Perceiving guaranteed collision-free robot trajectories in unknown and unpredictable environments

The dissertation introduces novel approaches for solving a fundamental problem: detecting a collision-free robot trajectory based on sensing in real-world environments that are mostly unknown and unpredictable, i.e., obstacle geometries and their motions are unknown. Such a collision-free trajectory must provide a guarantee of safe robot motion by accounting for robot motion uncertainty and obstacle motion uncertainty. Further, as simultaneous planning and execution of robot motion is required to navigate in such environments, the collision-free trajectory must be detected in real-time. Two novel concepts: (a) dynamic envelopes and (b) atomic obstacles, are introduced to perceive if a robot at a configuration q, at a future time t, i.e., at a point ? = (q, t) in the robot's configuration-time space (CT space), will be collision-free or not, based on sensor data generated at each sensing moment t, in real-time. A dynamic envelope detects a collision-free region in the CT space in spite of unknown motions of obstacles. Atomic obstacles are used to represent perceived unknown obstacles in the environment at each sensing moment. The robot motion uncertainty is modeled by considering that a robot actually moves in a certain tunnel of a desired trajectory in its CT space. An approach based on dynamic envelopes is presented for detecting if a continuous tunnel of trajectories are guaranteed collision-free in an unpredictable environment, where obstacle motions are unknown. An efficient collision-checker is also developed that can perform fast real-time collision detection between a dynamic envelope and a large number of atomic obstacles in an unknown environment. The effectiveness of these methods is tested for different robots using both simulations and real-world experiments

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

Parallelizing RRT on large-scale distributed-memory architectures

This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance

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

Safe human-robot interaction based on dynamic sphere-swept line bounding volumes

This paper presents a geometric representation for human operators and robotic manipulators, which cooperate in the development of flexible tasks. The main goal of this representation is the implementation of real-time proximity queries, which are used by safety strategies for avoiding dangerous collisions between humans and robotic manipulators. This representation is composed of a set of bounding volumes based on swept-sphere line primitives, which encapsulate their links more precisely than previous sphere-based models. The radius of each bounding volume does not only represent the size of the encapsulated link, but it also includes an estimation of its motion. The radii of these dynamic bounding volumes are obtained from an algorithm which computes the linear velocity of each link. This algorithm has been implemented for the development of a safety strategy in a real human–robot interaction task.This work is funded by the Spanish Ministry of Education and the Spanish Ministry of Science and Innovation through the projects DPI2005-06222 and DPI2008-02647 and the grant AP2005-1458

Fast probabilistic collision checking for sampling-based motion planning using locality-sensitive hashing

We present a novel approach to perform fast probabilistic collision checking in high-dimensional configuration spaces to accelerate the performance of sampling-based motion planning. Our formulation stores the results of prior collision queries, and then uses such information to predict the collision probability for a new configuration sample. In particular, we perform an approximate k-NN ( k-nearest neighbor) search to find prior query samples that are closest to the new query configuration. The new query sample’s collision status is then estimated according to the collision checking results of these prior query samples, based on the fact that nearby configurations are likely to have the same collision status. We use locality-sensitive hashing techniques with sub-linear time complexity for approximate k-NN queries. We evaluate the benefit of our probabilistic collision checking approach by integrating it with a wide variety of sampling-based motion planners, including PRM (Probabilistic roadmaps), lazyPRM, RRT Rapidly exploring random trees, and RRT*. Our method can improve these planners in various manners, such as accelerating the local path validation, or computing an efficient order for the graph search on the roadmap. Experiments on a set of benchmarks demonstrate the performance of our method, and we observe up to 2x speedup in the performance of planners on rigid and articulated robots. </jats:p