979 research outputs found

    Sampling-Based Motion Planning: A Comparative Review

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    Sampling-based motion planning is one of the fundamental paradigms to generate robot motions, and a cornerstone of robotics research. This comparative review provides an up-to-date guideline and reference manual for the use of sampling-based motion planning algorithms. This includes a history of motion planning, an overview about the most successful planners, and a discussion on their properties. It is also shown how planners can handle special cases and how extensions of motion planning can be accommodated. To put sampling-based motion planning into a larger context, a discussion of alternative motion generation frameworks is presented which highlights their respective differences to sampling-based motion planning. Finally, a set of sampling-based motion planners are compared on 24 challenging planning problems. This evaluation gives insights into which planners perform well in which situations and where future research would be required. This comparative review thereby provides not only a useful reference manual for researchers in the field, but also a guideline for practitioners to make informed algorithmic decisions.Comment: 25 pages, 7 figures, Accepted for Volume 7 (2024) of the Annual Review of Control, Robotics, and Autonomous System

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

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    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

    Scaling Robot Motion Planning to Multi-core Processors and the Cloud

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    Imagine a world in which robots safely interoperate with humans, gracefully and efficiently accomplishing everyday tasks. The robot's motions for these tasks, constrained by the design of the robot and task at hand, must avoid collisions with obstacles. Unfortunately, planning a constrained obstacle-free motion for a robot is computationally complex---often resulting in slow computation of inefficient motions. The methods in this dissertation speed up this motion plan computation with new algorithms and data structures that leverage readily available parallel processing, whether that processing power is on the robot or in the cloud, enabling robots to operate safer, more gracefully, and with improved efficiency. The contributions of this dissertation that enable faster motion planning are novel parallel lock-free algorithms, fast and concurrent nearest neighbor searching data structures, cache-aware operation, and split robot-cloud computation. Parallel lock-free algorithms avoid contention over shared data structures, resulting in empirical speedup proportional to the number of CPU cores working on the problem. Fast nearest neighbor data structures speed up searching in SO(3) and SE(3) metric spaces, which are needed for rigid body motion planning. Concurrent nearest neighbor data structures improve searching performance on metric spaces common to robot motion planning problems, while providing asymptotic wait-free concurrent operation. Cache-aware operation avoids long memory access times, allowing the algorithm to exhibit superlinear speedup. Split robot-cloud computation enables robots with low-power CPUs to react to changing environments by having the robot compute reactive paths in real-time from a set of motion plan options generated in a computationally intensive cloud-based algorithm. We demonstrate the scalability and effectiveness of our contributions in solving motion planning problems both in simulation and on physical robots of varying design and complexity. Problems include finding a solution to a complex motion planning problem, pre-computing motion plans that converge towards the optimal, and reactive interaction with dynamic environments. Robots include 2D holonomic robots, 3D rigid-body robots, a self-driving 1/10 scale car, articulated robot arms with and without mobile bases, and a small humanoid robot.Doctor of Philosoph

    High-DOF Motion Planning in Dynamic Environments using Trajectory Optimization

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    Motion planning is an important problem in robotics, computer-aided design, and simulated environments. Recently, robots with a high number of controllable joints are increasingly used for different applications, including in dynamic environments with humans and other moving objects. In this thesis, we address three main challenges related to motion planning algorithms for high-DOF robots in dynamic environments: 1) how to compute a feasible and constrained motion trajectory in dynamic environments; 2) how to improve the performance of realtime computations for high-DOF robots; 3) how to model the uncertainty in the environment representation and the motion of the obstacles. We present a novel optimization-based algorithm for motion planning in dynamic environments. We model various constraints corresponding to smoothness, as well as kinematics and dynamics bounds, as a cost function, and perform stochastic trajectory optimization to compute feasible high-dimensional trajectories. In order to handle arbitrary dynamic obstacles, we use a replanning framework that interleaves planning with execution. We also parallelize our approach on multiple CPU or GPU cores to improve the performance and perform realtime computations. In order to deal with the uncertainty of dynamic environments, we present an efficient probabilistic collision detection algorithm that takes into account noisy sensor data. We predict the future obstacle motion as Gaussian distributions, and compute the bounded collision probability between a high-DOF robot and obstacles. We highlight the performance of our algorithms in simulated environments as well as with a 7-DOF Fetch arm.Doctor of Philosoph

    High-Dimensional Motion Planning and Learning Under Uncertain Conditions

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    Many existing path planning methods do not adequately account for uncertainty. Without uncertainty these existing techniques work well, but in real world environments they struggle due to inaccurate sensor models, arbitrarily moving obstacles, and uncertain action consequences. For example, picking up and storing childrens toys is a simple task for humans. Yet, for a robotic household robot the task can be daunting. The room must be modeled with sensors, which may or may not detect all the strewn toys. The robot must be able to detect and avoid the child who may be moving the very toys that the robot is tasked with cleaning. Finally, if the robot missteps and places a foot on a toy, it must be able to compensate for the unexpected consequences of its actions. This example demonstrates that even simple human tasks are fraught with uncertainties that must be accounted for in robotic path planning algorithms. This work presents the first steps towards migrating sampling-based path planning methods to real world environments by addressing three different types of uncertainty: (1) model uncertainty, (2) spatio-temporal obstacle uncertainty (moving obstacles) and (3) action consequence uncertainty. Uncertainty is encoded directly into path planning through a data structure in order to successfully and efficiently identify safe robot paths in sensed environments with noise. This encoding produces comparable clearance paths to other planning methods which are a known for high clearance, but at an order of magnitude less computational cost. It also shows that formal control theory methods combined with path planning provides a technique that has a 95% collision-free navigation rate with 300 moving obstacles. Finally, it demonstrates that reinforcement learning can be combined with planning data structures to autonomously learn motion controls of a seven degree of freedom robot despite a low computational cost despite the number of dimensions

    Real-Time Robot Motion Planning Algorithms and Applications Under Uncertainty

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    Robot motion planning is an important problem for real-world robot applications. Recently, the separation of workspaces between humans and robots has been gradually fading, and there is strong interest in developing solutions where collaborative robots (cobots) can interact or work safely with humans in a shared space or in close proximity. When working with humans in real-world environments, the robots need to plan safe motions under uncertainty stemming from many sources such as noise of visual sensors, ambiguity of verbal instruction, and variety of human motions. In this thesis, we propose novel optimization-based and learning-based robot motion planning algorithms to deal with the uncertainties in real-world environments. To handle the input noise of visual cameras and the uncertainty of shape and pose estimation of surrounding objects, we present efficient probabilistic collision detection algorithms for Gaussian and non-Gaussian error distributions. By efficiently computing upper bounds of collision probability between an object and a robot, we present novel trajectory planning algorithms that guarantee that the collision probability at any trajectory point is less than a user-specified threshold. To enable human-robot interaction using natural language instructions, we present a mapping function from grounded linguistic semantics to the coefficients of the motion planning optimization problem. The mapping function considers task descriptions and motion-related constraints. For collaborative robots working with a human in close proximity, we present human intention and motion prediction algorithms for efficient task ordering and safe motion planning. The robot observes the human poses in real-time and predicts the future human motion based on the history of human poses. We also present an occlusion-aware robot motion planning algorithm that accounts for occlusion in the visual sensor data and uses learning-based techniques for trajectory planning. We highlight the benefits of our collision detection and robot motion planning algorithms with a 7-DOF Fetch robot arm in simulated and real-world environments.Doctor of Philosoph
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