- 2008
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In: Proceedings of the AIAA Guidance, Navigation, and Control Conference (GNC). - : AIAA. - 9781563479458
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Conference paper (peer-reviewed)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.
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