<div>We describe a new task called The Amazing Race: Robot Edition. In this task, the robot is placed in a real, unknown environment, without a map, and asked to find a designated location. It will need to explore its surroundings, find and approach people, engage them in a dialogue to obtain directions to the goal, and follow those directions to the hallway with the goal. We describe and implement a variety of robotic behaviors that performs each of these functions. We test these in the real world in test environments that were distinct from the training environments where we developed our methods and trained our models. Additionally, these test environments were completely unmodified and reflect the state of the real world.</div><div>First, we describe how our robotic system solves this problem where the environment is constrained to a single floor or a single building. We demonstrate that we are able to find a goal location in never-before-seen environments. Next, we describe a machine-learned approach to the dialogue and components of our system to make it more robust to the diversity and noisiness of navigational instructions someone may provide.</div>

Disasters especially from natural phenomena are inevitable. The affected areas recover from the aftermath of a natural disaster with the support from various agents participating in humanitarian operations. There are several domains of the operation, and distributing relief aids is one. For distribution, satisfying the demand for relief aid is important since the condition of the environment is unfavorable to affected people and resources needed for the victim’s life are scarce. However, it becomes problematic when the logistic agents believed to be work properly fail to deliver the emergency goods because of the capacity loss induced from the environment after disasters. This study was proposed to address the problem of logistic agents’ unexpected incapacity which hinders scheduled distribution. The decrease in a logistic agent’s supply capability delays<br>achieving the goal of supplying required relief goods to the affected people which further endangers them. Regarding the stated problem, this study explored the importance of<br>setting the profile of logistic agents that can survive for certain duration of times. Therefore, this research defines the “survivability” and the profile of logistic agents for surviving the last mile distribution through agent based modeling and simulation. Through simulations, this study uncovered that the logistic exercise could gain survivability with the certain number and organization of logistic agents. Proper formation of organization establish the logistics’ survivability, but excessive size can threaten the survivability.

<div>Path planning is the generation of paths for the robots to navigate based on some constraints. Coverage path planning is where the robots needs to cover an entire work space for various applications like sensing, inspection and so on. Though there are numerous works on 2D coverage and also coverage using a single robot, the works on 3D coverage and multi-agents are very limited. This thesis makes several contributions to multi-agent path planning for 3D structures.</div><div><br></div><div>Motivated by the inspection of 3D structures, especially airplanes, we present a 3D coverage path planning algorithm for a multi-UAV system. We propose a unified method, where the viewpoints selection and path generation are done simultaneously for multiple UAVs. The approach is scalable in terms of number of UAVs and is also robust to models with variations in geometry. The proposed method also distributes the task uniformly amongst the multiple UAVs involved and hence making the best use of the robotics team. The uniform task distribution is an integral part of the path planner. Various performance measures of the paths generated in terms of coverage, path length and time also has been presented. </div>

The thesis introduces an innovative UAV detection system. The commercial UAV market is booming. Meanwhile, the risks and threats from improper UAV usages are also booming. CUAS is to protect the public and facilities. The problem is a lack of an intelligent platform which can adapt many sensors for UAV detection. The hypothesis is that, the system can track the UAV’s movement by applying the multi-agent system (MAS) to UAV route track. The experiment proves that the multi-agent system benefits for the UAV track. <br>

<div>Enhancing the capability to control a tremendous range of physical actuators and sensors, combined with wireless technology and the Internet of Things (IoT), apparel technologies play a significant role in supporting safe, comfortable and healthy living, observing each customer’s conditions. Since apparel technologies have advanced to enable humans to work as a team with the clothing they wear, the interaction between a human and apparel is further enhanced with the introduction of sensors, wireless network, and artificially intelligent techniques. A variety of wearable technologies have been developed and spread to meet the needs of customers, however, some wearable devices are considered as non-practical tech-oriented, not consumer-oriented.</div><div>The purpose of this research is to develop an apparel system which integrates intelligent autonomous agents, human-based sensors, wireless network protocol, mobile application management system and a zipper robot. This research is an augmentation to the existing research and literature, which are limited to the zipping and unzipping process without much built in intelligence. This research is to face the challenges of the elderly and people with self-care difficulties. The intent is to provide a scientific path for intelligent zipper robot systems with potential, not only to help people, but also to be commercialized.</div><div>The research develops an intelligent system to control of zippers fixed on garments, based on the profile and desire of the human. The theoretical and practical elements of developing small, integrated, intelligent zipper robots that interact with an application by using a lightweight MQTT protocol for application in the daily lives of diverse populations of people with physical challenges. The system functions as intelligent automatized garment to ensure users could positively utilize a zipper robot device to assist in putting on garments which also makes them feel comfortable wearing and interacting with the system. This research is an approach towards the “future of fashion”, and the goal is to incentivize and inspire others to develop new instances of wearable robots and sensors that help people with specific needs to live a better life.</div>

