Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

On Delaying Collision Checking in PRM Planning: Application to Multi-Robot Coordination

Abstract: This paper describes the foundations and algorithms of a new probabilistic roadmap (PRM) planner that is: single-query—instead of pre-computing a roadmap covering the entire free space, it uses the two input query configurations to explore as little space as possible; bi-directional—it explores the robot's free space by building a roadmap made of two trees rooted at the query configurations; and lazy in checking collisions—it delays collision tests along the edges of the roadmap until they are absolutely needed. Several observations motivated this strategy: (1) PRM planners spend a large fraction of their time testing connections for collision; (2) most connections in a roadmap are not on the final path; (3) the collision test for a connection is most expensive when there is no collision; and (4) any short connection between two collision-free configurations has high prior probability of being collision-free. The strengths of single-query and bi-directional sampling techniques and those of delayed collisi...

Planning in information space for a quadrotor helicopter in a GPS-denied environment

Abstract: This paper describes a motion planning algorithm for a quadrotor helicopter flying autonomously without GPS. Without accurate global positioning, the vehicle's ability to localize itself varies across the environment, since different environmental features provide different degrees of localization. If the vehicle plans a path without regard to how well it can localize itself along that path, it runs the risk of becoming lost. We use the Belief Roadmap (BRM) algorithm [1], an information-space extension of the Probabilistic Roadmap algorithm, to plan vehicle trajectories that incorporate sensing. We show that the original BRM can be extended to use the Unscented Kalman Filter (UKF), and describe a sampling algorithm that minimizes the number of samples required to find a good path. Finally, we demonstrate the BRM path- planning algorithm on the helicopter, navigating in an indoor environment with a laser range-finder.

An Efficient Localization Scheme for a Differential-Driving Mobile Robot Based on RFID System

Abstract: This paper presents an efficient localization scheme for an indoors mobile robot using Radio-Frequency IDentification (RFID) systems. The mobile robot carries an RFID reader at the bottom of the chassis, which reads the RFID tags on the floor to localize the mobile robot. Each of the RFID tags stores its own absolute position, which is used to calculate the position, orientation, and velocity of the mobile robot. However, a localization system based on RFID technology inevitably suffers from an estimation error. In this paper, a new triangular pattern of arranging the RFID tags on the floor has been proposed to reduce the estimation error of the conventional square pattern. In addition, the motion-continuity property of the differential-driving mobile robot has been utilized to improve the localization accuracy of the mobile robot. According to the conventional approach, two readers are necessary to identify the orientation of the mobile robot. Therefore, this new approach, based on the motion-continuity property of the differential-driving mobile robot, provides a cheap and fast estimation of the orientation. The proposed algorithms used to raise the accuracy of the robot localization are successfully verified through experiments.

Robot navigation in dense human crowds: Statistical models and experimental studies of human-robot cooperation

Abstract: We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the “freezing robot problem”: once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place or performs unnecessary maneuvers to avoid collisions. We argue that this problem can be avoided if the robot anticipates human cooperation, and accordingly we develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a “multiple goal” extension that models the goal-driven nature of human decision making. We validate this model with an empirical study of robot navigation in dense human crowds 488 runs, specifically testing how cooperation models effect navigation performance. The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 0.8 humans/m2, while a state-of-the-art non-cooperative planner exhibits unsafe behavior more than three times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m2. We also show that our non-cooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.