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

Adaptive causal models for fault diagnosis and recovery in multi-robot teams

Abstract — This paper presents an adaptive causal model method (adaptive CMM) for fault diagnosis and recovery in complex multi-robot teams. We claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors, presenting extensive experimental results to support this claim. To our knowledge, these results show the first, full implementation of a CMM on a large multi-robot team. However, because of the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, our empirical results show that one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Instead, an adaptive method is needed to enable the robot team to use its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. We present our case-based learning approach, called LeaF (for Learning-based Fault diagnosis), that enables robot team members to adapt their causal models, thereby improving their ability to diagnose and recover from these faults over time. I

Proc. of Performance Metrics for Intelligent Systems Workshop, 2006. Fault-Tolerance Based Metrics for Evaluating System Performance in Multi-Robot Teams

Abstract — The failure-prone complex operating environment of a standard multi-robot application dictates some amount of fault-tolerance to be incorporated into the system. Being able to identify the extent of fault-tolerance in a system would be a useful analysis tool for the designer. Unfortunately, it is difficult to quantify system fault-tolerance on its own. A more tangible metric for evaluation is the “effectiveness” [8] measure of fault-tolerance. Effectiveness is measured by identifying the influence of fault-tolerance towards overall system performance. In this paper, we explore the significance of the relationship between fault-tolerance and system performance, and develop metrics to measure fault-tolerance within the context of system performance. A main focus of our approach is to capture the effect of intelligence, reasoning, or learning on the effective fault-tolerance of the system, rather than relying purely on measures of redundancy. The developed metrics are designed to be application independent and can be used to evaluate and/or compare different fault-diagnostic architectures. We show the utility of the designed metrics by applying them to a sample complex heterogeneous multi-robot team application and evaluating the effective fault-tolerance exhibited by the system. I

on Intelligent Robots and Systems, Beijing, China, 2006. Adaptive Causal Models for Fault Diagnosis and Recovery in Multi-Robot Teams

Abstract — This paper presents an adaptive causal model method (adaptive CMM) for fault diagnosis and recovery in complex multi-robot teams. We claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors, presenting extensive experimental results to support this claim. To our knowledge, these results show the first, full implementation of a CMM on a large multi-robot team. However, because of the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, our empirical results show that one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Instead, an adaptive method is needed to enable the robot team to use its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. We present our case-based learning approach, called LeaF (for Learning-based Fault diagnosis), that enables robot team members to adapt their causal models, thereby improving their ability to diagnose and recover from these faults over time. I

Market-based Coordination of Recharging Robots

As multi-robot systems gain acceptance for use in functionally-distributed missions that require complex coordination for executing tasks such as planning, coordination, and information sharing in highly dynamic and potentially hazardous operating environments [12, 13, 20, 28-31], the ability of the robots to operate for extended time in the field becomes critical to mission success. Consequently, the problem of autonomous recharging is becoming increasingly important to mobile robotics as it has the potential to greatly enhance the operational time and capability of robots. Existing approaches, however, are greedy in nature and have little to no coordination between robots, leading to inefficient solutions that adversely affect system performance. Effective coordination of robot teams is an ongoing challenge and has been addressed using techniques varying from switched control [38-39], vision-based formation control [40], to market based approaches [27, 42, 43]. In this report, we advance the state of the art in autonomous recharging by developing, implementing, testing, and evaluating a market-based distributed algorithm for effectively coordinating recharging robots. Such a system is “charge-aware” and accounts for battery life when during task allocation process. The developed solution has been evaluated, in simulation and in field tests, on a team of pioneer mobile robots executing a set of transportation tasks in an indoor environment. Results show that our approach consistently outperforms the state of the art in recharging strategies.</p