Adaptive user interface for vehicle swarm control

An algorithm to automatically generate behaviors for robotic vehicles has been created and tested in a laboratory setting. This system is designed to be applied in situations where a large number of robotic vehicles must be controlled by a single operator. The system learns what behaviors the operator typically issues and offers these behaviors to the operator in future missions. This algorithm uses the symbolic clustering method Gram-ART to generate these behaviors. Gram-ART has been shown to be successful at clustering such standard symbolic problems as the mushroom dataset and the Unix commands dataset. The algorithm was tested by having users complete exploration and tracking missions. Users were brought in for two sessions of testing. In the first session, they familiarized themselves with the testing interface and generated training information for Gram-ART. In the second session, the users ran missions with and without the generated behaviors to determine what effect the generated behaviors had on the users\u27 performance. Through these human tests, missions with generated behaviors enabled are shown to have reduced operator workload over those without. Missions with generated behaviors required fewer button presses than those without while maintaining a similar or greater level of mission success. Users also responded positively in a survey after the second session. Most users\u27 responses indicated that the generated behaviors increased their ability to complete the missions --Abstract, page iii

A Multi-Robot Task Assignment Framework for Search and Rescue with Heterogeneous Teams

In post-disaster scenarios, efficient search and rescue operations involve collaborative efforts between robots and humans. Existing planning approaches focus on specific aspects but overlook crucial elements like information gathering, task assignment, and planning. Furthermore, previous methods considering robot capabilities and victim requirements suffer from time complexity due to repetitive planning steps. To overcome these challenges, we introduce a comprehensive framework__the Multi-Stage Multi-Robot Task Assignment. This framework integrates scouting, task assignment, and path-planning stages, optimizing task allocation based on robot capabilities, victim requirements, and past robot performance. Our iterative approach ensures objective fulfillment within problem constraints. Evaluation across four maps, comparing with a state-of-the-art baseline, demonstrates our algorithm's superiority with a remarkable 97 percent performance increase. Our code is open-sourced to enable result replication.Comment: The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023 Advances in Multi-Agent Learning - Coordination, Perception, and Control Workshop

Impact of Relational Networks in Multi-Agent Learning: A Value-Based Factorization View

Effective coordination and cooperation among agents are crucial for accomplishing individual or shared objectives in multi-agent systems. In many real-world multi-agent systems, agents possess varying abilities and constraints, making it necessary to prioritize agents based on their specific properties to ensure successful coordination and cooperation within the team. However, most existing cooperative multi-agent algorithms do not take into account these individual differences, and lack an effective mechanism to guide coordination strategies. We propose a novel multi-agent learning approach that incorporates relationship awareness into value-based factorization methods. Given a relational network, our approach utilizes inter-agents relationships to discover new team behaviors by prioritizing certain agents over other, accounting for differences between them in cooperative tasks. We evaluated the effectiveness of our proposed approach by conducting fifteen experiments in two different environments. The results demonstrate that our proposed algorithm can influence and shape team behavior, guide cooperation strategies, and expedite agent learning. Therefore, our approach shows promise for use in multi-agent systems, especially when agents have diverse properties.Comment: Accepted to International Conference on Decision and Control (IEEE CDC 2023

Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams

Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared objectives in multi-agent teams. In real-world scenarios, however, agents may encounter unforeseen failures due to constraints like battery depletion or mechanical issues. Existing state-of-the-art methods in MARL often recover slowly -- if at all -- from such malfunctions once agents have already converged on a cooperation strategy. To address this gap, we present the Collaborative Adaptation (CA) framework. CA introduces a mechanism that guides collaboration and accelerates adaptation from unforeseen failures by leveraging inter-agent relationships. Our findings demonstrate that CA enables agents to act on the knowledge of inter-agent relations, recovering from unforeseen agent failures and selecting appropriate cooperative strategies.Comment: Presented at Multi-Agent Dynamic Games (MADGames) workshop at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Influence of Team Interactions on Multi-Robot Cooperation: A Relational Network Perspective

Relational networks within a team play a critical role in the performance of many real-world multi-robot systems. To successfully accomplish tasks that require cooperation and coordination, different agents (e.g., robots) necessitate different priorities based on their positioning within the team. Yet, many of the existing multi-robot cooperation algorithms regard agents as interchangeable and lack a mechanism to guide the type of cooperation strategy the agents should exhibit. To account for the team structure in cooperative tasks, we propose a novel algorithm that uses a relational network comprising inter-agent relationships to prioritize certain agents over others. Through appropriate design of the team's relational network, we can guide the cooperation strategy, resulting in the emergence of new behaviors that accomplish the specified task. We conducted six experiments in a multi-robot setting with a cooperative task. Our results demonstrate that the proposed method can effectively influence the type of solution that the algorithm converges to by specifying the relationships between the agents, making it a promising approach for tasks that require cooperation among agents with a specified team structure.Comment: Accepted to Multi-Robot and Multi-Agent Systems (IEEE MRS 2023

Potential Polygamous Breeding Behavior in Northern Bobwhite

Breeding behavior ofradio-tagged northern bobwhite (Colinus uirginianus) was observed at Fort Bragg Military Reservation (n = 19), North Carolina, in 1985-88, and Tall Timbers Research Station (n = 27), Florida, during 1984-86. We observed apparent polygamous breeding behavior in 95% (18 of 19) of the radio-tagged northern bobwhite at Fort Bragg, and 93% (25 of 27) of the birds at Tall Timbers. We documented 5 cases of double-clutching by radio-tagged females. Twenty-seven percent of Fort Bragg clutches (n = 30), and 20% of Tall Timbers clutches (n = 56) were incubated by radio-tagged males. Northern bobwhite exhibited characteristics of both rapid multiclutch and am bisexual polygamous mating systems. Northern bobwhite are capable of uniparental care, have long breeding seasons, live in an environment with fluctuating resources, suffer high predation pressure during the nesting season, and raise precocial young; all traits that are similar to other bird species which have evolved polygamous mating systems

Real Time Mission Planning

The different advantageous embodiments provide a system comprising a number of computers, a graphical user interface, first program code stored on the computer, and second program code stored on the computer. The graphical user interface is executed by a computer in the number of computers. The computer is configured to run the first program code to define a mission using a number of mission elements. The computer is configured to run the second program code to generate instructions for a number of assets to execute the mission and monitor the number of assets during execution of the mission

Real Time Mission Planning

The different advantageous embodiments provide a system comprising a number of computers, a graphical user interface, first program code stored on the computer, and second program code stored on the computer. The graphical user interface is executed by a computer in the number of computers. The computer is configured to run the first program code to define a mission using a number of mission elements. The computer is configured to run the second program code to generate instructions for a number of assets to execute the mission and monitor the number of assets during execution of the mission

Taxonomy of Trust-Relevant Failures and Mitigation Strategies

We develop a taxonomy that categorizes HRI failure types and their impact on trust to structure the broad range of knowledge contributions. We further identify research gaps in order to support fellow researchers in the development of trustworthy robots. Studying trust repair in HRI has only recently been given more interest and we propose a taxonomy of potential trust violations and suitable repair strategies to support researchers during the development of interaction scenarios. The taxonomy distinguishes four failure types: Design, System, Expectation, and User failures and outlines potential mitigation strategies. Based on these failures, strategies for autonomous failure detection and repair are presented, employing explanation, verification and validation techniques. Finally, a research agenda for HRI is outlined, discussing identified gaps related to the relation of failures and HR-trust