The use of robots in our daily lives is increasing. Different types of robots perform different tasks that are too dangerous or too dull to be done by humans. These sophisticated machines are susceptible to different types of faults. These faults have to be detected and diagnosed in time to allow recovery and continuous operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches and techniques that are applicable to different types of physical machines. Yet the domain of robotics poses unique requirements that are very challenging for traditional FDD approaches. The study of FDD for robotics is relatively new, and only few surveys were presented. These surveys have focused on traditional FDD approaches and how these approaches may broadly apply to a generic type of robot. Yet robotic systems can be identified by fundamental characteristics, which pose different constraints and requirements from FDD. In this article, we aim to provide the reader with useful insights regarding the use of FDD approaches that best suit the different characteristics of robotic systems. We elaborate on the advantages these approaches have and the challenges they must face. To meet this aim, we use two perspectives: (1) we elaborate on FDD from the perspective of the different characteristics a robotic system may have and give examples of successful FDD approaches, and (2) we elaborate on FDD from the perspective of the different FDD approaches and analyze the advantages and disadvantages of each approach with respect to robotic systems. Finally, we describe research opportunities for robotic systems’ FDD. With these three contributions, readers from the FDD research communities are introduced to FDD for robotic systems, and the robotics research community is introduced to the field of FDD.
This paper introduces a novel encoding of Model Based Diagnosis (MBD) to Boolean Satisfaction (SAT) focusing on minimal cardinality diagnosis. The encoding is based on a combination of sophisticated MBD preprocessing algorithms and the application of a SAT compiler which optimizes the encoding to provide more succinct CNF representations than obtained with previous works. Experimental evidence indicates that our approach is superior to all published algorithms for minimal cardinality MBD. In particular, we can determine, for the first time, minimal cardinality diagnoses for the entire standard ISCAS-85 and 74XXX benchmarks. Our results open the way to improve the state-of-the-art on a range of similar MBD problems.
This article provides new techniques for optimizing domain design for goal and plan recognition using plan libraries. We define two new problems: Goal Recognition Design for Plan Libraries (GRD-PL) and Plan Recognition Design (PRD). Solving the GRD-PL helps to infer which goal the agent is trying to achieve, while solving PRD can help to infer how the agent is going to achieve its goal. For each problem, we define a worst-case distinctiveness measure that is an upper bound on the number of observations that are necessary to unambiguously recognize the agent’s goal or plan. This article studies the relationship between these measures, showing that the worst-case distinctiveness of GRD-PL is a lower bound of the worst-case plan distinctiveness of PRD and that they are equal under certain conditions. We provide two complete algorithms for minimizing the worst-case distinctiveness of plan libraries without reducing the agent’s ability to complete its goals: One is a brute-force search over all possible plans and one is a constraint-based search that identifies plans that are most difficult to distinguish in the domain. These algorithms are evaluated in three hierarchical plan recognition settings from the literature. We were able to reduce the worst-case distinctiveness of the domains using our approach, in some cases reaching 100% improvement within a predesignated time window. Our iterative algorithm outperforms the brute-force approach by an order of magnitude in terms of runtime.
Voting is an essential mechanism that allows multiple agents to reach a joint decision. The joint decision, representing a function over the preferences of all agents, is the winner among all possible (candidate) decisions. To compute the winning candidate, previous work has typically assumed that voters send their complete set of preferences for computation, and in fact this has been shown to be required in the worst case. However, in practice, it may be infeasible for all agents to send a complete set of preferences due to communication limitations and willingness to keep as much information private as possible. The goal of this paper is to empirically evaluate algorithms to reduce communication on various sets of experiments. Accordingly, we propose an iterative algorithm that allows the agents to send only part of their preferences, incrementally. Experiments with simulated and real-world data show that this algorithm results in an average of 35% savings in communications, while guaranteeing that the actual winning candidate is revealed. A second algorithm applies a greedy heuristic to save up to 90% of communications. While this heuristic algorithm cannot guarantee that a true winning candidate is found, we show that in practice, close approximations are obtained.
Agents in a team must be in agreement. Unfortunately, they may come to disagree due to sensing uncertainty, communication failures, etc. Once a disagreement occurs we should detect the disagreement and diagnose it. Unfortunately, current diagnosis techniques do not scale well with the number of agents, as they have high communication and computation complexity. We suggest three techniques to reduce this complexity: (i) reducing the amount of diagnostic reasoning by sending targeted queries; (ii) using lightweight behavior recognition to recognize which beliefs of the agents might be in conflict; and (iii) grouping the agents according to their role and behavior and then diagnosing the groups based on representative agents. We examine these techniques in large-scale teams, in two domains, and show that combining the techniques produces a diagnosis process which is highly scalable in both communication and computation.
The use of robots has increased significantly in the recent years; rapidly expending to numerous applications. These sophisticated machines are susceptible to different types of faults that might endanger the robot or its surroundings. These faults must be detected and diagnosed in time to allow continual operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches that are applicable to different types of physical machines. However, the domain of robotics poses unique requirements that challenge traditional FDD approaches. The study of FDD for robotics is relatively new; only few surveys were presented. These surveys have focused on the single robot scenario. To the best of our knowledge, there is no survey that focuses on FDD for Multi-Robot Systems (MRS). In this paper we set out to fill this gap. This paper provides detailed insights to the world of FDD for MRS. We first describe how different attributes of MRS pose different challenges for FDD. With respect to these challenges, we survey different FDD approaches applicable for MRS. We conclude with a description of research opportunities in this field. With these contributions it is the authors’ intention to provide detailed insights to the world of FDD for MRS.
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