Situational reasoning for road driving in an urban environment

Robot navigation in urban environments requires situational reasoning. Given the complexity of the environment and the behavior specified by traffic rules, it is necessary to recognize the current situation to impose the correct traffic rules. In an attempt to manage the complexity of the situational reasoning subsystem, this paper describes a finite state machine model to govern the situational reasoning process. The logic state machine and its interaction with the planning system are discussed. The approach was implemented on Alice, Team Caltech’s entry into the 2007 DARPA Urban Challenge. Results from the qualifying rounds are discussed. The approach is validated and the shortcomings of the implementation are identified

Robotic motion planning in dynamic, cluttered, uncertain environments

This paper presents a strategy for planning robot motions in dynamic, cluttered, and uncertain environments. Successful and efficient operation in such environments requires reasoning about the future system evolution and the uncertainty associated with obstacles and moving agents in the environment. This paper presents a novel procedure to account for future information gathering (and the quality of that information) in the planning process. After first presenting a formal Dynamic Programming (DP) formulation, we present a Partially Closed-loop Receding Horizon Control algorithm whose approximation to the DP solution integrates prediction, estimation, and planning while also accounting for chance constraints that arise from the uncertain location of the robot and other moving agents. Simulation results in simple static and dynamic scenarios illustrate the benefit of the algorithm over classical approaches

Probabilistic Collision Checking with Chance Constraints

Abstract-Obstacle avoidance, and by extension collision checking, is a basic requirement for robot autonomy. Most classical approaches to collision checking ignore the uncertainties associated with the robot and obstacle's geometry and position. It is natural to use a probabilistic description of the uncertainties. However, constraint satisfaction cannot be guaranteed in this case and collision constraints must instead be converted to chance constraints. Standard results for linear probabilistic constraint evaluation have been applied to probabilistic collision evaluation, but this approach ignores the uncertainty associated with the sensed obstacle. An alternative formulation of probabilistic collision checking that accounts for robot and obstacle uncertainty is presented which allows for dependent object distributions (e.g., interactive robotobstacle models). In order to efficiently enforce the resulting collision chance constraints, an approximation is proposed and the validity of this approximation is evaluated. The results presented here have been applied to robot motion planning in dynamic, uncertain environments

Robotic motion planning in dynamic, cluttered, uncertain environments

This paper presents a strategy for planning robot motions in dynamic, cluttered, and uncertain environments. Successful and efficient operation in such environments requires reasoning about the future system evolution and the uncertainty associated with obstacles and moving agents in the environment. This paper presents a novel procedure to account for future information gathering (and the quality of that information) in the planning process. After first presenting a formal Dynamic Programming (DP) formulation, we present a Partially Closed-loop Receding Horizon Control algorithm whose approximation to the DP solution integrates prediction, estimation, and planning while also accounting for chance constraints that arise from the uncertain location of the robot and other moving agents. Simulation results in simple static and dynamic scenarios illustrate the benefit of the algorithm over classical approaches

Probabilistic Collision Checking With Chance Constraints

Obstacle avoidance, and by extension collision checking, is a basic requirement for robot autonomy. Most classical approaches to collision-checking ignore the uncertainties associated with the robot and obstacle’s geometry and position. It is natural to use a probabilistic description. of the uncertainties. However, constraint satisfaction cannot be guaranteed, in this case, and collision constraints must instead be converted to chance constraints. Standard results for linear probabilistic constraint evaluation have been applied to probabilistic collision evaluation; however, this approach ignores the uncertainty associated with the sensed obstacle. An alternative formulation of probabilistic collision checking that accounts for robot and obstacle uncertainty is presented which allows for dependent object distributions (e.g., interactive robot-obstacle models). In order to efficiently enforce the resulting collision chance constraints, an approximation is proposed and the validity of this approximation is evaluated. The results presented here have been applied to robot-motion planning in dynamic, uncertain environments

Robot Motion Planning in Dynamic, Uncertain Environments

This paper presents a strategy for planning robot motions in dynamic, uncertain environments (DUEs). Successful and efficient robot operation in such environments requires reasoning about the future evolution and uncertainties of the states of the moving agents and obstacles. A novel procedure to account for future information gathering (and the quality of that information) in the planning process is presented. To approximately solve the stochastic dynamic programming problem that is associated with DUE planning, we present a partially closed-loop receding horizon control algorithm whose solution integrates prediction, estimation, and planning while also accounting for chance constraints that arise from the uncertain locations of the robot and obstacles. Simulation results in simple static and dynamic scenarios illustrate the benefit of the algorithm over classical approaches. The approach is also applied to more complicated scenarios, including agents with complex, multimodal behaviors, basic robot-agent interaction, and agent information gathering

Human steroid biosynthesis, metabolism and excretion are differentially reflected by serum and urine steroid metabolomes: a comprehensive review.

Advances in technology have allowed for the sensitive, specific, and simultaneous quantitative profiling of steroid precursors, bioactive steroids and inactive metabolites, facilitating comprehensive characterization of the serum and urine steroid metabolomes. The quantification of steroid panels are therefore gaining favor over quantification of single marker metabolites in the clinical and research laboratories. However, although the biochemical pathways for the biosynthesis and metabolism of steroid hormones are now well defined, a gulf still exists between this knowledge and its application to the measured steroid profiles. In this review, we present an overview of steroid hormone biosynthesis and metabolism by the liver and peripheral tissues, specifically highlighting the pathways linking and differentiating the serum and urine steroid metabolomes. A brief overview of the methodology used in steroid profiling is also provided