Autonomous navigation is a pre-requisite for field robots to carry out precision agriculture tasks. Typically, a robot has to navigate along a crop field multiple times during a season for monitoring the plants, for applying agrochemicals, or for performing targeted interventions. In this paper, we propose a visual-based navigation framework tailored to row-crop fields that exploits the regular crop-row structure present in fields. Our approach uses only the images from on-board cameras without the need for performing explicit localization or maintaining a map of the field. Thus, it can operate without expensive RTK-GPS solutions often used in agricultural automation systems. Our navigation approach allows the robot to follow the crop rows accurately and handles the switch to the next row seamlessly within the same framework. We implemented our approach using C++ and ROS and thoroughly tested it in several simulated fields with different shapes and sizes. We also demonstrated the system running at frame-rate on an actual robot operating on a test row-crop field. The code and data have been published.

Nowadays, mobile robots are deployed in many indoor environments such as offices or hospitals. These environments are subject to changes in the traversability that often happen following patterns. In this paper, we investigate the problem of navigating in such environments over extended periods of time by capturing and exploiting these patterns to make informed decisions for navigation. Our approach uses a probabilistic graphical model to incrementally estimate a model of the traversability changes from the robot's observations and to make predictions at currently unobserved locations. In the belief space defined by the predictions, we plan paths that trade off the risk to encounter obstacles and the information gain of visiting unknown locations. We implemented our approach and tested it in different indoor environments. The experiments suggest that, in the long run, our approach leads robots to navigate along shorter paths compared to following a greedy shortest path policy.

Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data. We propose a novel, neural network-based observation model that computes the expected overlap of two 3D LiDAR scans. The model predicts the overlap and yaw angle offset between the current sensor reading and virtual frames generated from a pre-built map. We integrate this observation model into a Monte-Carlo localization framework and tested it on urban datasets collected with a car in different seasons. The experiments presented in this paper illustrate that our method can reliably localize a vehicle in typical urban environments. We furthermore provide comparisons to a beam-endpoint and a histogram-based method indicating a superior global localization performance of our method with fewer particles.

Robot navigation in outdoor environments is exposed to detrimental factors such as vibrations or power consumption due to the different terrains on which the robot navigates. In this paper, we address the problem of actively improving navigation by planning paths that aim at reducing over time phenomena such as vibrations during traversal. Our approach uses a Gaussian Process (GP) mixture model and an aerial image of the environment to learn and improve continuously a place-dependent model of such phenomena from the experiences of the robot. We use this model to plan paths that trade-off the exploration of unknown promising regions and the exploitation of known areas where the impact of the detrimental factors on navigation is low, leading to an improved navigation over time. We implemented our approach and thoroughly tested it using real-world data. Our experiments suggest that our approach with no initial information leads the robot, after few runs, to follow paths along which it experiences similar vibrations or energy consumption as if it was following the optimal path computed given the ground truth information.

Although most robots use probabilistic algorithms to solve state estimation problems, path planning is often performed without considering the uncertainty about the robot's position. Uncertainty, however, matters in planning, but considering it often leads to computationally expensive algorithms. In this paper, we investigate the problem of path planning considering the uncertainty in the robot's belief about the world, in its perceptions and in its action execution. We propose the use of an uncertainty-augmented Markov Decision Process to approximate the underlying Partially Observable Markov Decision Process, and we employ a localization prior to estimate how the belief about the robot's position propagates through the environment. This yields to a planning approach that generates navigation policies able to make decisions according to the degree of uncertainty while being computationally tractable. We implemented our approach and thoroughly evaluated it on different navigation problems. Our experiments suggest that we are able to compute policies that are more effective than approaches that ignore the uncertainty, and that also outperform policies that always take the safest actions.

The demand for flexible industrial robotic solutions that are able to accomplish tasks at different locations in a factory is growing more and more. When deploying mobile robots in a factory environment, the predictability and reproducibility of their behaviors become important and are often requested. In this paper, we propose an easy-to-use motion planning scheme that can take into account user preferences for robot navigation. The preferences are extracted implicitly from the previous experiences or from demonstrations and are automatically considered in the subsequent planning steps. This leads to reproducible and thus better to predict navigation behaviors of the robot, without requiring experts to hard-coding control strategies or cost functions within a planner. Our system has been implemented and evaluated on a simulated KUKA mobile robot in different environments.

As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of explicit representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.