Networked telerobots are operated by humans through remote interactions and have found applications in unstructured environments, such as outer space, underwater, telesurgery, manufacturing etc. In precision agricultural robotics, target monitoring, recognition and detection is a complex task, requiring expertise, hence more efficiently performed by collaborative human-robot systems. A HUB is an online portal, a platform to create and share scientific and advanced computing tools. HUB-CI is a similar tool developed by PRISM center at Purdue University to enable cyber-augmented collaborative interactions over cyber-supported complex systems. Unlike previous HUBs, HUB-CI enables both physical and virtual collaboration between several groups of human users along with relevant cyber-physical agents. This research, sponsored in part by the Binational Agricultural Research and Development Fund (BARD), implements the HUB-CI model to improve the Collaborative Intelligence (CI) of an agricultural telerobotic system for early detection of anomalies in pepper plants grown in greenhouses. Specific CI tools developed for this purpose include: (1) Spectral image segmentation for detecting and mapping to anomalies in growing pepper plants; (2) Workflow/task administration protocols for managing/coordinating interactions between software, hardware, and human agents, engaged in the monitoring and detection, which would reliably lead to precise, responsive mitigation. These CI tools aim to minimize interactions’ conflicts and errors that may impede detection effectiveness, thus reducing crops quality. Simulated experiments performed show that planned and optimized collaborative interactions with HUB-CI (as opposed to ad-hoc interactions) yield significantly fewer errors and better detection by improving the system efficiency by between 210% to 255%. The anomaly detection method was tested on the spectral image data available in terms of number of anomalous pixels for healthy plants, and plants with stresses providing statistically significant results between the different classifications of plant health using ANOVA tests (P-value = 0). Hence, it improves system productivity by leveraging collaboration and learning based tools for precise monitoring for healthy growth of pepper plants in greenhouses.

<p>By using flexible and stretchable materials in place of fixed components, soft robots can materially adapt or change to their environment, providing built-in safeties for robotic operation around humans or fragile, delicate objects. And yet, building a robot out of only soft and flexible materials can be a significant challenge depending on the tasks that the robot needs to perform, for example if the robot were to need to exert higher forces (even temporarily) or self-report its current state (as it deforms unexpectedly around external objects). Thus, the appeal of multifunctional materials for soft robots, wherein the materials used to build the body of the robot also provide actuation, sensing, or even simply electrical connections, all while maintaining the original vision of environmental adaptability or safe interactions. Multifunctional material systems are explored throughout the body of this dissertation in three ways: (1) Sensor integration into high strain actuators for state estimation and closed-loop control. (2) Simplified control of multifunctional material systems by enabling multiple functions through a single input stimulus (<i>i.e.</i>, only requiring one source of input power). (3) Presenting a solution for the open challenge of controlling both well established and newly developed thermally-responsive soft robotic materials through an on-body, high strain, uniform, Joule-heating energy source. Notably, these explorations are not isolated from each other as, for example, work towards creating a new material for thermal control also facilitated embedded sensory feedback. The work presented in this dissertation paves a way forward for multifunctional material integration, towards the end-goal of full-functioning soft robots, as well as (more broadly) design methodologies for other safety-forward or adaptability-forward technologies.</p>

