skip to main content

Explore Research Products in the NSF-PAR

Title: Misdirection in Robot Teams: Methods and Ethical Considerations (Extended Abstract)
This NSF funded project currently underway studies strategies to enable robots, multi-robots and teams of multi-robots to model, generate, and cope with misdirection in various situations. This research direction in robotic control offers a novel approach to resilience in and among these teams to these forms of possible disruption. Computational models, drawn particularly from studies of human endeavors and group behaviors, provide a general framework for understanding, producing, and countering misdirection in robotic systems. A framework of computational models will be designed using recursive schema-theoretic models of behaviors at the individual and team levels, building on decentralized methods of control and communication.  more » « less
Award ID(s):
1848653
NSF-PAR ID:
10172210
Author(s) / Creator(s):
Date Published:
Journal Name:
2019 Conference of the International Association for Computing and Philosophy (IACAP 2019
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ( , 2021 IEEE International Conference on Intelligence and Safety for Robotics)
    null (Ed.)
    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. 
    more » « less
  2. ; ( , Proceedings of 15th IEEE International Conference on Advanced Robotics and Its SOcial Impacts (ARSO-2019))
    Teams of robots tasked with making critical decisions in competitive environments are at risk for being shepherded or misdirected to a location that is advantageous for a competing team. Our lab is working to understand how adversarial teams of robots can successfully move their competition to desired locations in part so that we can then devise practices to counter these strategies and help make team functioning more successful and secure. In this paper, preliminary research is presented that studies how a team of robots can be shepherded or misdirected to a disadvantageous location. We draw inspiration from herding practices as well as deceptive practices seen in higher-order primates and humans. We define behaviors for the target (mark) agents to be moved as well as members of the shepherding team (a pushing agent and pulling shills) and present simulation results showing how these behaviors move robots to a desired location. These behaviors were implemented and trialed on hardware platform. A discussion of ongoing research into understanding misdirection in multi-robot teams concludes this paper. 
    more » « less
  3. ( , 2019 International Association for Computing and Philosophy annual conference)
    Trust, dependability, cohesion, and capability are integral to an effective team. These attributes are the same for teams of robots. When multiple teams with competing incentives are tasked, a strategy, if available, may be to weaken, influence or sway the attributes of other teams and limit their understanding of their full range of options. Such strategies are widely found in nature and in sporting contests such as feints, misdirection, etc. This talk focuses on one class of higher-level strategies for multi-robots, i.e., to intentionally misdirect using shills or confederates where needed, and the ethical considerations associated with deploying such teams. As multi-robot systems become more autonomous, distributed, networked, numerous, and with more capability to make critical decisions, the prospect for intentional and unintentional misdirection must be anticipated. While benefits are clearly apparent to the team performing the deception, ethical questions surrounding the use of misdirection or other forms of deception are quite real. 
    more » « less
  4. ; ; ; ( , 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE))
    null (Ed.)
    Recursive neural networks can be trained to serve as a memory for robots to perform intelligent behaviors when localization is not available. This paper develops an approach to convert a spatial map, represented as a scalar field, into a trained memory represented by the long short-term memory (LSTM) neural network. The trained memory can be retrieved through sensor measurements collected by robots to achieve intelligent behaviors, such as tracking level curves in the map. Memory retrieval does not require robot locations. The retrieved information is combined with sensor measurements through a Kalman filter enabled by the LSTM (LSTM-KF). Furthermore, a level curve tracking control law is designed. Simulation results show that the LSTM-KF and the control law are effective to generate level curve tracking behaviors for single-robot and multi-robot teams. 
    more » « less
  5. ; ; ; ; ; ; ; ; ( , 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft))
    Soft robots employ flexible and compliant materials to perform adaptive tasks and navigate uncertain environments. However, soft robots are often unable to achieve forces and precision on the order of rigid-bodied robots. In this paper, we propose a new class of mobile soft robots that can reversibly transition between compliant and stiff states without reconfiguration. The robot can passively conform or actively control its shape, stiffen in its current configuration to function as a rigid-bodied robot, then return to its flexible form. The robotic structure consists of passive granular material surrounded by an active membrane. The membrane is composed of interconnected robotic sub-units that can control the packing density of the granular material and exploit jamming behaviors by varying the length of the interconnecting cables. Each robotic sub-unit uses a differential drive system to achieve locomotion and self-reconfigurability. We present the robot design and perform a set of locomotion and object manipulation experiments to characterize the robot's performance in soft and rigid states. We also introduce a simulation framework in which we model the jamming soft robot design and study the scalability of this class of robots. The proposed concept demonstrates the properties of both soft and rigid robots, and has the potential to bridge the gap between the two 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email