We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to structure from motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera

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MonoSLAM: Real-Time Single Camera SLAM

We introduce a new method of a biped walking pattern generation by using a preview control of the zero-moment point (ZMP). First, the dynamics of a biped robot is modeled as a running cart on a table which gives a convenient representation to treat ZMP. After reviewing conventional methods of ZMP based pattern generation, we formalize the problem as the design of a ZMP tracking servo controller. It is shown that we can realize such controller by adopting the preview control theory that uses the future reference. It is also shown that a preview controller can be used to compensate the ZMP error caused by the difference between a simple model and the precise multibody model. The effectiveness of the proposed method is demonstrated by a simulation of walking on spiral stairs.

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Biped walking pattern generation by using preview control of zero-moment point

From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP. The paper discusses the utility of a portable and flexible simulation framework that allows for direct incorporation of various control techniques. This renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity. It also increases productivity by offering built-in and ready-to-use functionalities, as well as a multitude of programming approaches. This allows for a multitude of applications including rapid algorithm development, system verification, rapid prototyping, and deployment for cases such as safety/remote monitoring, training and education, hardware control, and factory automation simulation.

V-REP: A versatile and scalable robot simulation framework

It is known that for a large magnitude push a human or a humanoid robot must take a step to avoid a fall. Despite some scattered results, a principled approach towards "when and where to take a step" has not yet emerged. Towards this goal, we present methods for computing capture points and the capture region, the region on the ground where a humanoid must step to in order to come to a complete stop. The intersection between the capture region and the base of support determines which strategy the robot should adopt to successfully stop in a given situation. Computing the capture region for a humanoid, in general, is very difficult. However, with simple models of walking, computation of the capture region is simplified. We extend the well-known linear inverted pendulum model to include a flywheel body and show how to compute exact solutions of the capture region for this model. Adding rotational inertia enables the humanoid to control its centroidal angular momentum, much like the way human beings do, significantly enlarging the capture region. We present simulations of a simple planar biped that can recover balance after a push by stepping to the capture region and using internal angular momentum. Ongoing work involves applying the solution from the simple model as an approximate solution to more complex simulations of bipedal walking, including a 3D biped with distributed mass.

/pdf/capture-point-a-step-toward-humanoid-push-recovery-12jssfk8t5.pdf

Capture Point: A Step toward Humanoid Push Recovery

For 3D walking control of a biped robot we analyze the dynamics of a 3D inverted pendulum in which motion is constrained to move along an arbitrarily defined plane. This analysis yields a simple linear dynamics, the 3D linear inverted pendulum mode (3D-LIPM). Geometric nature of trajectories under the 3D-LIPM and a method for walking pattern generation are discussed. A simulation result of a walking control using a 12-DOF biped robot model is also shown.

The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation

2P1-F16 Path Smoothing for Humanoid Motion Planning using B-spline

A method and a device for detecting a low rigidity capable of preventing a force detection means from being damaged even if a large impact force acts thereon by using an impact absorbing means and the force detection means formed integrally with each other, the device wherein at least one impact absorbing and detection mechanism (3) having the impact absorbing means (4) and the force detection means (5) formed integrally with each other is installed between a pair of opposed substrates (2a, 2b) displaced in such a direction that the mutual distance thereof is varied by the action of an impact force, and a force between both substrates (2a, 2b) is detected by the force detection means (5) while absorbing the impact force acting between both substrates (2a, 2b) by the resiliency of the impact absorbing means (4).

Method and device for detecting low rigidity

https://www.jstage.jst.go.jp/article/jrsj1983/15/6/15_6_911/_pdf

Fast Passing Over Steps by a ‘Variable Structure Type Four-wheeled Robot’ Using a Dynamic Trajectory Planning Method for a Manipulator with Passive Joint

2A1-D22 Turn Motion of a Humanoid Robot HRP-4C Exploiting MoCap Data

This paper proposes a system to enable a humanoid robot to perform various tasks semi-autonomously according to higher-layer procedures called task sequences. A task sequence is a sequentially structured procedure that describes how to perform a particular task based on sensing data, motion planners, and a robot controller. These task sequences are processed by the task sequencer to move the robot. The goal of our system is to support comprehensive tasks, such as disaster response, with a number of task sequences defined for various subtasks. To efficiently develop required task sequences, the task sequencer is designed such that task sequences can be executed interactively; further, the task sequencer is integrated with a dynamics simulator that can be used to test the sequences without actually moving the robot. We verified our system by using it to make our HRP-2Kai robot perform the tasks defined in the DARPA Robotics Challenge, a competition involving disaster response robots.

Shuuji Kajita

Papers

2P1-F16 Path Smoothing for Humanoid Motion Planning using B-spline

Method and device for detecting low rigidity

Fast Passing Over Steps by a ‘Variable Structure Type Four-wheeled Robot’ Using a Dynamic Trajectory Planning Method for a Manipulator with Passive Joint

2A1-D22 Turn Motion of a Humanoid Robot HRP-4C Exploiting MoCap Data

Developing semi-autonomous humanoid robots that perform various composite tasks via a task sequencer and dynamics simulator