Download or read book Risk Aware Planning and Probabilistic Prediction for Autonomous Systems Under Uncertain Environments written by Weiqiao Han and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis considers risk aware planning and probabilistic prediction for autonomous systems under uncertain environments. Motion planning under uncertainty looks for trajectories with bounded probability of collision with uncertain obstacles. Existing methods to address motion planning problems under uncertainty are either limited to Gaussian uncertainties and convex linear obstacles, or rely on sampling based methods that need uncertainty samples. In this thesis, we consider non-convex uncertain obstacles, stochastic nonlinear systems, and non-Gaussian uncertainty. We utilize concentration inequalities, higher order moments, and risk contours to handle non-Gaussian uncertainties. Without considering dynamics, we use RRT to plan trajectories together with SOS programming to verify the safety of the trajectory. Considering stochastic nonlinear dynamics, we solve nonlinear programming problems in terms of moments of random variables and controls using off-the-self solvers to generate trajectories with guaranteed bounded risk. Then we consider trajectory prediction for autonomous vehicles. We propose a hierarchical end-to-end deep learning framework for autonomous driving trajectory prediction: Keyframe MultiPath (KEMP). Our model is not only more general but also simpler than previous methods. Our model achieves state-of-the-art performance in autonomous driving trajectory prediction tasks.

Download or read book Belief State Planning for Autonomous Driving Planning with Interaction Uncertain Prediction and Uncertain Perception written by Hubmann, Constantin and published by KIT Scientific Publishing. This book was released on 2021-09-13 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty.

Download or read book State Estimation Planning and Behavior Selection Under Uncertainty for Autonomous Robotic Exploration in Dynamic Environments written by Georgios Lidoris and published by kassel university press GmbH. This book was released on 2011 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Execution of Temporal Plans with Sensing Actions and Bounded Risk written by Pedro Henrique de Rodrigues Quemel e Assis Santana and published by . This book was released on 2016 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: A special report on the cover of the June 2016 issue of the IEEE Spectrum magazine reads: "can we trust robots?" In a world that has been experiencing a seemingly irreversible process by which autonomous systems have been given increasingly more space in strategic areas such as transportation, manufacturing, energy supply, planetary exploration, and even medical surgeries, it is natural that we start asking ourselves if these systems could be held at the same or even higher levels of safety than we expect from humans. In an effort to make a contribution towards a world of autonomy that we can trust, this thesis argues that one necessary step in this direction is the endowment of autonomous agents with the ability to dynamically adapt to their environment while meeting strict safety guarantees. From a technical standpoint, we propose that autonomous agents in safety-critical applications be able to execute conditional plans (or policies) within risk bounds (also referred to as chance constraints). By being conditional, the plan allows the autonomous agent to adapt to its environment in real-time by conditioning the choice of activity to be executed on the agent's current level of knowledge, or belief, about the true state of world. This belief state is, in turn, a function of the history of potentially noisy sensor observations gathered by the agent from the environment. With respect to bounded risk, it refers to the fact that executing such conditional plans should guarantee to keep the agent "safe" - as defined by sets of state constraints - with high probability, while moving away from the conservatism of minimum risk approaches. In this thesis, we propose Chance-Constrained Partially Observable Markov Decision Processes (CC-POMDP's) as a formalism for conditional risk-bounded planning under uncertainty. Moreover, we present Risk-bounded AO* (RAO*), a heuristic forward search-based algorithm that searches for solutions to a CC-POMDP by leveraging admissible utility and risk heuristics to simultaneously guide the search and perform early pruning of overly-risky policy branches. In an effort to facilitate the specification of risk-bounded behavior by human modelers, we also present the Chance-constrained Reactive Model-based Programming Language (cRMPL), a novel variant of RMPL that incorporates chance constraints as part of its syntax. Finally, in support of the temporal planning applications with duration uncertainty that this thesis is concerned about, we present the Polynomial-time Algorithm for Risk-aware Scheduling (PARIS) and its extension to conditional scheduling of Probabilistic Temporal Plan Networks (PTPN's). The different tools and algorithms developed in the context of this thesis are combined to form the Conditional Planning for Autonomy with Risk (CLARK) system, a risk-aware conditional planning system that can generate chance-constrained, dynamic temporal plans for autonomous agents that must operate under uncertainty. With respect to our empirical validation, each component of CLARK is benchmarked against the relevant state of the art throughout the chapters, followed by several demonstrations of the whole CLARK system working in tandem with other building blocks of an architecture for autonomy.

