Download or read book Real time Sampling based Motion Planning with Dynamic Obstacles written by Kevin Rose and published by . This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predictive Sampling based Robot Motion Planning in Unmodeled Dynamic Environments written by Javier Matias Ruiz and published by . This book was released on 2019 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes a predictive sampling-based algorithm for real-time robot motion planning to reach dynamic goals. The planner utilizes all available information about future obstacle and goal positions over a time window to select a path that approximately minimizes the time to reach this goal. Then, we integrate the proposed method with an online learning algorithm that predicts future goal and obstacle positions. Because future states are predicted by propagation, an incorrect model would result into a greater prediction error for large horizons. Thus, using a pool of candidate models, we utilize a Multiple Model Adaptive Estimation~(MMAE) method with online parameter estimation to learn an appropriate model that keeps this error bounded. Several simulations show the efficacy of the proposed algorithms.

Download or read book Generalized Sampling based Feedback Motion Planners written by Sandip Kumar and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The motion planning problem can be formulated as a Markov decision process (MDP), if the uncertainties in the robot motion and environments can be modeled probabilistically. The complexity of solving these MDPs grow exponentially as the dimension of the problem increases and hence, it is nearly impossible to solve the problem even without constraints. Using hierarchical methods, these MDPs can be transformed into a semi-Markov decision process (SMDP) which only needs to be solved at certain landmark states. In the deterministic robotics motion planning community, sampling based algorithms like probabilistic roadmaps (PRM) and rapidly exploring random trees (RRTs) have been successful in solving very high dimensional deterministic problem. However they are not robust to system with uncertainties in the system dynamics and hence, one of the primary objective of this work is to generalize PRM/RRT to solve motion planning with uncertainty. We first present generalizations of randomized sampling based algorithms PRM and RRT, to incorporate the process uncertainty, and obstacle location uncertainty, termed as "generalized PRM" (GPRM) and "generalized RRT" (GRRT). The controllers used at the lower level of these planners are feedback controllers which ensure convergence of trajectories while mitigating the effects of process uncertainty. The results indicate that the algorithms solve the motion planning problem for a single agent in continuous state/control spaces in the presence of process uncertainty, and constraints such as obstacles and other state/input constraints. Secondly, a novel adaptive sampling technique, termed as "adaptive GPRM" (AGPRM), is proposed for these generalized planners to increase the efficiency and overall success probability of these planners. It was implemented on high-dimensional robot n-link manipulators, with up to 8 links, i.e. in a 16-dimensional state-space. The results demonstrate the ability of the proposed algorithm to handle the motion planning problem for highly non-linear systems in very high-dimensional state space. Finally, a solution methodology, termed the "multi-agent AGPRM" (MAGPRM), is proposed to solve the multi-agent motion planning problem under uncertainty. The technique uses a existing solution technique to the multiple traveling salesman problem (MTSP) in conjunction with GPRM. For real-time implementation, an "inter-agent collision detection and avoidance" module was designed which ensures that no two agents collide at any time-step. Algorithm was tested on teams of homogeneous and heterogeneous agents in cluttered obstacle space and the algorithm demonstrate the ability to handle such problems in continuous state/control spaces in presence of process uncertainty.

Download or read book Massive Parallelism and Sampling Strategies for Robust and Real time Robotic Motion Planning written by Brian Ichter and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is a fundamental problem in robotics, whereby one seeks to compute a low-cost trajectory from an initial state to a goal region that avoids any obstacles. Sampling-based motion planning algorithms have emerged as an effective paradigm for planning with complex, high-dimensional robotic systems. These algorithms maintain only an implicit representation of the state space, constructed by sampling the free state space and locally connecting samples (under the supervision of a collision checking module). This thesis presents approaches towards enabling real-time and robust sampling-based motion planning with improved sampling strategies and massive parallelism. In the first part of this thesis, we discuss algorithms to leverage massively parallel hardware (GPUs) to accelerate planning and to consider robustness during the planning process. We present an algorithm capable of planning at rates amenable to application within control loops, ∼10 ms. This algorithm uses approximate dynamic programming to explore the state space in a massively-parallel, near-optimal manner. We further present two algorithms capable of real-time, uncertainty-aware and perception-aware motion planning that exhaustively explore the state space via a multiobjective search. This search identifies a Pareto set of promising paths (in terms of cost and robustness) and certifies their robustness via Monte Carlo methods. We demonstrate the effectiveness of these algorithm in numerical simulations and a physical experiment on a quadrotor. In the second part of this thesis, we examine sampling-strategies for probing the state space; traditionally this has been uniform, independent, and identically distributed (i.i.d.) random points. We present a methodology for biasing the sample distribution towards regions of the state space in which the solution trajectory is likely to lie. This distribution is learned via a conditional variational autoencoder, allowing a general methodology, which can be used in combination with any sampling- based planner and can effectively exploit the underlying structure of a planning problem while maintaining the theoretical guarantees of sampling-based approaches. We also analyze the use of deterministic, low-dispersion samples instead of i.i.d. random points. We show that this allows deterministic asymptotic optimality (as opposed to probabilistic), a convergence rate bound in terms of the sample dispersion, reduced computational complexity, and improved practical performance. The technical approaches in this work are applicable to general robotic systems and lay the foundations of robustness and algorithmic speed required for robotic systems operating in the world.

