An Approach for Multi-Robot Opportunistic Coexistence in Shared Space

This thesis considers a situation in which multiple robots operate in the same environment towards the achievement of different tasks. In this situation, please consider that not only the tasks, but also the robots themselves are likely be heterogeneous, i.e., different from each other in their morphology, dynamics, sensors, capabilities, etc. As an example, think about a "smart hotel": small wheeled robots are likely to be devoted to cleaning floors, whereas a humanoid robot may be devoted to social interaction, e.g., welcoming guests and providing relevant information to them upon request. Under these conditions, robots are required not only to co-exist, but also to coordinate their activity if we want them to exhibit a coherent and effective behavior: this may range from mutual avoidance to avoid collisions, to a more explicit coordinated behavior, e.g., task assignment or cooperative localization. The issues above have been deeply investigated in the Literature. Among the topics that may play a crucial role to design a successful system, this thesis focuses on the following ones: (i) An integrated approach for path following and obstacle avoidance is applied to unicycle type robots, by extending an existing algorithm [1] initially developed for the single robot case to the multi-robot domain. The approach is based on the definition of the path to be followed as a curve f (x;y) in space, while obstacles are modeled as Gaussian functions that modify the original function, generating a resulting safe path. The attractiveness of this methodology which makes it look very simple, is that it neither requires the computation of a projection of the robot position on the path, nor does it need to consider a moving virtual target to be tracked. The performance of the proposed approach is analyzed by means of a series of experiments performed in dynamic environments with unicycle-type robots by integrating and determining the position of robot using odometry and in Motion capturing environment. (ii) We investigate the problem of multi-robot cooperative localization in dynamic environments. Specifically, we propose an approach where wheeled robots are localized using the monocular camera embedded in the head of a Pepper humanoid robot, to the end of minimizing deviations from their paths and avoiding each other during navigation tasks. Indeed, position estimation requires obtaining a linear relationship between points in the image and points in the world frame: to this end, an Inverse Perspective mapping (IPM) approach has been adopted to transform the acquired image into a bird eye view of the environment. The scenario is made more complex by the fact that Pepper\u2019s head is moving dynamically while tracking the wheeled robots, which requires to consider a different IPM transformation matrix whenever the attitude (Pitch and Yaw) of the camera changes. Finally, the IPM position estimate returned by Pepper is merged with the estimate returned by the odometry of the wheeled robots through an Extened Kalman Filter. Experiments are shown with multiple robots moving along different paths in a shared space, by avoiding each other without onboard sensors, i.e., by relying only on mutual positioning information. Software for implementing the theoretical models described above have been developed in ROS, and validated by performing real experiments with two types of robots, namely: (i) a unicycle wheeled Roomba robot(commercially available all over the world), (ii) Pepper Humanoid robot (commercially available in Japan and B2B model in Europe)

Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots

Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation problem of robotics is safe area search and map building. In this report, a global collision-free path planning algorithm for ground mobile robots in dynamic environments is presented firstly. Considering the advantages of sensor network, the presented path planning algorithm is developed to a sensor network based navigation algorithm for ground mobile robots. The 2D range finder sensor network is used in the presented method to detect static and dynamic obstacles. The sensor network can guide each ground mobile robot in the detected safe area to the target. Furthermore, the presented navigation algorithm is extended into 3D environments. With the measurements of the sensor network, any flying robot in the workspace is navigated by the presented algorithm from the initial position to the target. Moreover, in this report, another navigation problem, safe area search and map building for ground mobile robot, is studied and two algorithms are presented. In the first presented method, we consider a ground mobile robot equipped with a 2D range finder sensor searching a bounded 2D area without any collision and building a complete 2D map of the area. Furthermore, the first presented map building algorithm is extended to another algorithm for 3D map building

Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace

The article presents the experimental evaluation of an integrated approach for path following and obstacle avoidance, implemented on wheeled robots. Wheeled robots are widely used in many different contexts, and they are usually required to operate in partial or total autonomy: in a wide range of situations, having the capability to follow a predetermined path and avoiding unexpected obstacles is extremely relevant. The basic requirement for an appropriate collision avoidance strategy is to sense or detect obstacles and make proper decisions when the obstacles are nearby. According to this rationale, the approach is based on the definition of the path to be followed as a curve on the plane expressed in its implicit form f(x, y) = 0, which is fed to a feedback controller for path following. Obstacles are modeled through Gaussian functions that modify the original function, generating a resulting safe path which - once again - is a curve on the plane expressed as f\u2032(x, y) = 0: the deformed path can be fed to the same feedback controller, thus guaranteeing convergence to the path while avoiding all obstacles. The features and performance of the proposed algorithm are confirmed by experiments in a crowded area with multiple unicycle-like robots and moving persons

Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions

To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict pedestrian motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.Comment: Submitted to 2014 International Workshop on the Algorithmic Foundations of Robotic

Optimal Path Planning in Distinct Topo-Geometric Classes using Neighborhood-augmented Graph and its Application to Path Planning for a Tethered Robot in 3D

Many robotics applications benefit from being able to compute multiple locally optimal paths in a given configuration space. Examples include path planning for of tethered robots with cable-length constraints, systems involving cables, multi-robot topological exploration & coverage, and, congestion reduction for mobile robots navigation without inter-robot coordination. Existing paradigm is to use topological path planning methods that can provide optimal paths from distinct topological classes available in the underlying configuration space. However, these methods usually require non-trivial and non-universal geometrical constructions, which are prohibitively complex or expensive in 3 or higher dimensional configuration spaces with complex topology. Furthermore, topological methods are unable to distinguish between locally optimal paths that belong to the same topological class but are distinct because of genus-zero obstacles in 3D or due to high-cost or high-curvature regions. In this paper we propose an universal and generalized approach to multi-class path planning using the concept of a novel neighborhood-augmented graph, search-based planning in which can compute paths in distinct topo-geometric classes. This approach can find desired number of locally optimal paths in a wider variety of configuration spaces without requiring any complex pre-processing or geometric constructions. Unlike the existing topological methods, resulting optimal paths are not restricted to distinct topological classes, thus making the algorithm applicable to many other problems where locally optimal and geometrically distinct paths are of interest. For the demonstration of an application of the proposed approach, we implement our algorithm to planning for shortest traversible paths for a tethered robot with cable-length constraint navigating in 3D and validate it in simulations & experiments.Comment: 18 pages, 17 figure

Localization and Navigation of the CoBots Over Long-term Deployments

For the last three years, we have developed and researched multiple collaborative robots, CoBots, which have been autonomously traversing our multi-floor buildings. We pursue the goal of long-term autonomy for indoor service mobile robots as the ability for them to be deployed indefinitely while they perform tasks in an evolving environment. The CoBots include several levels of autonomy, and in this paper we focus on their localization and navigation algorithms. We present the Corrective Gradient Refinement (CGR) algorithm, which refines the proposal distribution of the particle filter used for localization with sensor observations using analytically computed state space derivatives on a vector map. We also present the Fast Sampling Plane Filtering (FSPF) algorithm that extracts planar regions from depth images in real time. These planar regions are then projected onto the 2D vector map of the building, and along with the laser rangefinder observations, used with CGR for localization. For navigation, we present a hierarchical planner, which computes a topological policy using a graph representation of the environment, computes motion commands based on the topological policy, and then modifies the motion commands to side-step perceived obstacles. The continuous deployments of the CoBots over the course of one and a half years have provided us with logs of the CoBots traversing more than 130km over 1082 deployments, which we publish as a dataset consisting of more than 10 million laser scans. The logs show that although there have been continuous changes in the environment, the robots are robust to most of them, and there exist only a few locations where changes in the environment cause increased uncertainty in localization