Effects of automation on situation awareness in controlling robot teams

Declines in situation awareness (SA) often accompany automation. Some of these effects have been characterized as out-of-the-loop, complacency, and automation bias. Increasing autonomy in multi-robot control might be expected to produce similar declines in operators’ SA. In this paper we review a series of experiments in which automation is introduced in controlling robot teams. Automating path planning at a foraging task improved both target detection and localization which is closely tied to SA. Timing data, however, suggested small declines in SA for robot location and pose. Automation of image acquisition, by contrast, led to poorer localization. Findings are discussed and alternative explanations involving shifts in strategy proposed

Multi-cue 3D Object Recognition in Knowledge-based Vision-guided Humanoid Robot System

Abstract — A vision based object recognition subsystem on knowledge-based humanoid robot system is presented. Humanoid robot system for real world service application must integrate an object recognition subsystem and a motion planning subsystem in both mobility and manipulation tasks. These requirements involve the vision system capable of self-localization for navigation tasks and object recognition for manipulation tasks, while communicating with the motion planning subsystem. In this paper, we describe a design and implementation of knowledge based visual 3D object recognition system with multi-cue integration using particle filter technique. The particle filter provides very robust object recognition performance and knowledge based approach enables robot to perform both object localization and self localization with movable/fixed information. Since this object recognition subsystem share knowledge with a motion planning subsystem, we are able to generate vision-guided humanoid behaviors without considering visual processing functions. Finally, in order to demonstrate the generality of the system, we demonstrated several vision-based humanoid behavior experiments in a daily life environment. Fig. 1. system Behavior example of knowledge based vision guided humanoid I