The onset of Industry 4.0 has provided considerable benefits to Intelligent Cyber-Physical Systems (ICPS), with technologies such as internet of things, wireless sensing, cognitive computing and artificial intelligence to improve automation and control. However, with increasing automation, the “human” element in industrial systems is often overlooked for the sake of standardization. While automation aims to redirect the workload of human to standardized and programmable entities, humans possess qualities such as cognitive awareness, perception and intuition which cannot be automated (or programmatically replicated) but can provide automated systems with much needed robustness and sustainability, especially in unstructured and dynamic environments. Incorporating tangible human skills and knowledge within industrial environments is an essential function of “Human-in-the-loop” (HITL) Systems, a term for systems powerfully augmented by different qualities of human agents. The primary challenge, however, lies in the realistic modelling and application of these qualities; an accurate human model must be developed, integrated and tested within different cyber-physical workflows to 1) validate the assumed advantages, investments and 2) ensure optimized collaboration between entities. Agricultural Robotic Systems (ARS) are an example of such cyber-physical systems (CPS) which, in order to reduce reliance on traditional human-intensive approaches, leverage sensor networks, autonomous robotics and vision systems and for the early detection of diseases in greenhouse plants. Complete elimination of humans from such environments can prove sub-optimal given that greenhouses present a host of dynamic conditions and interactions which cannot be explicitly defined or managed automatically. Supported by efficient algorithms for sampling, routing and search, HITL augmentation into ARS can provide improved detection capabilities, system performance and stability, while also reducing the workload of humans as compared to traditional methods. This research thus studies the modelling and integration of humans into the loop of ARS, using simulation techniques and employing intelligent protocols for optimized interactions. Human qualities are modelled in human “classes” within an event-based, discrete time simulation developed in Python. A logic controller based on collaborative intelligence (HUB-CI) efficiently dictates workflow logic, owing to the multi-agent and multi-algorithm nature of the system. Two integration hierarchies are simulated to study different types of integrations of HITL: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks and classification accuracy are measured and compared for different collaboration protocols within each hierarchy, to verify the impact of chosen sampling and search algorithms. The experiments performed show the statistically significant advantages of HUB-CI based protocol over traditional protocols in terms of collaborative task performance and disease detectability, thus justifying added investment due to the inclusion of HITL. The results also discuss the competitive factors between both integrations, laying out the relative advantages and disadvantages and the scope for further research. Improving human modelling and expanding the range of human activities within the loop can help to improve the practicality and accuracy of the simulation in replicating an HITL-ARS. Finally, the research also discusses the development of a user-interface software based on ARS methodologies to test the system in the real-world.<br>

<div>There are millions of individuals in the world who currently experience limited mobility as a result of aging, stroke, injuries to the brain or spinal cord, and certain neurological diseases. Robotic Assistive Devices (RADs) have shown superiority in helping people with limited mobility by providing physical movement assistance. However, RADs currently existing on the market for people with limited mobility are still far from intelligent.</div><div><br></div><div>Learning control strategies are developed in this study to make a Cable-Driven Assistive Device (CDAD) intelligent in assisting a human joint (e.g., a knee joint, an ankle joint, or a wrist joint). CDADs are a type of RADs designed based on Cable-Driven Parallel Robots (CDPRs). A PID–FNN control strategy and DDPG-based strategies are proposed to allow a CDAD to learn physical human-robot interactions when controlling the pose of the human joint. Both pose-tracking and trajectory-tracking tasks are designed to evaluate the PID–FNN control strategy and the DDPG-based strategies through simulations. Simulations are conducted in the Gazebo simulator using an example CDAD with three degrees of freedom and four cables. Simulation results show that the proposed PID–FNN control strategy and DDPG-based strategies work in controlling a CDAD with proper learning.</div>

