| 1. |
- Boström-Rost, Per, 1988-
(författare)
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On Informative Path Planning for Tracking and Surveillance
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis studies a class of sensor management problems called informative path planning (IPP). Sensor management refers to the problem of optimizing control inputs for sensor systems in dynamic environments in order to achieve operational objectives. The problems are commonly formulated as stochastic optimal control problems, where to objective is to maximize the information gained from future measurements. In IPP, the control inputs affect the movement of the sensor platforms, and the goal is to compute trajectories from where the sensors can obtain measurements that maximize the estimation performance. The core challenge lies in making decisions based on the predicted utility of future measurements.In linear Gaussian settings, the estimation performance is independent of the actual measurements. This means that IPP becomes a deterministic optimal control problem, for which standard numerical optimization techniques can be applied. This is exploited in the first part of this thesis. A surveillance application is considered, where a mobile sensor is gathering information about features of interest while avoiding being tracked by an adversarial observer. The problem is formulated as an optimization problem that allows for a trade-off between informativeness and stealth. We formulate a theorem that makes it possible to reformulate a class of nonconvex optimization problems with matrix-valued variables as convex optimization problems. This theorem is then used to prove that the seemingly intractable IPP problem can be solved to global optimality using off-the-shelf optimization tools.The second part of this thesis considers tracking of a maneuvering target using a mobile sensor with limited field of view. The problem is formulated as an IPP problem, where the goal is to generate a sensor trajectory that maximizes the expected tracking performance, captured by a measure of the covariance matrix of the target state estimate. When the measurements are nonlinear functions of the target state, the tracking performance depends on the actual measurements, which depend on the target’s trajectory. Since these are unavailable in the planning stage, the problem becomes a stochastic optimal control problem. An approximation of the problem based on deterministic sampling of the distribution of the predicted target trajectory is proposed. It is demonstrated in a simulation study that the proposed method significantly increases the tracking performance compared to a conventional approach that neglects the uncertainty in the future target trajectory.
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| 2. |
- Colledanchise, Michele
(författare)
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Behavior Trees in Robotics
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Behavior Trees (BTs) are a Control Architecture (CA) that was invented in the video game industry, for controlling non-player characters. In this thesis we investigate the possibilities of using BTs for controlling autonomous robots, from a theoretical as well as practical standpoint. The next generation of robots will need to work, not only in the structured assembly lines of factories, but also in the unpredictable and dynamic environments of homes, shops, and other places where the space is shared with humans, and with different and possibly conflicting objectives. The nature of these environments makes it impossible to first compute the long sequence of actions needed to complete a task, and then blindly execute these actions. One way of addressing this problem is to perform a complete re-planning once a deviation is detected. Another way is to include feedback in the plan, and invoke additional incremental planning only when outside the scope of the feedback built into the plan. However, the feasibility of the latter option depends on the choice of CA, which thereby impacts the way the robot deals with unpredictable environments. In this thesis we address the problem of analyzing BTs as a novel CA for robots. The philosophy of BTs is to create control policies that are both modular and reactive. Modular in the sense that control policies can be separated and recombined, and reactive in the sense that they efficiently respond to events that were not predicted, either caused by external agents, or by unexpected outcomes of robot's own actions. Firstly, we propose a new functional formulation of BTs that allows us to mathematically analyze key system properties using standard tools from robot control theory. In particular we analyze whenever a BT is safe, in terms of avoiding particular parts of the state space; and robust, in terms of having a large domain of operation. This formulation also allows us to compare BTs with other commonly used CAs such as Finite State Machines (FSMs); the Subsumption Architecture; Sequential Behavior Compositions; Decision Trees; AND-OR Trees; and Teleo-Reactive Programs. Then we propose a framework to systematically analyze the efficiency and reliability of a given BT, in terms of expected time to completion and success probability. By including these performance measures in a user defined objective function, we can optimize the order of different fallback options in a given BT for minimizing such function. Finally we show the advantages of using BTs within an Automated Planning framework. In particular we show how to synthesize a policy that is reactive, modular, safe, and fault tolerant with two different approaches: model-based (using planning), and model-free (using learning).