Download or read book Robust Resilient and Risk Aware Optimization and Controls for Cyber Physical Systems written by Venkatraman Renganathan and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Cyber-Physical Systems (CPS) are physical processes that are tightly integrated with computation and communication systems for monitoring and control. Advances in CPS design has equipped them with adaptability, resiliency, safety, and security features that exceed the simple embedded systems of the past. On the other hand, the design of CPS involving complex interconnections between modules often leaves open several points for attackers to strike. This PhD dissertation is aimed upon developing theoretical techniques and building simulation tools based on distributional robustness for uncertainty handling tailored for guaranteeing resiliency in attack-prone CPS. As these systems become large, devising both model-based and moment-based methods for detecting anomalies are critical for robust and efficient operation. Similarly, safely deploying robots in dynamic and unknown environments require a systematic accounting of various risks both within and across layers in an autonomy stack from perception to motion planning and control. However, the perception and planning components in a robot autonomy stack are loosely coupled, in the sense that nominal estimates from the perception system may be used for planning, while inherent perception uncertainties are usually ignored, inspired from the classical separation of estimation and control in linear systems theory. As motion planning algorithms must be coupled with the outputs of inherently uncertain perception systems, there is a crucial need for tightly coupled perception and planning frameworks that explicitly incorporate perception uncertainties. In the first contribution of this dissertation, we show that robotic networks having graph robustness properties guaranteeing resiliency against malicious agents can be compromised through spoofing attack. We quantify the misclassification probability through distributionally robust pairwise comparison of the physical fingerprints of the agents. We propose a variant of robust consensus protocol to guarantee spoof resiliency against malicious agents who might spoof arbitrary amounts of spoofed identities. In the second contribution of this dissertation, we design anomaly detector for cyber-physical systems. Threshold of an anomaly detector limits the potential impact of a stealthy attacker attacking a CPS. We show that the traditional chi-squared anomaly detector raises false alarms more than a desired value in face of non-Gaussian uncertainties. To address the above problem, we propose a distributionally robust approach for tuning anomaly detector threshold and further analyse the problem when the system model has multiplicative noise uncertainties. In the final contribution of this dissertation, we establish a systematic framework for integrating the perception and control components, tailored for the robotic systems that are designed to operate in dynamic, cluttered and unknown environments. We propose a distributionally robust incremental sampling-based motion planning framework that explicitly and coherently incorporates perception and prediction uncertainties. We formulate distributionally robust risk constraints through linear temporal logic specifications to help the robot make coherent risk assessment without increasing the computation complexity while operating in unknown environments.

