The Quest For "Always-On" Autonomous Mobile Robots

Building ``always-on'' robots to be deployed over extended periods of time in real human environments is challenging for several reasons. Some fundamental questions that arise in the process include: 1) How can the robot reconcile unexpected differences between its observations and its outdated map of the world? 2) How can we scalably test robots for long-term autonomy? 3) Can a robot learn to predict its own failures, and their corresponding causes? 4) When the robot fails and is unable to recover autonomously, can it utilize partially specified, approximate human corrections to overcome its failures? We summarize our research towards addressing all of these questions. We present 1) Episodic non-Markov Localization to maintain the belief of the robot's location while explicitly reasoning about unmapped observations; 2) a 1,000km challenge to test for long-term autonomy; 3) feature-based and learning-based approaches to predicting failures; and 4) human-in-the-loop SLAM to overcome robot mapping errors, and SMT-based robot transition repair to overcome state machine failures.

Indoor mobile robot localization in dynamic and cluttered environments using artificial landmarks

Purpose Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments. Design/methodology/approach The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot’s pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environment. Findings The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima. Originality/value By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.

Probabilistic Approach to Mobile Robot Localization Based on Gaussian Models of Sensors

In this paper the probabilistic approach to mobile robot localization is discussed. Generally probabilistic localization uses some type of sensors model. In this paper Gaussian model, which is the most appropriate probabilistic model of the sensors, is used. The main body of the article deal with the proposal of own approach to probabilistic localization, which is inspired by Markov localization. That is why the Markov localization is described in the introduction of the article. At the end of the article several experiments with the real robot are described. Results of the experiments have proven that proposed localization is accurate, fast and reliable.