Mobile robot navigation

About: Mobile robot navigation is a research topic. Over the lifetime, 14713 publications have been published within this topic receiving 263092 citations.

Socially aware motion planning with deep reinforcement learning

Abstract: For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.

Estimating the absolute position of a mobile robot using position probability grids

Abstract: In order to re-use existing models of the environment mobile robots must be able to estimate their position and orientation in such models. Most of the existing methods for position estimation are based on special purpose sensors or aim at tracking the robot's position relative to the known starting point. This paper describes the position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment. Our method is designed to work with standard sensors and is independent of any knowledge about the starting point. It is a Bayesian approach based on certainty grids. In each cell of such a grid we store the probability that this cell refers to the current position of the robot. These probabilities are obtained by integrating the likelihoods of sensor readings over time. Results described in this paper show that our technique is able to reliably estimate the position of a robot in complex environments. Our approach has proven to be robust with respect to inaccurate environmental models, noisy sensors, and ambiguous situations.

Navigation Strategies for Exploring Indoor Environments

Abstract: In this paper, we investigate safe and efficient map-building strategies for a mobile robot with imperfect control and sensing. In the implementation, a robot equipped with a range sensor builds apolygonal map layout of a previously unknown indoor environment. The robot explores the environment and builds the map concurrently by patching together the local models acquired by the sensor into a global map. A well-studied and related problem is the simultaneous localization and mapping SLAM problem, where the goal is to integrate the information collected during navigation into the most accurate map possible. However, SLAM does not address the sensor-placement portion of the map-building task. That is, given the map built so far, where should the robot go next? This is the main question addressed in this paper. Concretely, an algorithm is proposed to guide the robot through a series of ?good? positions, where ?good? refers to the expected amount and quality of the information that will be revealed at each new location. This is similar to the next-best-view NBV problem studied in computer vision and graphics. However, in mobile robotics the problem is complicated by several issues, two of which are particularly crucial. One is to achieve safe navigation despite an incomplete knowledge of the environment and sensor limitations e.g., in range and incidence. The other issue is the need to ensure sufficient overlap between each new local model and the current map, in order to allow registration of successive views under positioning uncertainties inherent to mobile robots. To address both issues in a coherent framework, in this paper we introduce the concept of a safe region, defined as the largest region that is guaranteed to be free of obstacles given the sensor readings made so far. The construction of a safe region takes sensor limitations into account. In this paper we also describe an NBV algorithm that uses the safe-region concept to select the next robot position at each step. The new position is chosen within the safe region in order to maximize the expected gain of information under the constraint that the local model at this new position must have a minimal overlap with the current global map. In the future, NBV and SLAM algorithms should reinforce each other. While a SLAM algorithm builds a map by making the best use of the available sensory data, an NBV algorithm, such as that proposed here, guides the navigation of the robot through positions selected to provide the best sensory inputs.