Download or read book Probabilistic Risk Assessment and the Path Planning of Safe Task Aware Autonomous Resilient Systems STAARS written by Uluhan Cem Kaya and published by . This book was released on 2019 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advancements on the unmanned systems manifest the potential of these technologies to impact our daily life. In particular, the unmanned aircraft systems (UAS) become ordinary for people in almost any area from aerial photography to emergency responses, from agricultural services to even autonomous deliveries. In-creased autonomy and advancements in low-cost high-computing technologies made these compact autonomous solutions accessible to any party with ease. Easiness and affordability to access these systems accelerated the innovations and the novel ideas for the solution of diverse real-life problems. Despite its benefits, however, this widespread availability also resulted in the safety and regulatory concerns in general. In an autonomous flight task over a public space, besides the mission objectives and the benefits, concerns regarding the public safety, privacy, and the regulations have to be addressed systematically during the planning and considered in the decision-making process. Therefore, there is a need for a comprehensive framework that can properly quantify and assess the risks incurred by the UAS operations to these concerns. This thesis presents the development of a probabilistic risk assessment frame-work and a path planning implementation of a concept of Safe Task-Aware Autonomous Resilient Systems (STAARS) to address the safety concerns. STAARS is conceptualized to consider the safety by quantifying and assessing the risks, task-awareness by adapting different tasks and environments, and resiliency by withstanding and making decisions in adversarial conditions. As a result, a multi-objective decision-making capability is introduced in this concept. The thesis aims to establish a framework that could be used for the path planning of UAS operations to quantify, assess and compare the risks incurred by these operations as well as the prots of the mission objectives such that a multi-objective optimization can be achieved with a task-level decision-making capability. The pro-posed framework consists of the risk assessment part where a probabilistic risk expo-sure concept and the UAS failure mode analysis are utilized and a generic utility-based approach for the multi-objective optimization part. In the next step, a commonly used path planning algorithm, which is rapidly-exploring random trees (RRT), is introduced. Finally, the implementation of the proposed framework for a couple ofsimple UAS scenarios are demonstrated using the path planner.

Download or read book Multi objective Path planning for Autonomous Agents Using Dynamic Game Theory written by Jhanani Selvakumar and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous systems which are designed to assist humans in complex environments, are often required to reliably operate under uncertainty. When probabilistic models for uncertainty are not available, the game-theoretic framework for adversarial/cooperative interactions allows us to solve problems for autonomous systems, such as control of uncertain dynamical systems, modeling biological systems, and deployment of sensor networks. This work focuses on decision-making and control problems for autonomous agents in uncertain environments. Characteristic sources of such uncertainty are wind or oceanic flows, radiation fields, and moving obstacles. In our approach, we model the agent-environment interactions induced by these sources of uncertainty as the actions of an adversary, which tries to prevent the agent from achieving its objective (e.g., reaching a target location). This modeling naturally leads to the formulation of a dynamic game between the autonomous agent and its environment. Control problems of autonomous agents that are subject to uncertain dynamic influences such as strong winds, fit into the structure of two-player zero-sum differential games. Many modern decision-making problems, however, cannot be put under the umbrella of zero-sum games because they involve complex interplay between multiple agents, which is not purely antagonistic. In this context, we address a special class of decision-making and path-planning problems, for autonomous agents that aim to reach a specified target set while avoiding multiple adversarial elements (such as mobile agents or obstacles). This class of problems, referred to as reach-avoid problems, corresponds to multi-player non-zero-sum dynamic games. Multi-player dynamic games typically require solving coupled partial differential equations, which is computationally and temporally expensive, if at all tractable. This intractability is particularly true, for problems of high dimensionality, and if there are agents in the game which have multiple objectives. For this reason, approximate solutions to dynamic multi-agent games are desirable in practice. Considering the binary objective of our agent of interest, we propose three approaches to the path-planning problem. Each approach is based on the characterization of risk to the agent, and uses a distinct method to determine a feasible solution to the multi-agent game. First, we propose an approximate divide-and-conquer approach that allows us to compute the global path for the agent of interest by concatenating local paths computed on a dynamic graph-abstraction of the environment. Through extensive simulations, we have demonstrated the effectiveness of the proposed approach. However, the proposed method does not guarantee global optimality or completeness of the solution, and also incurs considerable computational cost at each step. To improve computational tractability of the path-planning problem, next, we propose a feedback strategy based on greedy minimization of risk, where the risk metric is characterized with regard to the dual objective of the agent of interest. The same risk metric also aids us in partitioning the state-space of the game, which is useful to infer the outcome of the game from its initial conditions. The feedback strategy is computationally simple. Further, through numerical simulations, this approach has been found to be effective in a large number of cases, in guiding the autonomous vehicle to its target set. In order to further improve the target-reaching capability of the autonomous agent, we propose a third approach, a reduction of the dynamic multi-player game to a sequence of single-act games, one played at each time step. The proposed approach is also easy to implement and also does not incur significant loss of optimality. At each step, the optimal set of player strategies can be calculated efficiently and reliably via convex programming tools. More importantly, the proposed sequential formulation of the dynamic game allows us to account for the effect of the current actions of the agents on the final outcome of the original dynamic game. However, the payoffs of future games are altered by the past games and consequently, the equilibria for the single-act games (stage-wise equilibria) might not be optimal when the dynamic game is viewed as a whole. The choice of stage-wise equilibria can be improved by recording past actions and their effect on future payoffs. Drawing upon the history of actions and outcome patterns if any, we can learn to make better choices in the present. For multi-agent games with multiple non-aligned objectives for each agent, learning processes can aid in high-level switching between the optimal strategies corresponding to individual objectives. We propose the use of model-free reinforcement learning methods to obtain a feedback policy for the agent of interest. The challenges here, are to characterize an appropriate reward function, particularly under consideration of multiple objectives for the agent, and also to optimize parameters of the learning process. The goal of this thesis is to contribute a solid framework, which is based on game theory, and combines analytical and computational techniques, to address the problem of path-planning for an autonomous agent with multiple objectives in uncertain environments

