Fast swept-volume distance for robust collision detection

The need for collision detection arises in several robotics areas, including motion-planning, online collision avoidance, and simulation. At the heart of most current methods are algorithms for interference detection and/or distance computation. A few recent algorithms and implementations are very fast, but to use them for accurate collision detection, very small step sizes can be necessary, reducing their effective efficiency. We present a fast, implemented technique for doing exact distance computation and interference detection for translationally-swept bodies. For rotationally swept bodies, we adapt this technique to improve accuracy, for any given step size, in distance computation and interference detection. We present preliminary experiments that show that the combination of basic and swept-body calculations holds much promise for faster accurate collision detection

Time Advancement and Bounds Intersection Checking for Faster Broad-Phase Collision Detection of Paired Object Trajectories

For self-driving mechanisms, the motion planning requires a reasonably fast algorithm for collision detection along the trajectories. We present three algorithms for the detection of collision among objects with predefined trajectories. The first algorithm uses the intersection of the path’s bounding box. The second algorithm sequentially checks for intersection between each pair of corresponding axis-aligned bounding boxes (AABB) from the trajectories of the two paths. Lastly, the latter algorithm is modified using iterative time advancement to an estimated earliest possible collision time. Simulation experiments on a variety of pair trajectories demonstrate a significant speedup of the proposed algorithms over the existing baseline algorithm. They are, therefore, preferable alternatives for faster broad-phase collision detection in applications such as motion planning

The Umbra Simulation and Integration Framework Applied to Emergency Response Training

The Mine Emergency Response Interactive Training Simulation (MERITS) is intended to prepare personnel to manage an emergency in an underground coal mine. The creation of an effective training environment required realistic emergent behavior in response to simulation events and trainee interventions, exploratory modification of miner behavior rules, realistic physics, and incorporation of legacy code. It also required the ability to add rich media to the simulation without conflicting with normal desktop security settings. Our Umbra Simulation and Integration Framework facilitated agent-based modeling of miners and rescuers and made it possible to work with subject matter experts to quickly adjust behavior through script editing, rather than through lengthy programming and recompilation. Integration of Umbra code with the WebKit browser engine allowed the use of JavaScript-enabled local web pages for media support. This project greatly extended the capabilities of Umbra in support of training simulations and has implications for simulations that combine human behavior, physics, and rich media

Recursive Algorithm for Motion Primitive Estimation

©2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai, China.The need for knowing future manipulator motion arises in several robotics applications, including notification or avoidance of imminent collisions and real-time optimization of velocity commands. This paper presents a real-time, low overhead algorithm for identification of future manipulator motions, based on measurements of prior motions and the instantaneous sensed actuator velocity commanded by an operator. Experimental results with a human-controlled, two degree of-freedom manipulator demonstrate the ability to quickly learn and accurately estimate future manipulator motions

Contact Force Modeling Between Non Convex Objects Using a Nonlinear Damping Model

At Sandia National Laboratories, the authors are developing the ability to accurately predict motions for arbitrary numbers of bodies of arbitrary shapes experiencing multiple applied forces and intermittent contacts. In particular, they are concerned with the simulation of systems such as part feeders or mobile robots operating in realistic environments. Preliminary investigation of commercial dynamics software packages led us to the conclude that they could use a commercial code to provide everything they needed except for the contact model. They found that ADAMS best fit the needs for a simulation package. To simulate intermittent contacts, they need collision detection software that can efficiently compute the distances between non-convex objects and return the associated witness features. They also require a computationally efficient contact model for rapid simulation of impact, sustained contact under load, and transition to and from contact conditions. This paper provides a technical review of a custom hierarchical distance computation engine developed at Sandia, called the C-Space Toolkit (CSTk). In addition, the authors describe an efficient contact model using a non-linear damping term developed at Ohio State. Both the CSTk and the non-linear damper have been incorporated in a simplified two-body testbed code, which is used to investigate how to correctly model the contact using these two utilities. They have incorporated this model into ADAMS SOLVER using the callable function interface. An example that illustrate the capabilities of the 9.02 release of ADAMS with the extensions is provided

VLSH: Voronoi-based Locality Sensitive Hashing

Abstract-We present a fast, yet accurate k-nearest neighbor search algorithm for high-dimensional sampling-based motion planners. Our technique is built on top of Locality Sensitive Hashing (LSH), but is extended to support arbitrary distance metrics used for motion planning problems and adapt irregular distributions of samples generated in the configuration space. To enable such novel characteristics our method embeds samples generated in the configuration space into a simple l2 norm space by using pivot points. We then implicitly define Voronoi regions and use local LSHs with varying quantization factors for those Voronoi regions. We have applied our method and other prior techniques to high-dimensional motion planning problems. Our method is able to show performance improvement by a factor of up to three times even with higher accuracy over prior, approximate nearest neighbor search techniques

A configuration space toolkit for automated spatial reasoning: Technical results and LDRD project final report

A robot`s configuration space (c-space) is the space of its kinematic degrees of freedom, e.g., the joint-space of an arm. Sets in c-space can be defined that characterize a variety of spatial relationships, such as contact between the robot and its environment. C-space techniques have been fundamental to research progress in areas such as motion planning and physically-based reasoning. However, practical progress has been slowed by the difficulty of implementing the c-space abstraction inside each application. For this reason, we proposed a Configuration Space Toolkit of high-performance algorithms and data structures meeting these needs. Our intent was to develop this robotics software to provide enabling technology to emerging applications that apply the c-space abstraction, such as advanced motion planning, teleoperation supervision, mechanism functional analysis, and design tools. This final report presents the research results and technical achievements of this LDRD project. Key results and achievements included (1) a hybrid Common LISP/C prototype that implements the basic C-Space abstraction, (2) a new, generic, algorithm for constructing hierarchical geometric representations, and (3) a C++ implementation of an algorithm for fast distance computation, interference detection, and c-space point-classification. Since the project conclusion, motion planning researchers in Sandia`s Intelligent Systems and Robotics Center have been using the CSTk libcstk.so C++ library. The code continues to be used, supported, and improved by projects in the ISRC

Physically-based sampling for motion planning

Motion planning is a fundamental problem with applications in a wide variety of areas including robotics, computer graphics, animation, virtual prototyping, medical simulations, industrial simulations, and trac planning. Despite being an active area of research for nearly four decades, prior motion planning algorithms are unable to provide adequate solutions that satisfy the constraints that arise in these applications. We present a novel approach based on physics-based sampling for motion planning that can compute collision-free paths while also satisfying many physical constraints. Our planning algorithms use constrained simulation to generate samples which are biased in the direction of the nal goal positions of the agent or agents. The underlying simulation core implicitly incorporates kinematics and dynamics of the robot or agent as constraints or as part of the motion model itself. Thus, the resulting motion is smooth and physically-plausible for both single robot and multi-robot planning. We apply our approach to planning of deformable soft-body agents via the use of graphics hardware accelerated interference queries. We highlight the approach with a case study on pre-operative planning for liver chemoembolization. Next, we apply it to the case of highly articulated serial chains. Through dynamic dimensionality reduction and optimized collision response, we can successfully plan the motion of \\snake-like robots in a practical amount of time despite the high number of degrees of freedom in the problem. Finally, we show the use of the approach for a large number of bodies in dynamic environments. By applying our approach to both global and local interactions between agents, we can successfully plan for thousands of simple robots in real-world scenarios. We demonstrate their application to large crowd simulations