Morphogenetic swarm robotics. For self-organizing robotic systems, we want to construct the systems to be robust, flexible, independent, autonomous, which can adapt dynamically to the current conditions of their environments. In other words, they will exhibit some life-like intelligence, which are characterized by self-organization, self-configuration, self-adaptive, and self-repair. Distributed approaches seem desirable. However, it is well known that, for distributed approaches, it is difficult to predict the emerging behaviors from local interactions of individual agents, neither is it trivial to design rules for local interactions to generate a desired global behavior.
As we know, biological systems, from macroscopic swarm systems of social insects to microscopic cellular systems, can generate robust and complex emerging behaviors through relatively simple local interactions subject to various kinds of uncertainties. Embryonic development of multi-cellular organisms is governed by gene regulatory networks (GRNs), which are a collection of genes that interact with each other and with other chemicals in the cell. Inspired by the major principles of gene regulation and cellular interactions in multi-cellular development, in this project, we aim to replicate biological robustness by organizing complex behaviors from locally interacting individuals using morphogenetic approaches, and eventually provide a self-organizing framework for morphogenetic swarm robotic systems.
Some video demos for morphogenetic swarm robotics:
Morphogenetic Modular Robotics. Self-reconfigurable modular robots are autonomous robots with a variable morphology, where they are able to deliberately change their own shape by reorganizing the connectivity of their modules to adapt to new environments, perform new tasks, or recover from damage. Each module is an independent unit that is able to connect it to or disconnect it from other units to form various structure/patterns dynamically. Compared with conventional robotic systems, self-reconfigurable robots are potentially more robust and more adaptive. In this project, we mainly focus on developing a morphogenetic modular robot, CROSSCUBE, which is inspired by the embryonic development of multi-cellular organisms. In principle, CROSSCUBE is a two-layer modular robotic system. The first layer is a lattice-based locomotion design, which defines the basic mechanics of the modules in CROSSCUBE and the major constraints of the modules. The second layer consists of a high-level controller using a morphogenetic approach for CROSSCUBE to adaptively generate and form patterns.
Some video demos for morphogenetic modular robotics:
· From a cube to a mobile vehicle. Movie (4.8MB)
· Automatic change morphology to traverse a narrow tunnel. Movie (5.4MB)
· Self-reconfigure to adapt to different environmental situations. Movie (10MB)
Social Cognitive Robotics
Evolving Social Cognitive Agents System. In this project, we will develop a CIVS (Civilization-Inspired Vying Societies) system with evolving social cognitive agents, which is a novel evolutionary learning system loosely inspired by the history of human civilization. The system uses a bottom-up Alife (Artificial Life) approach with the goal of generating intelligent emergent behavior in agents and agent groups. CIVS proceeds from the idea that an agent will not develop complex, intelligent behavior in isolation; it must be part of a culture and social behaviors that support such development. Thus, we propose a simulated world where agents exist as part of societies, so that there are complex competition/cooperation relationships acting at several different scales.
Swarm Intelligence based Multi-Robot Coordination. One of the main challenges for multi-robot systems is to create intelligent agents that can adapt their behaviors based on interaction with the environment and other agents, become more proficient in their tasks over time, and adapt to new situations as they occur. Such ability is crucial for developing robots in human environments. Swarm robots are often observed to display many of the attributes which are typical in collective intelligent system in general including robustness, distributedness, adaptability, flexibility, and self-organization. Typical problem domain for the study of swarm-based robotic systems including foraging, box-pushing, aggregation and segregation, formation forming, cooperative mapping, soccer tournaments, site preparation, sorting, and collective construction. The main challenge of these control architecture are scalability due to limited sensing and communication capability of swarm robots. In this project, we are focusing on swarm intelligence based control approaches, which is an innovative computational and behavioral metaphor for solving distributed problems by taking its inspiration from the behaviors of social insects swarming, flocking, herding, and shoaling phenomena in vertebrates, where social insect colonies are able to build sophisticated structures and regulate the activities of millions of individuals by endowing each individual with simple rules based on local perception.
Distributed Reinforcement Learning for Multi-Agent Systems. Most available distributed multi-robot paradigms require expert behavior development with extensive tuning for each specific application, and lack of capability to generate new behaviors to adapt to dynamic environments. Therefore, it is desirable that robots can learn by themselves to accomplish the assigned tasks, instead of following some predefined rules or behaviors which may not be suitable for unknown environments. More and more researchers realized that automatic learning approaches have more potential for cooperative multi-robot system under unknown dynamic environments. However, multi-robot learning is a very challenging problem due to the dynamic interactions among the robots. More specifically, the very large state spaces may make it infeasible for a system to define the state. Due to the dynamic interaction among robots and with environment, it may bring some uncertainty in the reward assignment after each action of an agent. Usually in the real-world scenarios, sensor noise and uncertainty are unpreventable, which leads to more challenge in learning performance since these sensing information are the only feedback that an learning agent can rely on. Furthermore, it is very hard to merge information from different robot experiences due to its distributed characteristics. The goal of this project is to develop a decentralized cooperative multi-Q reinforcement learning algorithm, where each robot learns to make optimal decisions cooperatively with other robots based on its own Q value and the correlated Q values of other robots in a team.
Object Detection and Tracking. Visual interpretation of people and their behaviors is an important issue in many applications, such as service robots, surveillance systems, public security systems, virtual reality interfaces, and other government, commercial, and entertainment applications. The ability to recognize and track people is therefore an important visual problem. When the vision system is installed on a mobile platform, such as a mobile robot, the problem becomes more challenging since the background environments are dynamic and the people are not always visually isolated but are partially occluded by other objects. To tackle this issue, in this project, we are focusing on developing some robust adaptive-window-tracking algorithms with real-time performance. More specifically, we propose a new object tracking algorithm that embeds swarming particles into generic particle filter framework (PSO-PF) to achieve more robustness and flexibility. Firstly a group of particles associated with potential solutions are initialized in a high-dimensional space. Then particle swarm optimization (PSO) is used to drive particles flying. The object is tracked when the particles reach convergence. This PSO-based algorithm contains resample, similarity measure, and integration together such that the degeneracy problem of particle filter can be avoided. Furthermore, a multiple feature model is proposed for object description to enhance the tracking accuracy and efficiency. The proposed algorithm is independent with specific objects and can be used for any free-selected object tracking. Some experimental results demonstrate efficiency and robustness of the algorithm. Now we are focusing on developing a bio-inspired hierarchical neural network (HNN) model for complex pattern recognition and learning.
Behavior Learning and Recognition. Recognizing human behaviors in a video stream is critical in many applications, such as video surveillance, video indexing, video annotation, and video summarization. One major objective of automatic visual surveillance systems is to recognize normal patterns and detect abnormal behaviors. Automatic behavior detection is a challenging problem due to its large amount of video data to be analyzed and its real-time requirements. Furthermore, it is difficult to represent the behavior patterns based on video signal data, which is not always a one-to-one mapping. The goal of automatic behavior recognition is to learn a model that is capable of detecting unseen abnormal behavior patterns while recognizing expected normal behavior patterns. The primary source of difficulty in behavior recognition is that behaviors may vary dynamically in both shape and duration. Although it is unrealistic to obtain a large training data set for abnormal behaviors, it is possible to do so for normal behaviors, allowing the creation of well-estimated models for normal behaviors. A better approach for abnormal behaviors detection is to learn the model of normal behaviors, and then detect an abnormality based on its dissimilarity from normal behaviors. Therefore, in this project, we propose a self-organizing hidden Markov models (SO-HMMs) based framework to tackle this issue, which is an online learning method that can dynamically generate models for normal behavior recognition and abnormal behavior detection in an unsupervised manner. This SO-HMMs method is able to learn from the current data and generate new corresponding HMMs if a new data sequence cannot fit into any of the existing trained HMMs. This self-growing and self-organizing capability can provide more robustness and flexibility for dynamic scenarios where no prior knowledge about abnormal behaviors is given.