Download or read book Intelligent Autonomous Systems 16 written by Marcelo H. Ang Jr and published by Springer Nature. This book was released on 2022-04-07 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest advances and research achievements in the fields of autonomous robots and intelligent systems, presented at the IAS-16 conference, conducted virtually in Singapore, from 22 to 25 June 2021. IAS is a common platform for an exchange and sharing of ideas among the international scientific research and technical community on some of the main trends of robotics and autonomous systems: navigation, machine learning, computer vision, control, and robot design—as well as a wide range of applications. IAS-16 reflects the rise of machine learning and deep learning developments in the robotics field, as employed in a variety of applications and systems. All contributions were selected using a rigorous peer-reviewed process to ensure their scientific quality. Despite the challenge of organising a conference during a pandemic, the IAS biennial conference remains an essential venue for the robotics and autonomous systems community ever since its inception in 1986. Chapters 46 of this book is available open access under a CC BY 4.0 license at link.springer.com

Download or read book Online Risk aware Conditional Planning with Qualitative Autonomous Driving Applications written by Matthew Quinn Deyo and published by . This book was released on 2018 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driving is often stressful and dangerous due to uncertainty in the actions of nearby vehicles. Having the ability to model driving maneuvers qualitatively and guarantee safety bounds in uncertain traffic scenarios are two steps towards building trust in vehicle autonomy. In this thesis, we present an approach to the problem of Qualitative Autonomous Driving (QAD) using risk-bounded conditional planning. First, we present Incremental Risk-aware AO* (iRAO*), an online conditional planning algorithm that builds off of RAO* for use in larger dynamic systems like driving. An illustrative example is included to better explain the behavior and performance of the algorithm. Second, we present a Chance-Constrained Hybrid Multi-Agent MDP as a framework for modeling our autonomous vehicle in traffic scenarios using qualitative driving maneuvers. Third, we extend our driving model by adding variable duration to maneuvers and develop two approaches to the resulting complexity. We present planning results from various driving scenarios, as well as from scaled instances of the illustrative example, that show the potential for further applications. Finally, we propose a QAD system, using the different tools developed in the context of this thesis, and show how it would fit within an autonomous driving architecture.