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| 3. |
- Sprague, Christopher
(författare)
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Efficient and Trustworthy Artificial Intelligence for Critical Robotic Systems
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Critical robotic systems are systems whose functioning is critical to both ensuring the accomplishment of a given mission and preventing the endangerment of life and the surrounding environment. These critical aspects can be formally captured by convergence, in the sense that the system's state goes to a desired region of the statespace, and safety, in the sense that the system's state avoids unsafe regions of the statespace. Data-driven control policies, found through e.g. imitation learning or reinforcement learning, can outperform model-based methods in achieving convergence and safety efficiently; however, they often only do so by encouraging them, thus, they can be difficult to trust. Model-based control policies, on the other hand, are often well-suited to admitting formal guarantees of convergence and safety, thus they are often easier to trust. The main question asked in this thesis is: how can we compose data-driven and model-based control policies together to encourage efficiency while, at the same time, formally guaranteeing convergence and safety?We answer this question with behaviour trees, a framework to represent hybrid control systems in a modular way. We present the first formal definition of behaviour trees as a hybrid system and present the conditions under which the execution of any behaviour tree as a hybrid control system will formally guarantee convergence and safety. Moreover, we present the conditions under which such formal guarantees can be maintained when including unguaranteed data-driven control policies, such as those coming from imitation learning or reinforcement learning. We also present an approach to synthesise such data-driven control policies in such a way that they encourage convergence and safety by adapting to unforeseen events. Alongside the above, we also explore an ancillary aspect of robot autonomy by improving the efficiency of simultaneous localisation and mapping through imitation learning. Lastly, we validate the advantages of behaviour trees' modularity in a real-world autonomous underwater vehicle's control system, and argue that this modularity contributes to efficiency, in terms of ease of use, and trust, in terms of facilitating human understanding.
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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. |
- Wang, Yuquan, 1985-
(författare)
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Reactive control and coordination of redundant robotic systems
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Redundant robotic systems, in terms of manipulators with one or twoarms, mobile manipulators, and multi-agent systems, have received an in-creasing amount of attention in recent years. In this thesis we describe severalways to improve robotic system performance by exploiting the redundancy.As the robot workspace becomes increasingly dynamic, it is common towork with imperfect geometric models of the robots or its workspace. Inorder to control the robot in a robust way in the presence of geometric uncer-tainties, we propose to assess the stability of our controller with respect to acertain task by deriving bounds on the geometric uncertainties. Preliminaryexperimental results support the fact that stability is ensured if the proposedbounds on the geometric uncertainties are fulfilled.As a non-contact measurement, computer vision could provide rich infor-mation for robot control. We introduce a two step method that transformsthe position-based visual servoing problem into a quadratic optimization prob-lem with linear constraints. This method is optimal in terms of minimizinggeodesic distance and allows us to integrate constraints, e.g. visibility con-straints, in a natural way.In the case of a single robot with redundant degrees of freedom, we canspecify a family of complex robotic tasks using constraint based programming(CBP). CBP allows us to represent robotic tasks with a set of equality andinequality constraints. Using these constraints we can formulate quadraticprogramming problems that exploit the redundancy of the robot and itera-tively resolve the trade-off between the different constraints. For example, wecould improve the velocity or force transmission ratios along a task-dependent direction using the priorities between different constraints in real time.Using the reactiveness of CBP, we formulated and implemented a dual-armpan cleaning task. If we mount a dual-arm robot on a mobile base, we proposeto use a virtual kinematic chain to specify the coordination between the mobilebase and two arms. Using the modularity of the CBP, we can integrate themobility and dual-arm manipulation by adding coordination constraints intoan optimization problem where dual-arm manipulation constraints are alreadyspecified. We also found that the reactiveness and modularity of the CBPapproach is important in the context of teleoperation. Inspired by the 3Ddesign community, we proposed a teleoperation interface control mode thatis identical to the ones being used to locally navigate the virtual viewpoint ofmost Computer Aided Design (CAD) softwares.In the case of multiple robots, we combine ideas from multi-agent coopera-tive coverage control, with problem formulations from the resource allocationfield, to create a distributed convergent approach to the resource positioningproblem.
