1,261 research outputs found
A mosaic of eyes
Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties
Perception-aware Path Planning
In this paper, we give a double twist to the problem of planning under
uncertainty. State-of-the-art planners seek to minimize the localization
uncertainty by only considering the geometric structure of the scene. In this
paper, we argue that motion planning for vision-controlled robots should be
perception aware in that the robot should also favor texture-rich areas to
minimize the localization uncertainty during a goal-reaching task. Thus, we
describe how to optimally incorporate the photometric information (i.e.,
texture) of the scene, in addition to the the geometric one, to compute the
uncertainty of vision-based localization during path planning. To avoid the
caveats of feature-based localization systems (i.e., dependence on feature type
and user-defined thresholds), we use dense, direct methods. This allows us to
compute the localization uncertainty directly from the intensity values of
every pixel in the image. We also describe how to compute trajectories online,
considering also scenarios with no prior knowledge about the map. The proposed
framework is general and can easily be adapted to different robotic platforms
and scenarios. The effectiveness of our approach is demonstrated with extensive
experiments in both simulated and real-world environments using a
vision-controlled micro aerial vehicle.Comment: 16 pages, 20 figures, revised version. Conditionally accepted for
IEEE Transactions on Robotic
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
Motion Planning
Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms
Coverage Path Planning And Control For Autonomous Mobile Robots
Coverage control has many applications such as security patrolling, land mine detectors, and automatic vacuum cleaners. This Thesis presents an analytical approach for generation of control inputs for a non-holonomic mobile robot in coverage control. Neural Network approach is used for complete coverage of a given area in the presence of stationary and dynamic obstacles. A complete coverage algorithm is used to determine the sequence of points. Once the sequences of points are determined a smooth trajectory characterized by fifth order polynomial having second order continuity is generated. And the slope of the curve at each point is calculated from which the control inputs are generated analytically. Optimal trajectory is generated using a method given in research literature and a qualitative analysis of the smooth trajectory is done. Cooperative sweeping of multirobots is achieved by dividing the area to be covered into smaller areas depending on the number of robots. Once the area is divided into sub areas, each robot is assigned a sub area for cooperative sweeping
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
An MPC-based Optimal Motion Control Framework for Pendulum-driven Spherical Robots
Motion control is essential for all autonomous mobile robots, and even more
so for spherical robots. Due to the uniqueness of the spherical robot, its
motion control must not only ensure accurate tracking of the target commands,
but also minimize fluctuations in the robot's attitude and motors' current
while tracking. In this paper, model predictive control (MPC) is applied to the
control of spherical robots and an MPC-based motion control framework is
designed. There are two controllers in the framework, an optimal velocity
controller ESO-MPC which combines extend states observers (ESO) and MPC, and an
optimal orientation controller that uses multilayer perceptron (MLP) to
generate accurate trajectories and MPC with changing weights to achieve optimal
control. Finally, the performance of individual controllers and the whole
control framework are verified by physical experiments. The experimental
results show that the MPC-based motion control framework proposed in this work
is much better than PID in terms of rapidity and accuracy, and has great
advantages over sliding mode controller (SMC) for overshoot, attitude
stability, current stability and energy consumption.Comment: This paper has been submitted to Control Engineering Practic
An Autonomous Path Planning Method for Unmanned Aerial Vehicle based on A Tangent Intersection and Target Guidance Strategy
Unmanned aerial vehicle (UAV) path planning enables UAVs to avoid obstacles
and reach the target efficiently. To generate high-quality paths without
obstacle collision for UAVs, this paper proposes a novel autonomous path
planning algorithm based on a tangent intersection and target guidance strategy
(APPATT). Guided by a target, the elliptic tangent graph method is used to
generate two sub-paths, one of which is selected based on heuristic rules when
confronting an obstacle. The UAV flies along the selected sub-path and
repeatedly adjusts its flight path to avoid obstacles through this way until
the collision-free path extends to the target. Considering the UAV kinematic
constraints, the cubic B-spline curve is employed to smooth the waypoints for
obtaining a feasible path. Compared with A*, PRM, RRT and VFH, the experimental
results show that APPATT can generate the shortest collision-free path within
0.05 seconds for each instance under static environments. Moreover, compared
with VFH and RRTRW, APPATT can generate satisfactory collision-free paths under
uncertain environments in a nearly real-time manner. It is worth noting that
APPATT has the capability of escaping from simple traps within a reasonable
time
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