LeaF: A Learning-based Fault Diagnostic System for Multi-Robot Teams

The failure-prone complex operating environment of a standard multi-robot application dictates some amount of fault-tolerance to be incorporated into every system. In fact, the quality of the incorporated fault-tolerance has a direct impact on the overall performance of the system. Despite the extensive work being done in the field of multi-robot systems, there does not exist a general methodology for fault diagnosis and recovery. The objective of this research, in part, is to provide an adaptive approach that enables the robot team to autonomously detect and compensate for the wide variety of faults that could be experienced. The key feature of the developed approach is its ability to learn useful information from encountered faults, unique or otherwise, towards a more robust system. As part of this research, we analyzed an existing multi-agent architecture, CMM – Causal Model Method – as a fault diagnostic solution for a sample multi-robot application. Based on the analysis, we claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors. However, the analysis also showed that the CMM method in its current form is incomplete as a turn-key solution. Due to the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Therefore, based on these preliminary studies, we designed an alternate approach, called LeaF: Learning based Fault diagnostic architecture for multi-robot teams. LeaF is an adaptive method that uses its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. LeaF combines the initial fault model with a case-based learning algorithm, LID – Lazy Induction of Descriptions — to allow robot team members to diagnose faults and to automatically update their causal models. The modified LID algorithm uses structural similarity between fault characteristics as a means of classifying previously un-encountered faults. Furthermore, the use of learning allows the system to identify and categorize unexpected faults, enable team members to learn from problems encountered by others, and make intelligent decisions regarding the environment. To evaluate LeaF, we implemented it in two challenging and dynamic physical multi-robot applications. The other significant contribution of the research is the development of metrics to measure the fault-tolerance, within the context of system performance, for a multi-robot system. In addition to developing these metrics, we also outline potential methods to better interpret the obtained measures towards truly understanding the capabilities of the implemented system. The developed metrics are designed to be application independent and can be used to evaluate and/or compare different fault-tolerance architectures like CMM and LeaF. To the best of our knowledge, this approach is the only one that attempts to capture the effect of intelligence, reasoning, or learning on the effective fault-tolerance of the system, rather than relying purely on traditional redundancy based measures. Finally, we show the utility of the designed metrics by applying them to the obtained physical robot experiments, measuring the effective fault-tolerance and system performance, and subsequently analyzing the calculated measures to help better understand the capabilities of LeaF

Policy-Based Planning for Robust Robot Navigation

This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems. Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments. DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction. Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments. This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments. The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd

Instrumented sensor system - practice

technical reportIn previous work, we introduced the notion of Instrumented Logical Sensor Systems (ILSS) that are derived from a modeling and design methodology [4, 2]. The instrumented sensor approach is based on a sensori-computational model which defines the components of the sensor system in terms of their functionality, accuracy, robustness and efficiency. This approach provides a uniform specification language to define sensor systems as a composition of smaller, predefined components. From a software engineering standpoint, this addresses the issues of modularity, reusability, and reliability for building complex multisensor systems. In this report, we demonstrate the practicality of this approach and discuss several design and implementation aspects in the context of mobile robot applications

Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Progress toward multi‐robot reconnaissance and the MAGIC 2010 competition

Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human‐robot interfaces. This paper describes our 14‐robot team, which won the MAGIC 2010 competition. It was designed to perform urban reconnaissance missions. In the paper, we describe a variety of autonomous systems that require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, which is essential for autonomous planning and for giving humans situational awareness, required the development of fast loop‐closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. We will describe technical contributions throughout our system that played a significant role in its performance. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93532/1/21426_ftp.pd

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Fault Recovery in Swarm Robotics Systems using Learning Algorithms

When faults occur in swarm robotic systems they can have a detrimental effect on collective behaviours, to the point that failed individuals may jeopardise the swarm's ability to complete its task. Although fault tolerance is a desirable property of swarm robotic systems, fault recovery mechanisms have not yet been thoroughly explored. Individual robots may suffer a variety of faults, which will affect collective behaviours in different ways, therefore a recovery process is required that can cope with many different failure scenarios. In this thesis, we propose a novel approach for fault recovery in robot swarms that uses Reinforcement Learning and Self-Organising Maps to select the most appropriate recovery strategy for any given scenario. The learning process is evaluated in both centralised and distributed settings. Additionally, we experimentally evaluate the performance of this approach in comparison to random selection of fault recovery strategies, using simulated collective phototaxis, aggregation and foraging tasks as case studies. Our results show that this machine learning approach outperforms random selection, and allows swarm robotic systems to recover from faults that would otherwise prevent the swarm from completing its mission. This work builds upon existing research in fault detection and diagnosis in robot swarms, with the aim of creating a fully fault-tolerant swarm capable of long-term autonomy

Probabilistic consolidation of grasp experience

We present a probabilistic model for joint representation of several sensory modalities and action parameters in a robotic grasping scenario. Our non-linear probabilistic latent variable model encodes relationships between grasp-related parameters, learns the importance of features, and expresses confidence in estimates. The model learns associations between stable and unstable grasps that it experiences during an exploration phase. We demonstrate the applicability of the model for estimating grasp stability, correcting grasps, identifying objects based on tactile imprints and predicting tactile imprints from object-relative gripper poses. We performed experiments on a real platform with both known and novel objects, i.e., objects the robot trained with, and previously unseen objects. Grasp correction had a 75% success rate on known objects, and 73% on new objects. We compared our model to a traditional regression model that succeeded in correcting grasps in only 38% of cases

A Broad View on Robot Self-Defense: Rapid Scoping Review and Cultural Comparison

With power comes responsibility: as robots become more advanced and prevalent, the role they will play in human society becomes increasingly important. Given that violence is an important problem, the question emerges if robots could defend people, even if doing so might cause harm to someone. The current study explores the broad context of how people perceive the acceptability of such robot self-defense (RSD) in terms of (1) theory, via a rapid scoping review, and (2) public opinion in two countries. As a result, we summarize and discuss: increasing usage of robots capable of wielding force by law enforcement and military, negativity toward robots, ethics and legal questions (including differences to the well-known trolley problem), control in the presence of potential failures, and practical capabilities that such robots might require. Furthermore, a survey was conducted, indicating that participants accepted the idea of RSD, with some cultural differences. We believe that, while substantial obstacles will need to be overcome to realize RSD, society stands to gain from exploring its possibilities over the longer term, toward supporting human well-being in difficult times