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| 6. |
- Bhat, Sriharsha, 1991-, et al.
(författare)
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A Cyber-Physical System for Hydrobatic AUVs : System Integration and Field Demonstration
- 2020
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Ingår i: 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- Cyber-physical systems (CPSs) comprise a network of sensors and actuators that are integrated with a computing and communication core. Hydrobatic Autonomous Underwater Vehicles (AUVs) can be efficient and agile, offering new use cases in ocean production, environmental sensing and security. In this paper, a CPS concept for hydrobatic AUVs is validated in real-world field trials with the hydrobatic AUV SAM developed at the Swedish Maritime Robotics Center (SMaRC). We present system integration of hardware systems, software subsystems for mission planning using Neptus, mission execution using behavior trees, flight and trim control, navigation and dead reckoning. Together with the software systems, we show simulation environments in Simulink and Stonefish for virtual validation of the entire CPS. Extensive field validation of the different components of the CPS has been performed. Results of a field demonstration scenario involving the search and inspection of a submerged Mini Cooper using payload cameras on SAM in the Baltic Sea are presented. The full system including the mission planning interface, behavior tree, controllers, dead-reckoning and object detection algorithm is validated. The submerged target is successfully detected both in simulation and reality, and simulation tools show tight integration with target hardware.
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| 7. |
- Bhat, Sriharsha, et al.
(författare)
-
A Cyber-Physical System for Hydrobatic AUVs: System Integration and Field Demonstration
- 2020
-
Konferensbidrag (refereegranskat)abstract
- Cyber-physical systems (CPSs) comprise a network of sensors and actuators that are integrated with a computing and communication core. Hydrobatic Autonomous Underwater Vehicles (AUVs) can be efficient and agile, offering new use cases in ocean production, environmental sensing and security. In this paper, a CPS concept for hydrobatic AUVs is validated in real-world field trials with the hydrobatic AUV SAM developed at the Swedish Maritime Robotics Center (SMaRC). We present system integration of hardware systems, software subsystems for mission planning using Neptus, mission execution using behavior trees, flight and trim control, navigation and dead reckoning. Together with the software systems, we show simulation environments in Simulink and Stonefish for virtual validation of the entire CPS. Extensive field validation of the different components of the CPS has been performed. Results of a field demonstration scenario involving the search and inspection of a submerged Mini Cooper using payload cameras on SAM in the Baltic Sea are presented. The full system including the mission planning interface, behavior tree, controllers, dead-reckoning and object detection algorithm is validated. The submerged target is successfully detected both in simulation and reality, and simulation tools show tight integration with target hardware.