Download or read book Planning Under Uncertainty for Unmanned Aerial Vehicles written by Ryan Skeele and published by . This book was released on 2016 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unmanned aerial vehicle (UAV) technology has grown out of traditional research and military applications and has captivated the commercial and consumer markets, showing the ability to perform a spectrum of autonomous functions. This technology has the capability of saving lives in search and rescue, fighting wildfires in environmental monitoring, and delivering time dependent medicine in package delivery. These examples demonstrate the potential impact this technology will have on our society. However, it is evident how sensitive UAVs are to the uncertainty of the physical world. In order to properly achieve the full potential of UAVs in these markets, robust and efficient planning algorithms are needed. This thesis addresses the challenge of planning under uncertainty for UAVs. We develop a suite of algorithms that are robust to changes in the environment and build on the key areas of research needed for utilizing UAVs in a commercial setting. Throughout this research three main components emerged: monitoring targets in dynamic environments, exploration with unreliable communication, and risk-aware path planning. We use a realistic fire simulation to test persistent monitoring in an uncertain environment. The fire is generated using the standard program for modeling wildfire, FARSITE. This model was used to validate a weighted-greedy approach to monitoring clustered points of interest (POIs) over traditional methods of tracking a fire front. We implemented the algorithm on a commercial UAV to demonstrate the deployment capability. Dynamic monitoring has limited potential if if coordinated planning is fallible to uncertainty in the world. Uncertain communication can cause critical failures in coordinated planning algorithms. We develop a method for coordinated exploration of a multi-UAV team with unreliable communication and limited battery life. Our results show that the proposed algorithm, which leverages meeting, sacrificing, and relaying behavior, increases the percentage of the environment explored over a frontier-based exploration strategy by up to 18%. We test on teams of up to 8 simulated UAVs and 2 real UAVs able to cope with communication loss and still report improved gains. We demonstrate this work with a pair of custom UAVs in an indoor office environment. We introduce a novel approach to incorporating and addressing uncertainty in planning problems. The proposed Risk-Aware Graph Search (RAGS) algorithm combines traditional deterministic search techniques with risk-aware planning. RAGS is able to trade off the number of future path options, as well as the mean and variance of the associated path cost distributions to make online edge traversal decisions that minimize the risk of executing a high-cost path. The algorithm is compared against existing graphsearch techniques on a set of graphs with randomly assigned edge costs, as well as over a set of graphs with transition costs generated from satellite imagery data. In all cases, RAGS is shown to reduce the probability of executing high-cost paths over A*, D* and a greedy planning approach. High level planning algorithms can be brittle in dynamic conditions where the environment is not modeled perfectly. In developing planners for uncertainty we ensure UAVs will be able to operate in conditions outside the scope of prior techniques. We address the need for robustness in robotic monitoring, coordination, and path planning tasks. Each of the three methods introduced were tested in simulated and real environments, and the results show improvement over traditional algorithms.

Download or read book Moment based Risk bounded Trajectory Planning for Autonomous Vehicles written by Allen Mengyu Wang and published by . This book was released on 2020 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty in the behavior of agents on the road is arguably one of the greatest challenges preventing the large scale deployment of fully autonomous vehicles on public roads. This uncertainty is complex and challenging to characterize: empirical data shows multi-modal and non-Gaussian distributions of future positions of human driven vehicles. To drive safely, autonomous driving systems should generate prediction distributions of agent future positions that are representative of the uncertainty and use these distributions to plan trajectories with risk bounds (i.e. certificates on risk). Risk-bounded trajectory planning is a challenging problem, especially given the stringent run-time constraints imposed by autonomous driving. Thus, current approaches that are fast enough for autonomous driving are largely restricted to assuming Gaussian sources of uncertainty with linear constraints and model the ego vehicle as a point mass. To address these limitations, this thesis aims to develop a risk-bounded trajectory planner that can: 1) use multi-modal non-Gaussian predictions of agent positions, 2) account for ego vehicle and agent geometries, and 3) run in real time. To achieve generality, we dene a prediction representation, AMM-PFT, that represents uncertainty in terms of statistical moments of the prediction distribution. This approach provides generality as statistical moments are universal properties of distributions, and we provide methods for computing them from agent predictions. We then develop methods for bounding risk, given an AMM-PFT, by using statistical moments in deterministic inequalities known as concentration inequalities. These concentration inequalities are then encoded in a fixed risk allocation optimization problem, which we show can plan trajectories with 50 time step horizons in 12.9ms on average. In some scenarios, these concentration inequalities can be excessively conservative, so we develop non-differentiable methods for risk assessment that are tighter, but cannot be directly encoded in a gradient based optimization routine. Instead, these risk assessment methods are used to inform the outer loop that sets the risk allocation for optimizations; we call this algorithm SRAR and show that it can significantly reduce the average cost of trajectories while remaining safe. We provide controlled experiments demonstrating the advantages and stability of our approach, and we also provide demonstrations of our trajectory planning system in a simulation environment where it can safely drive through a neighborhood with multiple uncertain agents to get to its goal destination. We also consider the problem of using prediction distributions of agent actions, such as accelerating and turning. To use such predictions, we need to compute AMM-PFTs from these action distributions by propagating the uncertainty in actions into uncertainty in positions using nonlinear dynamics models such as the Dubin's Car. While the particle filter and variants of the Kalman filter can perform this propagation approximately, we develop an algorithm, TreeRing, that can search for closed form systems of equations to perform this propagation exactly for discrete time polynomial systems. We show that the Dubin's car can be transformed into a polynomial system, thus allowing us to apply TreeRing to develop a method for exactly computing AMM-PFTs given distributions of agent acceleration and turning. In numerical experiments, we show that it is more accurate than linearized propagation with the Kalman filter and, with a run-time of less than a microsecond per time step, it is much faster than Monte Carlo methods. While we only explore this particular application of TreeRing, it has the potential to improve performance in other filtering applications.

Download or read book Probabilistic Robotics written by Sebastian Thrun and published by MIT Press. This book was released on 2005-08-19 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Download or read book Safe Trajectories and Sequential Bayesian Decision Making Architecture for Reliable Autonomous Vehicle Navigation written by Dimia Iberraken and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in Autonomous Vehicles (AV) driving raised up all the importance to ensure the complete reliability of AV maneuvers even in highly dynamic and uncertain environments/situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architecture with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks (theoretically, lower than any human-like driving behavior), with a systemic way. Consequently, the aim is also to reduce the need for too extensive testing (which could take several months and years for each produced RAMS without at the end having absolute prove). Hence the goal in this Ph.D. thesis is to have a provable methodology for AV RAMS. This dissertation addresses the full pipeline from risk assessment, path planning to decision-making and control of autonomous vehicles. In the first place, an overall Probabilistic Multi-Controller Architecture (P-MCA) is designed for safe autonomous driving under uncertainties. The P-MCA is composed of several interconnected modules that are responsible for: assessing the collision risk with all observed vehicles while considering their trajectories' predictions; planning the different driving maneuvers; making the decision on the most suitable actions to achieve; control the vehicle movement; aborting safely the engaged maneuver if necessary (due for instance to a sudden change in the environment); and as last resort planning evasive actions if there is no other choice. The proposed risk assessment is based on a dual-safety stage strategy. The first stage analyzes the actual driving situation and predicts potential collisions. This is performed while taking into consideration several dynamic constraints and traffic conditions that are known at the time of planning. The second stage is applied in real-time, during the maneuver achievement, where a safety verification mechanism is activated to quantify the risks and the criticality of the driving situation beyond the remaining time to achieve the maneuver. The decision-making strategy is based on a Sequential Decision Networks for Maneuver Selection and Verification (SDN-MSV) and corresponds to an important module of the P-MCA. This module is designed to manage several road maneuvers under uncertainties. It utilizes the defined safety stages assessment to propose discrete actions that allow to: derive appropriate maneuvers in a given traffic situation and provide a safety retrospection that updates in real-time the ego-vehicle movements according to the environment dynamic, in order to face any sudden hazardous and risky situation. In the latter case, it is proposed to compute the corresponding low-level control based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) that allows the ego-vehicle to pursue the advised collision-free evasive trajectory to avert an accident and to guarantee safety at any time.The reliability and the flexibility of the overall proposed P-MCA and its elementary components have been intensively validated, first in simulated traffic conditions, with various driving scenarios, and secondly, in real-time with the autonomous vehicles available at Institut Pascal.

Download or read book Algorithmic Foundations of Robotics X written by Emilio Frazzoli and published by Springer. This book was released on 2013-02-14 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms are a fundamental component of robotic systems. Robot algorithms process inputs from sensors that provide noisy and partial data, build geometric and physical models of the world, plan high-and low-level actions at different time horizons, and execute these actions on actuators with limited precision. The design and analysis of robot algorithms raise a unique combination of questions from many elds, including control theory, computational geometry and topology, geometrical and physical modeling, reasoning under uncertainty, probabilistic algorithms, game theory, and theoretical computer science. The Workshop on Algorithmic Foundations of Robotics (WAFR) is a single-track meeting of leading researchers in the eld of robot algorithms. Since its inception in 1994, WAFR has been held every other year, and has provided one of the premiere venues for the publication of some of the eld's most important and lasting contributions. This books contains the proceedings of the tenth WAFR, held on June 13{15 2012 at the Massachusetts Institute of Technology. The 37 papers included in this book cover a broad range of topics, from fundamental theoretical issues in robot motion planning, control, and perception, to novel applications.

Download or read book Interaction aware Planning Under Uncertainty for Autonomous Driving written by Salar Arbabi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note is part of Quality testing.

Download or read book Handbook of Statistical Distributions with Applications written by K. Krishnamoorthy and published by CRC Press. This book was released on 2016-01-05 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Easy-to-Use Reference and Software for Statistical Modeling and TestingHandbook of Statistical Distributions with Applications, Second Edition provides quick access to common and specialized probability distributions for modeling practical problems and performing statistical calculations. Along with many new examples and results, this edition inclu

Download or read book Algorithmic Foundations of Robotics XII written by Ken Goldberg and published by Springer Nature. This book was released on 2020-05-06 with total page 931 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the outcomes of the 12th International Workshop on the Algorithmic Foundations of Robotics (WAFR 2016). WAFR is a prestigious, single-track, biennial international meeting devoted to recent advances in algorithmic problems in robotics. Robot algorithms are an important building block of robotic systems and are used to process inputs from users and sensors, perceive and build models of the environment, plan low-level motions and high-level tasks, control robotic actuators, and coordinate actions across multiple systems. However, developing and analyzing these algorithms raises complex challenges, both theoretical and practical. Advances in the algorithmic foundations of robotics have applications to manufacturing, medicine, distributed robotics, human–robot interaction, intelligent prosthetics, computer animation, computational biology, and many other areas. The 2016 edition of WAFR went back to its roots and was held in San Francisco, California – the city where the very first WAFR was held in 1994. Organized by Pieter Abbeel, Kostas Bekris, Ken Goldberg, and Lauren Miller, WAFR 2016 featured keynote talks by John Canny on “A Guided Tour of Computer Vision, Robotics, Algebra, and HCI,” Erik Demaine on “Replicators, Transformers, and Robot Swarms: Science Fiction through Geometric Algorithms,” Dan Halperin on “From Piano Movers to Piano Printers: Computing and Using Minkowski Sums,” and by Lydia Kavraki on “20 Years of Sampling Robot Motion.” Furthermore, it included an Open Problems Session organized by Ron Alterovitz, Florian Pokorny, and Jur van den Berg. There were 58 paper presentations during the three-day event. The organizers would like to thank the authors for their work and contributions, the reviewers for ensuring the high quality of the meeting, the WAFR Steering Committee led by Nancy Amato as well as WAFR’s fiscal sponsor, the International Federation of Robotics Research (IFRR), led by Oussama Khatib and Henrik Christensen. WAFR 2016 was an enjoyable and memorable event.