Mobile robots and vehicles motion systems: a unifying framework

Robots perform many different activities in order to accomplish their tasks. The robot motion capability is one of the most important ones for an autonomous be- havior in a typical indoor-outdoor mission (without it other tasks can not be done), since it drastically determines the global success of a robotic mission. In this thesis, we focus on the main methods for mobile robot and vehicle motion systems and we build a common framework, where similar components can be interchanged or even used together in order to increase the whole system performance

Any-Com Multi-Robot Path Planning

Robust autonomous robotic systems must use complete or probabilistically complete path planning algorithms when less expensive methods fail (where completeness is the algorithmic property of being able to find a solution to a problem when one exists). However, these methods are sometimes so computationally complex that a solution cannot be found within a reasonable amount of time. Communication between robots tends to increase completeness and reduce computational complexity; however, communication quality is environmentally dependent and often beyond control of the user or system. Previous approaches to the multi-robot path planning problem have each been tailored to a single point within the completeness vs. computational vs. communication state space, and are often ill-equipped to solve problems outside their design envelope. In contrast, I believe that truly robust multi-robot navigation can only be achieved by algorithms that automatically tune their performance within this state space to maximize performance vs. each problem the system faces. My personal bias is to maximize algorithmic completeness while respecting the computational resource and communication quality that is currently available. In order to be useful, the resulting solutions must be calculated within a reasonable amount of time. I also believe that it makes sense to divide the computational effort of finding multi-robot path planning solutions among all robots that the solution will benefit. This can be accomplished by recasting a networked team of robots as an ad-hoc distributed computer---allowing the team\u27s computational resources to be pooled increases the complexity of problems that can be solved within a particular amount of time. However, distributed computation in an ad-hoc framework must respect the fact that communication between computational nodes (i.e., robots) is usually unreliable. I propose the thesis “Sharing Any-Time search progress over an ad-hoc distributed computer that is created from a dynamic team of robots enables probabilistically complete, centralized, multi-robot path-planning across a broad class of instances with varied complexity, communication quality, and computational resources.” The work presented in this dissertation in support of my thesis can be divided into three related areas of focus. (1) I propose a new distributed planning concept called Coupled Forests Of Random Engrafting Search Trees (C-FOREST), and demonstrate that it has parallelization efficiency greater than 1 for many problems. (2) I propose using a robotic team as an ad-hoc distributed computing cluster, and demonstrate that when C-FOREST is run on this type of architecture it is able to exploit perfect communication when it exists, but also has graceful performance declines as communication quality deteriorates. I coin the term “Any-Com” to describe algorithms with the latter property. (3) I propose a dynamic team version of Any-Com C-FOREST that allows multiple robotic teams to form and then re-form as robots move about the environment. Each team acts as an ad-hoc distributed computer to solve its composite robots\u27 communal path planning problem. Limiting teams to include only conflicting robots improves algorithmic performance because it significantly reduces the computational complexity of the problem that each team must solve. Replanning through only the subset of the configuration space in which conflicts occurs has similar computational benefits

Topology based representations for motion synthesis and planning

Robot motion can be described in several alternative representations, including joint configuration or end-effector spaces. These representations are often used for manipulation or navigation tasks but they are not suitable for tasks that involve close interaction with the environment. In these scenarios, collisions and relative poses of the robot and its surroundings create a complex planning space. To deal with this complexity, we exploit several representations that capture the state of the interaction, rather than the state of the robot. Borrowing notions of topology invariances and homotopy classes, we design task spaces based on winding numbers and writhe for synthesizing winding motion, and electro-static fields for planning reaching and grasping motion. Our experiments show that these representations capture the motion, preserving its qualitative properties, while generalising over finer geometrical detail. Based on the same motivation, we utilise a scale and rotation invariant representation for locally preserving distances, called interaction mesh. The interaction mesh allows for transferring motion between robots of different scales (motion re-targeting), between humans and robots (teleoperation) and between different environments (motion adaptation). To estimate the state of the environment we employ real-time sensing techniques utilizing dense stereo tracking, magnetic tracking sensors and inertia measurements units. We combine and exploit these representations for synthesis and generalization of motion in dynamic environments. The benefit of this method is on problems where direct planning in joint space is extremely hard whereas local optimal control exploiting topology and metric of these novel representations can efficiently compute optimal trajectories. We formulate this approach in the framework of optimal control as an approximate inference problem. This allows for consistent combination of multiple task spaces (e.g. end-effector, joint space and the abstract task spaces we investigate in this thesis). Motion generalization to novel situations and kinematics is similarly performed by projecting motion from abstract representations to joint configuration space. This technique, based on operational space control, allows us to adapt the motion in real time. This process of real-time re-mapping generates robust motion, thus reducing the amount of re-planning.We have implemented our approach as a part of an open source project called the Extensible Optimisation library (EXOTica). This software allows for defining motion synthesis problems by combining task representations and presenting this problem to various motion planners using a common interface. Using EXOTica, we perform comparisons between different representations and different planners to validate that these representations truly improve the motion planning

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Extensions of sampling-based approaches to path planning in complex cost spaces: applications to robotics and structural biology

Planifier le chemin d’un robot dans un environnement complexe est un problème crucial en robotique. Les méthodes de planification probabilistes peuvent résoudre des problèmes complexes aussi bien en robotique, qu’en animation graphique, ou en biologie structurale. En général, ces méthodes produisent un chemin évitant les collisions, sans considérer sa qualité. Récemment, de nouvelles approches ont été créées pour générer des chemins de bonne qualité : en robotique, cela peut être le chemin le plus court ou qui maximise la sécurité ; en biologie, il s’agit du mouvement minimisant la variation énergétique moléculaire. Dans cette thèse, nous proposons plusieurs extensions de ces méthodes, pour améliorer leurs performances et leur permettre de résoudre des problèmes toujours plus difficiles. Les applications que nous présentons viennent de la robotique (inspection industrielle et manipulation aérienne) et de la biologie structurale (mouvement moléculaire et conformations stables). ABSTRACT : Planning a path for a robot in a complex environment is a crucial issue in robotics. So-called probabilistic algorithms for path planning are very successful at solving difficult problems and are applied in various domains, such as aerospace, computer animation, and structural biology. However, these methods have traditionally focused on finding paths avoiding collisions, without considering the quality of these paths. In recent years, new approaches have been developed to generate high-quality paths: in robotics, this can mean finding paths maximizing safety or control; in biology, this means finding motions minimizing the energy variation of a molecule. In this thesis, we propose several extensions of these methods to improve their performance and allow them to solve ever more difficult problems. The applications we present stem from robotics (industrial inspection and aerial manipulation) and structural biology (simulation of molecular motions and exploration of energy landscapes)

Extensions of sampling-based approaches to path planning in complex cost spaces: applications to robotics and structural biology

Planning a path for a robot in a complex environment is a crucial issue in robotics. So-called probabilistic algorithms for path planning are very successful at solving difficult problems and are applied in various domains, such as aerospace, computer animation, and structural biology. However, these methods have traditionally focused on finding paths avoiding collisions, without considering the quality of these paths. In recent years, new approaches have been developed to generate high-quality paths: in robotics, this can mean finding paths maximizing safety or control; in biology, this means finding motions minimizing the energy variation of a molecule. In this thesis, we propose several extensions of these methods to improve their performance and allow them to solve ever more difficult problems. The applications we present stem from robotics (industrial inspection and aerial manipulation) and structural biology (simulation of molecular motions and exploration of energy landscapes)

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Workers of the World: International Journal on Strikes and Social Conflicts, Vol. 1 No. 3

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