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| 8. |
- Caccamo, Sergio, 1987-
(författare)
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Enhancing geometric maps through environmental interactions
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The deployment of rescue robots in real operations is becoming increasingly commonthanks to recent advances in AI technologies and high performance hardware. Rescue robots can now operate for extended period of time, cover wider areas andprocess larger amounts of sensory information making them considerably more usefulduring real life threatening situations, including both natural or man-made disasters.In this thesis we present results of our research which focuses on investigating ways of enhancing visual perception for Unmanned Ground Vehicles (UGVs) through environmental interactions using different sensory systems, such as tactile sensors and wireless receivers.We argue that a geometric representation of the robot surroundings built upon vision data only, may not suffice in overcoming challenging scenarios, and show that robot interactions with the environment can provide a rich layer of new information that needs to be suitably represented and merged into the cognitive world model. Visual perception for mobile ground vehicles is one of the fundamental problems in rescue robotics. Phenomena such as rain, fog, darkness, dust, smoke and fire heavily influence the performance of visual sensors, and often result in highly noisy data, leading to unreliable or incomplete maps.We address this problem through a collection of studies and structure the thesis as follow:Firstly, we give an overview of the Search & Rescue (SAR) robotics field, and discuss scenarios, hardware and related scientific questions.Secondly, we focus on the problems of control and communication. Mobile robotsrequire stable communication with the base station to exchange valuable information. Communication loss often presents a significant mission risk and disconnected robotsare either abandoned, or autonomously try to back-trace their way to the base station. We show how non-visual environmental properties (e.g. the WiFi signal distribution) can be efficiently modeled using probabilistic active perception frameworks based on Gaussian Processes, and merged into geometric maps so to facilitate the SAR mission. We then show how to use tactile perception to enhance mapping. Implicit environmental properties such as the terrain deformability, are analyzed through strategic glancesand touches and then mapped into probabilistic models.Lastly, we address the problem of reconstructing objects in the environment. Wepresent a technique for simultaneous 3D reconstruction of static regions and rigidly moving objects in a scene that enables on-the-fly model generation. Although this thesis focuses mostly on rescue UGVs, the concepts presented canbe applied to other mobile platforms that operates under similar circumstances. To make sure that the suggested methods work, we have put efforts into design of user interfaces and the evaluation of those in user studies.
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| 9. |
- Özkahraman, Özer, 1992-
(författare)
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Multi-Agent Mission Planning and Execution for Small Autonomous Underwater Vehicles
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Our planet is mostly covered in water, most of it still unexplored.In order to understand our environment better, oceanographers have been mapping and monitoring these waters using ship-mounted sensors and wired vehicles with limited range compared to the vastness of the oceans.The limited range and dependence on manned support vehicles has kept missions expensive and infrequent.To solve this problem, the sensors need to become independent of support vehicles, they need to venture into completely unexplored, unmapped regions of the seas by themselves and safely return with the data.This is where autonomous underwater vehicles (AUVs) have started to make a difference.In this thesis we investigate how multiple small AUVs can be utilized to efficiently and accurately sense very large volumes of water.Water absorbs electromagnetic radiation, meaning satellite-based global positioning systems (we will use GPS to refer to any such system), wide-angle cameras and radio communications are infeasible.These constraints ultimately result in uncertain localization of the vehicles.Furthermore, the vehicles are under constant disturbances from the water currents, fish and bio-fouling, which result in the dynamics of the vehicles being uncertain or even changing during the mission.In the first part of this thesis, we focus on the large-scale sensing problem under localization uncertainties by examining the caging and coverage problems.In the coverage problem, each AUV is uncertain about its exact position while tasked with sensing a stationary area.We show that we can still guarantee complete coverage and formulate the efficiency characteristics of different approaches.Furthermore, we show that when the vehicles are equipped with sensors and low-bandwidth communication methods, we can increase the effective range of a team of AUVs considerably by utilizing loop-closures over shared pose-graphs. In the caging problem, the localization uncertainty is focused on the entity that is being caged, its location is unknown but bounded.We show that through a combination of algorithms, the caging problem can be solved and a solution can be guaranteed, while simultaneously producing a list of specifications for the mission.In the second part, we focus on the individuals of the team and what they need to do in order for the team of AUVs to succeed.First, we identify that when there is a team of cooperative vehicles working together, conflicting goals rise.Each vehicle needs to pick between satisfying its own constraints and the constraints that come from being in a team. We propose a solution to this problem through a combination of Control Barrier Function (CBF) and Behavior Trees (BT).Secondly, we examine the possibility that a vehicle might undergo physical changes, like a broken thruster, that result in the vehicle being unable to complete the entire mission.Even in such a scenario, if the broken vehicle can still move to contact a normal one, the rest of the team can compensate through re-planning and the overall mission can still be completed.To do so, the broken vehicle must compensate for the change until a rendezvous.We propose a data-driven pipeline that can detect and plan around such a physical change within some bounds.
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