Real-Time Embedded Systems
SMARbot. The goal of this project is to construct a simple, easy to use, embedded system framework that can generally meet or exceed the specifications of various robots, while maintaining a great deal of flexibility. For the purposes of architecture design, details concerning sensors and the chassis are intentionally ambiguous. Instead of viewing the problem from the perspective of what the end system should do, we take an approach that is similar to the design of a development board. Ideally, a maximal amount of interfaces and commonly used components should be provided, with a minimal number of constraints that would prevent the addition of other components. In this project, we propose a generic modular embedded system architecture called SMARbot (Stevens Modular Autonomous Robot), which consists of a set of hardware and software modules that can be configured to construct various types of robot systems. These modules include a high performance microprocessor, a reconfigurable hardware component, wireless communication, and diverse sensor and actuator interfaces.
The proposed modular architecture of SMARbot consists of the following design features:
· A modular hardware and software architecture, where individual modules should be easily modifiable and reusable.
· A high performance microcontroller in a common and well supported architecture. Open source compilers and debugging tools are available.
· Reconfigurable hardware, well supported with software tools and documentation.
· Power-efficient wireless communication, where inexpensive and off the shelf module are ideal.
· A large number of interfaces for sensors. Sensors such as infrared proximity sensors, cameras, sonars, and bumper switches need to be connected while leaving room for future expansion.
· Low power. Mobile robots generally run off of small batteries. The system should not consume more than 1000mW.
· Low cost. Using high end microprocessors or FPGAs is out of the question. PCB design should fit the design rules of low cost, fast turn prototypes. Total cost should be under $600USD each in single quantity.
· Small Size. The complete system should be approximately 3” cubed.
· Low risk design. Proven designs and off the shelf hardware will be used whenever available.
· Ease of fabrication. A reasonably skilled user should be able to assemble the necessary components into a working system.
The prototype of the SMARbot.
Reconfigurable System-on-Chip Architecture Design for Real-Time Systems. Conventionally, all the tasks of a complex real-time system are implemented on a general processor in software only due to its great flexibility. A powerful processor is needed to guarantee that hard deadlines always are met, even for very rare conjunctions of events. Sometimes the sensors of a real time system are supposed to work in parallel and independently, it would be difficult to implement the parallel sensing in software. Furthermore, with multi-tasks, signals, events, it would be a challenging job for a software engineer to implement all of the tasks under real-time constraints especially with some computation-intensive information, say video images. On the other hand, the specific hardware can respond to external inputs more efficiently since the multiple hardware units can all operate in parallel, concurrent, and asynchronous, so that individual response times are much less variable and easier to guarantee, even as the number of tasks increases. Parallel hardware is not so affected by issues such as task swapping, scheduling, interrupt service, and critical sections, which complicate real-time software solutions. The expensive ASIC can fulfill the speed criteria. However, it is a complicated and expensive procedure if a slight change occurs. The newest FPGA technology allows a designer to use a single reconfigurable platform to instantiate both the processors and the required logic units, which directly leads to feasibility of the reconfigurable system-on-chip (rSoC) architecture. In this project, a unified agent-based hardware/software representation is proposed to simplify the hardware/software codesign procedure. Currently, we are developing dynamic task allocation and scheduling algorithms for this rSoC platform. This rSoC platform is desirable for those real-time systems which must be highly responsive to the dynamic environments.
Bioinformatics is the science of managing, mining, and interpreting information from biological sequences and structures. Previous applications of data mining and machine learning to bioinformatics include gene finding, protein function domain detection, function motif detection, disease diagnosis, disease prognosis, disease treatment optimization, protein and gene interaction network reconstruction, and data cleansing. Some clustering algorithms have been proposed for these applications, such as K-means algorithms, Genetic Algorithm (GA), Self-Organizing Maps (SOM), Support Vector Machine (SVM), artificial Neural networks, and decision trees. Even though most available classification methods are powerful in the information they produce, they may not be efficient or applicable to predict the outcomes of other clinic occasions. Furthermore, the choice of classification algorithms can give different classifiers, which may or may not be biologically significant. Therefore, in this project, we plan to develop a swarm intelligence based neural network technique for a generic analytical and prediction system, which can analyze and interpret the dataset, predict the results in a consistent way with known biological information about the clinic case at hand. Eventually, this system will automatically provide an optimal treatment strategy for specific clinic occasion.
These research projects are partially sponsored by NSF, DOD, Honda Research Institute Europe, Armament Research, Development and Engineering Center (ARDEC), and Stevens Faculty Startup Funding.
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