Download or read book Robust Sampling based Motion Planning for Autonomous Vehicles in Uncertain Environments written by Brandon Douglas Luders and published by . This book was released on 2014 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: While navigating, autonomous vehicles often must overcome significant uncertainty in their understanding of the world around them. Real-world environments may be cluttered and highly dynamic, with uncertainty in both the current state and future evolution of environmental constraints. The vehicle may also face uncertainty in its own motion. To provide safe navigation under such conditions, motion planning algorithms must be able to rapidly generate smooth, certifiably robust trajectories in real-time. The primary contribution of this thesis is the development of a real-time motion planning framework capable of generating feasible paths for autonomous vehicles in complex environments, with robustness guarantees under both internal and external uncertainty. By leveraging the trajectory-wise constraint checking of sampling-based algorithms, and in particular rapidly-exploring random trees (RRT), the proposed algorithms can efficiently evaluate and enforce complex robustness conditions. For linear systems under bounded uncertainty, a sampling-based motion planner is presented which iteratively tightens constraints in order to guarantee safety for all feasible uncertainty realizations. The proposed bounded-uncertainty RRT* (BURRT*) algorithm scales favorably with environment complexity. Additionally, by building upon RRT*, BU-RRT* is shown to be asymptotically optimal, enabling it to efficiently generate and optimize robust, dynamically feasible trajectories. For large and/or unbounded uncertainties, probabilistically feasible planning is provided through the proposed chance-constrained RRT (CC-RRT) algorithm. Paths generated by CC-RRT are guaranteed probabilistically feasible for linear systems under Gaussian uncertainty, with extensions considered for nonlinear dynamics, output models, and/or non-Gaussian uncertainty. Probabilistic constraint satisfaction is represented in terms of chance constraints, extending existing approaches by considering both internal and external uncertainty, subject to time-step-wise and path-wise feasibility constraints. An explicit bound on the total risk of constraint violation is developed which can be efficiently evaluated online for each trajectory. The proposed CC-RRT* algorithm extends this approach to provide asymptotic optimality guarantees; an admissible risk-based objective uses the risk bounds to incentivize risk-averse trajectories. Applications of this framework are shown for several motion planning domains, including parafoil terminal guidance and urban navigation, where the system is subject to challenging environmental and uncertainty characterizations. Hardware results demonstrate a mobile robot utilizing this framework to safely avoid dynamic obstacles.

Download or read book Sampling Based Motion Planning Algorithms for Replanning and Spatial Load Balancing written by Beth Leigh Boardman and published by . This book was released on 2017 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents' positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis for each of the above mentioned algorithms is confirmed in simulations.

Download or read book Motion Planning with Obstacles and Dynamic Constraints written by Sunil Kumar Singh and published by . This book was released on 1988 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Sampling based Motion Planning Algorithms Analysis and Development written by Nathan Alexander Wedge and published by . This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic motion planning, which concerns the computation of paths and controls that drive an autonomous agent from one configuration to another, is quickly becoming a vitally important field of research as its applications diversify and become increasingly public. Many algorithms have been proposed to deal with this central problem; sampling-based approaches like the Rapidly-exploring Random Tree (RRT) and Probabilistic Roadmap Method (PRM) planners are among the most successful. Still, these algorithms are not fully understood and suffer from pathologically poorly-performing instances resulting from the contributions of random sampling and qualitative obstacle features like narrow passages. The large means and variances that result from these issues continue to motivate the development of new algorithms and adaptations to increase consistency and to allow more difficult problems to be solved. This research examines these performance issues with a focus on the Rapidly-exploring Random Tree (RRT) planner. Fundamental analysis establishes that the interaction of its Voronoi bias with particular obstacle features can compromise its efficacy and illustrates the types of distributions on its performance that result. It further provides guidance on the types of problems amenable to solutions by the algorithm and on the use of its alternative EXTEND and CONNECT heuristics and step size parameter. Observations from this analysis prompt an investigation of the use of restart strategies to manage issues of both scaling in computation and exploratory missteps. In turn, their impact provides a foundation for the introduction of a novel algorithm, the Path-length Annexed Random Tree (PART) planner, that directs its exploration on a local basis. This algorithm and its environment-adaptive successor, the Adaptive PART (APART) planner, demonstrate competitive performance on instructive examples and dramatic improvements on difficult benchmarks, while also supplementing their utility with the output of a connected roadmap.