<div>Self-organization in a multi-robot system is a spontaneous process where some form of overall order arises from local interactions between robots in an initially disordered system. Cooperative coordination strategies for self-organization promote teamwork to complete a task while increasing the total utility of the system. In this dissertation, we apply prosocial behavioral concepts such as altruism and cooperation in multi-robot systems and investigate their effects on overall system performance on given tasks. We stress the significance of this research in long-term applications involving minimal to no human supervision, where self-sustainability of the multi-robot group is of utmost importance for the success of the mission at hand and system re-usability in the future.</div><div><br></div><div>For part of the research, we take bio-inspiration of cooperation from the huddling behavior of Emperor Penguins in the Antarctic which allows them to share body heat and survive one of the harshest environments on Earth as a group. A cyclic energy sharing concept is proposed for a convoying structured multi-robot group inspired from penguin movement dynamics in a huddle with carefully placed induction coils to facilitate directional energy sharing with neighbors and a position shuffling algorithm, allowing long-term survival of the convoy as a group in the field. Simulation results validate that the cyclic process allows individuals an equal opportunity to be at the center of the group identified as the most energy conserving position, and as a result robot groups were able to travel over 4 times the distance during convoying with the proposed method without any robot failing as opposed to without the shuffling and energy sharing process. </div><div><br></div><div>An artificial potential based Adaptive Inter-agent Spacing (AIS) control law is also proposed for efficient energy distribution in an unstructured multi-robot group aimed at long-term survivability goals in the field. By design, as an altruistic behavior higher energy bearing robots are dispersed throughout the group based on their individual energy levels to counter skewed initial distributions for faster group energy equilibrium attainment. Inspired by multi-huddle merging and splitting behavior of Emperor Penguins, a clustering and sequential merging based systematic energy equilibrium attainment method is also proposed as a supplement to the AIS controller. The proposed system ensures that high energy bearing agents are not over crowded by low energy bearing agents. The AIS controller proposed for the unstructured energy sharing and distribution process yielded 55%, 42%, 23% and 33% performance improvements in equilibrium attainment convergence time for skewed, bi-modal, normal and random initial agent resource level distributions respectively on a 2D plane using the proposed energy distribution method over the control method of no adaptive spacing. Scalability analysis for both energy sharing concepts confirmed their application with consistently improved performances different sized groups of robots. Applicability of the AIS controller as a generalized resource distribution method under certain constraints is also discussed to establish its significance in various multi-robot applications.</div><div><br></div><div>A concept of group based survival from damaging directional external stimuli is also adapted from the Emperor Penguin huddling phenomenon where individuals on the damaging stimuli side continuously relocate to the leeward side of the group following the group boundary using Gaussian Processes Machine Learning based global health-loss rate minima estimations in a distributed manner. The method relies on cooperation from all robots where individuals take turns being sheltered by the group from the damaging external stimuli. The distributed global health loss rate minima estimation allowed the development of two settling conditions. The global health loss rate minima settling method yielded 12.6%, 5.3%, 16.7% and 14.2% improvement in average robot health over the control case of no relocation, while an optimized health loss rate minima settling method further improved on the global health loss rate settling method by 3.9%, 1.9%, 1.7% and 0.6% for robot group sizes 26, 35, 70 and 107 respectively.</div><div><br></div><div>As a direct application case study of collaboration in multi-robot systems, a distributed shape formation strategy is proposed where robots act as beacons to help neighbors settle in a prescribed formation by local signaling. The process is completely distributed in nature and does not require any external control due to the cooperation between robots. Beacon robots looking for a robot to settle as a neighbor and continue the shape formation process, generates a surface gradient throughout the formed shape that allow robots to determine the direction of the structure forming frontier along the dynamically changing structure surface and eventually reach the closest beacon. Simulation experiments validate complex shape formation in 2D and 3D using the proposed method. The importance of group collaboration is emphasized in this case study without which the shape formation process would not be possible, without a centralized control scheme directing individual agents to specific positions in the structure. </div><div> </div><div>As the final application case study, a collaborative multi-agent transportation strategy is proposed for unknown objects with irregular shape and uneven weight distribution. Although, the proposed system is robust to single robot object transportation, the proposed methodology of transport is focused on robots regulating their effort while pushing objects from an identified pushing location hoping other robots support the object moment on the other end of the center of mass to prevent unintended rotation and create an efficient path of the object to the goal. The design of the object transportation strategy takes cooperation cues from human behaviors when coordinating pushing of heavy objects from two ends. Collaboration is achieved when pushing agents can regulate their effort with one another to maintain an efficient path for the object towards the set goal. Numerous experiments of pushing simple shapes such as disks and rectangular boxes and complex arbitrary shapes with increasing number of robots validate the significance and effectiveness of the proposed method. Detailed robustness studies of changing weight of objects during transportation portrayed the importance of cooperation in multi-agent systems in countering unintended drift effects of the object and maintain a steady efficient path to the goal. </div><div><br></div><div>Each case study is presented independent of one another with the Penguin huddling based self-organizations in response to internal and external stimuli focused on fundamental self-organization methods, and the structure formation and object transportation strategies focused on cooperation in specific applications. All case studies are validated by relevant simulation and experiments to establish the effectiveness of altruistic and cooperative behaviors in multi-robot systems.</div>