Kamerajärjestelmän suunnan optimointi navigointitehtävässä

Navigation in an unknown environment consists of multiple separable subtasks, such as collecting information about the surroundings and navigating to the current goal. In the case of pure visual navigation, all these subtasks need to utilize the same vision system, and therefore a way to optimally control the direction of focus is needed. This thesis presents a case study, where the active sensing problem of directing the gaze of a mobile robot with three machine vision cameras is modeled as a partially observable Markov decision process (POMDP) using a mutual information (MI) based reward function. The key aspect of the solution is that the cameras are dynamically used either in monocular or stereo configuration. The algorithms are implemented on Robot Operating System (ROS) and the benefits of using the proposed active sensing implementation over fixed stereo cameras are demonstrated with simulations experiments. The proposed active sensing outperforms the fixed camera solution when prior information about the environment is highly uncertain, and performs just as good in other tested scenarios. --- Navigaatio ennalta tuntemattomassa ympäristössä koostuu useista erillisistä alitehtävistä kuten informaation keräämisestä ja tämänhetkiseen kohteeseen navigoinnista. Kun kyse on puhtaasti visuaalisesta navigoinnista, tarvitsee kaikkien alitehtävien hyödyntää samaa kamerajärjestelmää, joten kamerajärjestelmän suunnan optimointi on tarpeen. Tässä diplomityössä esitellään esimerkkitapaus, jossa kolmen mobiiliin robottiin kiinnitetyn kameran suunnan aktiivinen operointiongelma mallinnetaan osittain havaittavana Markov-päätösprosessina (POMDP), jossa käytetään keskinäisinformaatioon (MI) perustuvaa palkkiota. Olennainen osa ratkaisua on, että kameroita voidaan käyttää dynaamisesti sekä monokulaarisessa- että stereokamera-konfiguraatiossa. Kehitetyt algoritmit implementoidaan Robot Operating System (ROS) -järjestelmälle ja kameroiden aktiivisen operoinnin hyödyt verrattuna kiinteästi asennettuihin stereokameroihin osoitetaan simulaatioilla. Kehitetty aktiivinen operointi suoriutuu kiinteitä kameroita paremmin kun ennakkotieto ympäristöstä on hyvin epävarmaa, ja muissa kokeilluissa tapauksissa vähintään yhtä hyvin

Robotic Crop Interaction in Agriculture for Soft Fruit Harvesting

Autonomous tree crop harvesting has been a seemingly attainable, but elusive, robotics goal for the past several decades. Limiting grower reliance on uncertain seasonal labour is an economic driver of this, but the ability of robotic systems to treat each plant individually also has environmental benefits, such as reduced emissions and fertiliser use. Over the same time period, effective grasping and manipulation (G&M) solutions to warehouse product handling, and more general robotic interaction, have been demonstrated. Despite research progress in general robotic interaction and harvesting of some specific crop types, a commercially successful robotic harvester has yet to be demonstrated. Most crop varieties, including soft-skinned fruit, have not yet been addressed. Soft fruit, such as plums, present problems for many of the techniques employed for their more robust relatives and require special focus when developing autonomous harvesters. Adapting existing robotics tools and techniques to new fruit types, including soft skinned varieties, is not well explored. This thesis aims to bridge that gap by examining the challenges of autonomous crop interaction for the harvesting of soft fruit. Aspects which are known to be challenging include mixed obstacle planning with both hard and soft obstacles present, poor outdoor sensing conditions, and the lack of proven picking motion strategies. Positioning an actuator for harvesting requires solving these problems and others specific to soft skinned fruit. Doing so effectively means addressing these in the sensing, planning and actuation areas of a robotic system. Such areas are also highly interdependent for grasping and manipulation tasks, so solutions need to be developed at the system level. In this thesis, soft robotics actuators, with simplifying assumptions about hard obstacle planes, are used to solve mixed obstacle planning. Persistent target tracking and filtering is used to overcome challenging object detection conditions, while multiple stages of object detection are applied to refine these initial position estimates. Several picking motions are developed and tested for plums, with varying degrees of effectiveness. These various techniques are integrated into a prototype system which is validated in lab testing and extensive field trials on a commercial plum crop. Key contributions of this thesis include I. The examination of grasping & manipulation tools, algorithms, techniques and challenges for harvesting soft skinned fruit II. Design, development and field-trial evaluation of a harvester prototype to validate these concepts in practice, with specific design studies of the gripper type, object detector architecture and picking motion for this III. Investigation of specific G&M module improvements including: o Application of the autocovariance least squares (ALS) method to noise covariance matrix estimation for visual servoing tasks, where both simulated and real experiments demonstrated a 30% improvement in state estimation error using this technique. o Theory and experimentation showing that a single range measurement is sufficient for disambiguating scene scale in monocular depth estimation for some datasets. o Preliminary investigations of stochastic object completion and sampling for grasping, active perception for visual servoing based harvesting, and multi-stage fruit localisation from RGB-Depth data. Several field trials were carried out with the plum harvesting prototype. Testing on an unmodified commercial plum crop, in all weather conditions, showed promising results with a harvest success rate of 42%. While a significant gap between prototype performance and commercial viability remains, the use of soft robotics with carefully chosen sensing and planning approaches allows for robust grasping & manipulation under challenging conditions, with both hard and soft obstacles

A survey on active simultaneous localization and mapping: state of the art and new frontiers

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics

On the importance of sluggish state memory for learning long term dependency

The vanishing gradients problem inherent in Simple Recurrent Networks (SRN) trained with back-propagation, has led to a significant shift towards the use of Long Short-term Memory (LSTM) and Echo State Networks (ESN), which overcome this problem through either second order error-carousel schemes or different learning algorithms respectively. This paper re-opens the case for SRN-based approaches, by considering a variant, the Multi-recurrent Network (MRN). We show that memory units embedded within its architecture can ameliorate against the vanishing gradient problem, by providing variable sensitivity to recent and more historic information through layer- and self-recurrent links with varied weights, to form a so-called sluggish state-based memory. We demonstrate that an MRN, optimised with noise injection, is able to learn the long term dependency within a complex grammar induction task, significantly outperforming the SRN, NARX and ESN. Analysis of the internal representations of the networks, reveals that sluggish state-based representations of the MRN are best able to latch on to critical temporal dependencies spanning variable time delays, to maintain distinct and stable representations of all underlying grammar states. Surprisingly, the ESN was unable to fully learn the dependency problem, suggesting the major shift towards this class of models may be premature

A hybrid approach to simultaneous localization and mapping in indoors environment

This thesis will present SLAM in the current literature to benefit from then it will present the investigation results for a hybrid approach used where different algorithms using laser, sonar, and camera sensors were tested and compared. The contribution of this thesis is the development of a hybrid approach for SLAM that uses different sensors and where different factors are taken into consideration such as dynamic objects, and the development of a scalable grid map model with new sensors models for real time update of the map.The thesis will show the success found, difficulties faced and limitations of the algorithms developed which were simulated and experimentally tested in an indoors environment

A New Approach towards Non-holonomic Path Planning of Car-like Robots using Rapidly Random Tree Fixed Nodes(RRT*FN)

Autonomous car driving is gaining attention in industry and is also an ongoing research in scientific community. Assuming that the cars moving on the road are all autonomous, this thesis introduces an elegant approach to generate non-holonomic collision-free motion of a car connecting any two poses (configurations) set by the user. Particularly this thesis focusses research on "path-planning" of car-like robots in the presence of static obstacles. Path planning of car-like robots can be done using RRT and RRT*. Instead of generating the non-holonomic path between two sampled configurations in RRT, our approach finds a small incremental step towards the next random configuration. Since the incremental step can be in any direction we use RRT to guide the robot from start configuration to end configuration. This "easy-to-implement" mechanism provides flexibility for enabling standard plan- ners to solve for non-holonomic robots without much modifications. Thus, strength of such planners for car path planning can be easily realized. This thesis demon- strates this point by applying this mechanism for an effective variant of RRT called as RRT - Fixed Nodes (RRT*FN). Experiments are conducted by incorporating our mechanism into RRT*FN (termed as RRT*FN-NH) to show the effectiveness and quality of non-holonomic path gener- ated. The experiments are conducted for typical benchmark static environments and the results indicate that RRT*FN-NH is mostly finding the feasible non-holonomic solutions with a fixed number of nodes (satisfying memory requirements) at the cost of increased number of iterations in multiples of 10k. Thus, this thesis proves the applicability of mechanism for a highly constrained planner like RRT*-FN, where the path needs to be found with a fixed number of nodes. Although, comparing the algorithm (RRT*FN-NH) with other existing planners is not the focus of this thesis there are considerable advantages of the mechanism when applied to a planner. They are a) instantaneous non-holonomoic path generation using the strengths of that particular planner, b) ability to modify on-the-fly non-holomic paths, and c) simple to integrate with most of the existing planners. Moreover, applicability of this mechanism using RRT*-FN for non-holonomic path generation of a car is shown for a more realistic urban environments that have typical narrow curved roads. The experiments were done for actual road map obtained from google maps and the feasibility of non-holonomic path generation was shown for such environments. The typical number of iterations needed for finding such feasible solutions were also in multiple of 10k. Increasing speed profiles of the car was tested by limiting max speed and acceleration to see the effect on the number of iterations

Advancing Embedded and Extrinsic Solutions for Optimal Control and Efficiency of Energy Systems in Buildings

Buildings account for approximately 40% of all U.S. energy usage and carbon emissions. Reducing energy usage and improving efficiency in buildings has the potential for significant environmental and economic impacts. To do so, reoccurring identification of hardware and operational opportunities is needed to maintain building efficiency. Additionally, the development of controls that continually operate building systems and equipment at energy optimal conditions is required. This dissertation provides contributions to both of the aforementioned areas, which can be divided into two distinct portions. The first presents the framework for the development of an automated energy audit process, termed Autonomous Robotic Assessments of Energy (AuRAE). The automation of energy audits would decrease the cost of audits to customers, reduce the time auditors need to invest in an audit, and provide repeatable audit processes with enhanced data collection. In this framework of AuRAE, novel, audit-centric navigational strategies are presented that enable the complete exploration of a previously unknown space in a building while identifying and navigating to objects of interest in real-time as well as navigation around external building perimeters. Simulations of the navigational strategies show success in a variety of building layouts and size of objects of interest. Additionally, prototypes of robotic audit capabilities are demonstrated in the form of a lighting identification and analysis package on a ground vehicle and an environmental baseline measurement package on an aerial vehicle. The second portion presents the development and simulation of two advanced economic building energy controllers: one utilizes steady-state relationships for optimizing control setpoints while the other is an economic MPC method using dynamic models to optimize the same control setpoints. Both control methods balance the minimization of utility cost from energy usage with the cost of lost productivity due to occupant discomfort, differing from standard building optimal control that generally addresses occupant comfort through setpoint limits or comfort measure constraints. This is accomplished through the development of component-level economic objective functions for each subsystem in the modeled building. The results show that utility cost and the cost of occupant productivity from optimal comfort can be successfully balanced, and even improved over current control methods. The relative magnitude of the cost of lost productivity is shown to be significantly higher than the cost of utilities, suggesting that building operators, technicians, and researchers should make maintaining occupant comfort a top priority to achieve the greatest economic savings. Furthermore, the results demonstrate that by using steady-state predictions, the majority of the performance gains produced with a fully dynamic MPC solution can be recovered

Guidance and control of an autonomous underwater vehicle

Merged with duplicate record 10026.1/856 on 07.03.2017 by CS (TIS)A cooperative project between the Universities of Plymouth and Cranfield was aimed at designing and developing an autonomous underwater vehicle named Hammerhead. The work presented herein is to formulate an advance guidance and control system and to implement it in the Hammerhead. This involves the description of Hammerhead hardware from a control system perspective. In addition to the control system, an intelligent navigation scheme and a state of the art vision system is also developed. However, the development of these submodules is out of the scope of this thesis. To model an underwater vehicle, the traditional way is to acquire painstaking mathematical models based on laws of physics and then simplify and linearise the models to some operating point. One of the principal novelties of this research is the use of system identification techniques on actual vehicle data obtained from full scale in water experiments. Two new guidance mechanisms have also been formulated for cruising type vehicles. The first is a modification of the proportional navigation guidance for missiles whilst the other is a hybrid law which is a combination of several guidance strategies employed during different phases of the Right. In addition to the modelling process and guidance systems, a number of robust control methodologies have been conceived for Hammerhead. A discrete time linear quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated with the conventional and more advance guidance laws proposed. A model predictive controller (MPC) has also been devised which is constructed using artificial intelligence techniques such as genetic algorithms (GA) and fuzzy logic. A GA is employed as an online optimization routine whilst fuzzy logic has been exploited as an objective function in an MPC framework. The GA-MPC autopilot has been implemented in Hammerhead in real time and results demonstrate excellent robustness despite the presence of disturbances and ever present modelling uncertainty. To the author's knowledge, this is the first successful application of a GA in real time optimization for controller tuning in the marine sector and thus the thesis makes an extremely novel and useful contribution to control system design in general. The controllers are also integrated with the proposed guidance laws and is also considered to be an invaluable contribution to knowledge. Moreover, the autopilots are used in conjunction with a vision based altitude information sensor and simulation results demonstrate the efficacy of the controllers to cope with uncertain altitude demands.J&S MARINE LTD., QINETIQ, SUBSEA 7 AND SOUTH WEST WATER PL