Shengkang Chen

As robots are becoming more intelligent and more commonly used, it is critical for robots to behave ethically in human-robot interactions. However, there is a lack of agreement on a correct moral theory to guide human behavior, let alone robots. This paper introduces a robotic architecture that leverages cases drawn from different ethical frameworks to guide the ethical decision-making process and select the appropriate robotic action based on the specific situation. We also present an architecture implementation design used on a pill sorting task for older adults, where the robot needs to decide if it is appropriate to provide false encouragement so that the adults continue to be engaged in the training task.

This paper proposes a new cooperative localization algorithm that separates communication and observation into independent mechanisms. While existing algorithms acknowledge observations between robots are crucial in cooperative localization schemes, communication is considered only an auxiliary role in observation update but not explicitly stated. However, such algorithms require the communication to be available whenever needed, and it is difficult to consider the effect of communication imperfection, which is unavoidable in real systems. We propose the Global State–Covariance Intersection (GS-CI) multirobot cooperative localization algorithm that can independently update localization estimates through both observation and communication steps. We also provide a theoretical upper bound of the resulting estimation uncertainty based on observation and communication topologies. Simulations using generated data validates the theoretical analysis, and shows the comparable performance to the centralized equivalent approach with less communication together with real-world data.

Extended Kalman filter (EKF) does not guarantee consistent mean and covariance under linearization, even though it is the main framework for robotic localization. While Lie group improves the modeling of the state space in localization, the EKF on Lie group still relies on the arbitrary Gaussian assumption in face of nonlinear models. We instead use von Mises filter for orientation estimation together with the conventional Kalman filter for position estimation, and thus we are able to characterize the first two moments of the state estimates. Since the proposed algorithm holds a solid probabilistic basis, it is fundamentally relieved from the inconsistency problem. Furthermore, we extend the localization algorithm to fully circular representation even for position, which is similar to grid patterns found in mammalian brains and in recurrent neural networks. The applicability of the proposed algorithms is substantiated not only by strong mathematical foundation but also by the compar...

Heterogeneous teams of robots, leveraging a balance between autonomy and human interaction, bring powerful capabilities to the problem of exploring dangerous, unstructured subterranean environments. Here we describe the solution developed by Team CSIRO Data61, consisting of CSIRO, Emesent and Georgia Tech, during the DARPA Subterranean Challenge. These presented systems were fielded in the Tunnel Circuit in August 2019, the Urban Circuit in February 2020, and in our own Cave event, conducted in September 2020. A unique capability of the fielded team is the homogeneous sensing of the platforms utilised, which is leveraged to obtain a decentralised multi-agent SLAM solution on each platform (both ground agents and UAVs) using peer-to-peer communications. This enabled a shift in focus from constructing a pervasive communications network to relying on multi-agent autonomy, motivated by experiences in early circuit events. These experiences also showed the surprising capability of rugged...

When teams of mobile robots are tasked with different goals in a competitive environment, misdirection and counter-misdirection can provide significant advantages. Researchers have studied different misdirection methods but the number of approaches on counter-misdirection for multi-robot systems is still limited. In this work, a novel counter-misdirection approach for behavior-based multi-robot teams is developed by deploying a new type of agent: counter-misdirection agents (CMAs). These agents can detect the misdirection process and “push back” the misdirected agents collaboratively to stop the misdirection process. This approach has been implemented not only in simulation for various conditions, but also on a physical robotic testbed to study its effectiveness. It shows that this approach can stop the misdirection process effectively with a sufficient number of CMAs. This novel counter-misdirection approach can potentially be applied to different competitive scenarios such as military and sports applications.