Download or read book Sampling based Motion Planning written by Roland Jan Geraerts and published by . This book was released on 2006 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robotic Motion Planning in a Dynamic Environment with Moving Obstacles written by Tai-Jee Pan and published by . This book was released on 1990 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Sampling based Model Predictive Control Approach to Motion Planning for Autonomous Underwater Vehicles written by Charmane Venda Caldwell and published by . This book was released on 2011 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: In recent years there has been a demand from the commercial, research and military industries to complete tedious and hazardous underwater tasks. This has lead to the use of unmanned vehicles, in particular autonomous underwater vehicles (AUVs). To operate inthis environment the vehicle must display kinematically and dynamically feasible trajectories. Kinematic feasibility is important to allow for the limited turn radius of an AUV, while dynamic feasibility can take into consideration limited acceleration and braking capabilities due to actuator limitations and vehicle inertia. Model Predictive Control (MPC) is a method that has the ability to systematically handle multi-input multi-output (MIMO) control problems subject to constraints. It finds the control input by optimizing a cost function that incorporates a model of the system to predict future outputs subject to the constraints. This makes MPC a candidate method for AUV trajectory generation. However, traditional MPC has difficulties in computing control inputs in real time for processes with fast dynamics. This research applies a novel MPC approach, called Sampling-Based Model Predictive Control (SBMPC), to generate kinematically or dynamically feasible system trajectories for AUVs. The algorithm combines the benefits of sampling-based motion planning with MPC while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC, namely large computation times and local minimum problems. SBMPC is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization method (e.g., A?) in place of standard nonlinear programming. SBMPC can avoid local minimum, has only two parameters to tune, and has small computational times that allows it to be used online fast systems. A kinematic model, decoupled dynamic model and full dynamic model are incorporated in SBMPC to generate a kinematic and dynamic feasible 3D path. Simulation results demonstrate the efficacy of SBMPC in guiding an autonomous underwater vehicle from a start position to a goal position in regions populated with various types of obstacles.

Download or read book A Real time Framework for Kinodynamic Planning with Application to Quadrotor Obstacle Avoidance written by Ross E. Allen and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a full-stack, real-time planning framework for kinodynamic robots that is enabled by a novel application of machine learning for reachability analysis. As products of this work, three contributions are discussed in detail in this thesis. The first contribution is the novel application of machine learning for rapid approximation of reachable sets for dynamical systems. The second contribution is the synthesis of machine learning, sampling-based motion planning, and optimal control into a cohesive planning framework that is built on an offline-online computation paradigm. The final contribution is the application of this planning framework on a quadrotor system to produce, arguably, one of the first demonstrations of fully-online kinodynamic motion planning. During physical experiments, the framework is shown to execute planning cycles at a rate 3 Hz to 5 Hz, a significant improvement over existing techniques. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory. An event-based replanning structure is implemented to handle the case of dynamic, even adversarial, obstacles. A locally reactive control layer, inspired by potential fields methods, is added to the framework to help minimizes replanning events and produce graceful avoidance maneuvers in the presence of high speed obstacles.

Download or read book Sampling based Model Predictive Optimization with Application to Robot Kinodynamic Motion Planning written by Damion D. Dunlap and published by . This book was released on 2011 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Sampling Based Methods for Motion Planning with Constraints written by Zachary Kingston and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: ( a) sampling constraint-satisfying configurations and ( b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.

Download or read book Toward Real time Motion Planning written by Daniel Joseph Challou and published by . This book was released on 1993 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Performance of Local Planners with Respect to Sampling Strategies in Sampling based Motion Planning written by S. M. Rayhan Rahman and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Motion Planning Tussen Vette Obstakels written by Arnoldus Franciscus van der Stappen and published by . This book was released on 1994 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: