837 research outputs found
A Sampling-Based Tree Planner for Robot Navigation Among Movable Obstacles
This thesis proposes a planner that solves Navigation Among Movable Obstacles problems giving robots the ability to reason about the environment and choose when manipulating obstacles. The planner combines the A*-Search and the exploration strategy of the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. It is locally optimal and independent from the size of the map and from the number, shape, and position of obstacles. It assumes full world knowledgeope
Computing fast search heuristics for physics-based mobile robot motion planning
Mobile robots are increasingly being employed to assist responders in search and rescue missions. Robots have to navigate in dangerous areas such as collapsed buildings and hazardous sites, which can be inaccessible to humans. Tele-operating the robots can be stressing for the human operators, which are also overloaded with mission tasks and coordination overhead, so it is important to provide the robot with some degree of autonomy, to lighten up the task for the human operator and also to ensure robot safety.
Moving robots around requires reasoning, including interpretation of the environment, spatial reasoning, planning of actions (motion), and execution. This is particularly challenging when the environment is unstructured, and the terrain is \textit{harsh}, i.e. not flat and cluttered with obstacles.
Approaches reducing the problem to a 2D path planning problem fall short, and many of those who reason about the problem in 3D don't do it in a complete and exhaustive manner.
The approach proposed in this thesis is to use rigid body simulation to obtain a more truthful model of the reality, i.e. of the interaction between the robot and the environment. Such a simulation obeys the laws of physics, takes into account the geometry of the environment, the geometry of the robot, and any dynamic constraints that may be in place.
The physics-based motion planning approach by itself is also highly intractable due to the computational load required to perform state propagation combined with the exponential blowup of planning; additionally, there are more technical limitations that disallow us to use things such as state sampling or state steering, which are known to be effective in solving the problem in simpler domains.
The proposed solution to this problem is to compute heuristics that can bias the search towards the goal, so as to quickly converge towards the solution.
With such a model, the search space is a rich space, which can only contain states which are physically reachable by the robot, and also tells us enough information about the safety of the robot itself.
The overall result is that by using this framework the robot engineer has a simpler job of encoding the \textit{domain knowledge} which now consists only of providing the robot geometric model plus any constraints
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Topologic Maps for Robotic Exploration of Underground Flooded Mines
The mapping of confined environments in mobile robotics is traditionally tackled in dense occupancy maps, requiring large amounts of storage. For some use cases, such as the exploration of flooded mines, the use of dense maps in processing slow down processes like path generation. I introduce a method of generating topological maps in constrained spaces such as mines. By taking a structure with fewer points, traversal and storage of explored space can be made more efficient, avoiding com plex graphs generated by methods like RRT and it’s variants. It’s simpler structure also allows for more intuitive human-machine interactions with it’s fewer points. I also introduce an autonomous frontier-based exploration approach to generate the topological map during exploration, taking advantage of it’s traversal to navigate through known space. With this work, simulation tests show it is possible to success fully extract a simpler graph structure describing the topology during autonomous exploration and that this structure is robust through explored regionsO mapeamento de ambientes confinados em robótica móvel, é tradicionalmente abordado em mapas densos de ocupação, necessitando de grandes quantidades de armazenamento. Para certos casos, tal como a exploração de minas submersas, o uso de mapas densos no processamento, atrasa processos como geração de caminhos. Utilizando uma estrutura com menos pontos, a travessia e o armazenamento de espaço explorado tornam-se mais eficientes, evitando grafos complexos gerados por métodos como RRT e variantes. A sua estrutura mais simples permite também interações homem-máquina com o seu número reduzido de pontos. Introduzo também uma abordagem autónoma de exploração baseada em fronteiras, para gerar o mapa topo lógico durante a exploração, tirando vantagem da travessia do mesmo para navegar por espaço conhecido. Com este trabalho, testes em simulação mostram ser possÃvel extrair uma estrutura sob forma de grafo, descrevendo a topologia ao longo de explorações autónomas e que esta estrutura é robusta para a travessia em regiões explorada
A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES
The work in this thesis is concerned with the development of a novel and practical collision
avoidance system for autonomous underwater vehicles (AUVs). Synergistically,
advanced stochastic motion planning methods, dynamics quantisation approaches,
multivariable tracking controller designs, sonar data processing and workspace representation,
are combined to enhance significantly the survivability of modern AUVs.
The recent proliferation of autonomous AUV deployments for various missions such
as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial
increase in vehicle autonomy. One matching requirement of such missions is
to allow all the AUV to navigate safely in a dynamic and unstructured environment.
Therefore, it is vital that a robust and effective collision avoidance system should be
forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously
increasing its autonomy.
This thesis not only provides a holistic framework but also an arsenal of computational
techniques in the design of a collision avoidance system for AUVs. The
design of an obstacle avoidance system is first addressed. The core paradigm is the
application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly
developed version for use as a motion planning tool. Later, this technique is merged
with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages
of the RRT. A novel multi-node version which can also address time varying
final state is suggested. Clearly, the reference trajectory generated by the aforementioned
embedded planner must be tracked. Hence, the feasibility of employing the
linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent
Ricatti equation (SDRE) controller as trajectory trackers are explored.
The obstacle detection module, which comprises of sonar processing and workspace
representation submodules, is developed and tested on actual sonar data acquired
in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing
techniques applied are fundamentally derived from the image processing perspective.
Likewise, a novel occupancy grid using nonlinear function is proposed for the
workspace representation of the AUV. Results are presented that demonstrate the
ability of an AUV to navigate a complex environment.
To the author's knowledge, it is the first time the above newly developed methodologies
have been applied to an A UV collision avoidance system, and, therefore, it is
considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT
Active Mapping and Robot Exploration: A Survey
Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government
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