Robust Localization and Efficient Path Planning for Mobile Sensor Networks

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 오성회.The area of wireless sensor networks has flourished over the past decade due to advances in micro-electro-mechanical sensors, low power communication and computing protocols, and embedded microprocessors. Recently, there has been a growing interest in mobile sensor networks, along with the development of robotics, and mobile sensor networks have enabled networked sensing system to solve the challenging issues of wireless sensor networks by adding mobility into many different applications of wireless sensor networks. Nonetheless, there are many challenges to be addressed in mobile sensor networks. Among these, the estimation for the exact location is perhaps the most important to obtain high fidelity of the sensory information. Moreover, planning should be required to send the mobile sensors to sensing location considering the region of interest, prior to sensor placements. These are the fundamental problems in realizing mobile sensor networks which is capable of performing monitoring mission in unstructured and dynamic environment. In this dissertation, we take an advantage of mobility which mobile sensor networks possess and develop localization and path planning algorithms suitable for mobile sensor networks. We also design coverage control strategy using resource-constrained mobile sensors by taking advantages of the proposed path planning method. The dissertation starts with the localization problem, one of the fundamental issue in mobile sensor networks. Although global positioning system (GPS) can perform relatively accurate localization, it is not feasible in many situations, especially indoor environment and costs a tremendous amount in deploying all robots equipped with GPS sensors. Thus we develop the indoor localization system suitable for mobile sensor networks using inexpensive robot platform. We focus on the technique that relies primarily on the camera sensor. Since it costs less than other sensors, all mobile robots can be easily equipped with cameras. In this dissertation, we demonstrate that the proposed method is suitable for mobile sensor networks requiring an inexpensive off-the-shelf robotic platform, by showing that it provides consistently robust location information for low-cost noisy sensors. We also focus on another fundamental issue of mobile sensor networks which is a path planning problem in order to deploy mobile sensors in specific locations. Unlike the traditional planning methods, we present an efficient cost-aware planning method suitable for mobile sensor networks by considering the given environment, where it has environmental parameters such as temperature, humidity, chemical concentration, stealthiness and elevation. A global stochastic optimization method is used to improve the efficiency of the sampling based planning algorithm. This dissertation presents the first approach of sampling based planning using global tree extension. Based on the proposed planning method, we also presents a general framework for modeling a coverage control system consisting of multiple robots with resource constraints suitable for mobile sensor networks. We describe the optimal informative planning methods which deal with maximization problem with constraints using global stochastic optimization method. In addition, we describe how to find trajectories for multiple robots efficiently to estimate the environmental field using information obtained from all robots.Chapter 1 Introduction 1 1.1 Mobile Sensor networks 1 1.1.1 Challenges 3 1.2 Overview of the Dissertation 4 Chapter 2 Background 7 2.1 Localization in MSNs 7 2.2 Path planning in MSNs 10 2.3 Informative path planning in MSNs 12 Chapter 3 Robust Indoor Localization 15 3.1 An Overview of Coordinated Multi-Robot Localization 16 3.2 Multi-Robot Localization using Multi-View Geometry 19 3.2.1 Planar Homography for Robot Localization 20 3.2.2 Image Based Robot Control 21 3.3 Multi-Robot Navigation System 25 3.3.1 Multi-Robot System 26 3.3.2 Multi-Robot Navigation 30 3.4 Experimental Results 32 3.4.1 Coordinated Multi-Robot Localization: Single-Step 32 3.4.2 Coordinated Multi-Robot Localization: Multi-Step 36 3.5 Discussions and Comparison to Leap-Frog 42 3.5.1 Discussions 42 3.5.2 Comparison to Leap-Frog 45 3.6 Summary 51 Chapter 4 Preliminaries to Cost-Aware Path Planning 53 4.1 Related works 54 4.2 Sampling based path planning 56 4.3 Cross entropy method 59 4.3.1 Cross entropy based path planning 63 Chapter 5 Fast Cost-Aware Path Planning using Stochastic Optimization 65 5.1 Problem formulation 66 5.2 Issues with sampling-based path planning for complex terrains or high dimensional spaces 68 5.3 Cost-Aware path planning (CAPP) 73 5.3.1 CE Extend 75 5.4 Analysis of CAPP 81 5.4.1 Probabilistic Completeness 81 5.4.2 Asymptotic optimality 83 5.5 Simulation and experimental results 84 5.5.1 (P1) Cost-Aware Navigation in 2D 85 5.5.2 (P2) Complex Terrain Navigation 88 5.5.3 (P3) Humanoid Motion Planning 96 5.6 Summary 103 Chapter 6 Effcient Informative Path Planning 105 6.1 Problem formulation 106 6.2 Cost-Aware informative path planning (CAIPP) 109 6.2.1 Overall procedure 110 6.2.2 Update Bound 112 6.2.3 CE Estimate 115 6.3 Analysis of CAIPP 118 6.4 Simulation and experimental results 120 6.4.1 Single robot informative path planning 120 6.4.2 Multi robot informative path planning 122 6.5 Summary 125 Chapter 7 Conclusion and Future Work 129 Appendices 131 Appendix A Proof of Theorem 1 133 Appendix B Proof of Theorem 2 135 Appendix C Proof of Theorem 3 137 Appendix D Proof of Theorem 4 139 Appendix E Dubins' curve 141 Bibliography 147 초 록 163Docto

DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems

An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. Here, the optimization objective is to minimize the errors of estimating the relative location estimates in a distributed manner. Our novel graph-theoretic approach to solving this problem consists of three major components; (connectivity) graph formation, expansion through transition model, and optimization of relative poses. First, we estimate the relative pose-connectivity graph using the received signal strength between the connected robots, indicating relative ranges between them. Then, we apply a motion model to formulate graph expansion and optimize them using g$^2$o graph optimization as a distributed solver over dynamic networks. Finally, we theoretically analyze the algorithm and numerically validate its optimality and performance through extensive simulations. The results demonstrate the practicality of the proposed solution compared to a state-of-the-art algorithm for collaborative localization in multi-robot systems.Comment: Preprint of the Paper Accepted to DARS 202

Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper