A cyberphysical avatar is a semi-autonomous robot that adjusts to an unstructured environment and performs physical tasks subject to critical timing constraints while under human supervision. This article first realizes a cyberphysical avatar that integrates three key technologies: body-compliant control, neuroevolution, and real-time constraints. Body-compliant control is essential for operator safety, because avatars perform cooperative tasks in close proximity to humans; neuroevolution (NEAT) enables “programming” avatars such that they can be used by non-experts for a large array of tasks, some unforeseen, in an unstructured environment; and real-time constraints are indispensable to provide predictable, bounded-time response in human-avatar interaction. Then, we present a study on the tradeoffs between three design parameters for robotic task systems that must incorporate at least three dimensions: (1) the amount of training effort for robot to perform the task, (2) the time available to complete the task when the command is given, and (3) the quality of the result of the performed task. A tradeoff study in this design space by using the imprecise computation as a framework is to perform a common robotic task, specifically, grasping of unknown objects. The results were validated with a real robot and contribute to the development of a systematic approach for designing robotic task systems that must function in environments like flexible manufacturing systems of the future.

We present a study on the tradeoffs between three design parameters for robotic task systems that function in partially unknown and unstructured environments, and under timing constraints. The design space of these robotic tasks must incorporate at least three dimensions: (1) the amount of training effort to teach the robot to perform the task, (2) the time available to complete the task from the point when the command is given to perform the task, and (3) the quality of the result from performing the task. This paper presents a tradeoff study in this design space for a common robotic task, specifically, grasping of unknown objects in unstructured environments. The imprecise computation model is used to provide a framework for this study. The results were validated with a real robot and contribute to the development of a systematic approach for designing robotic task systems that must function in environments like flexible manufacturing systems of the future.

Dynamic duty cycling, where a node's duty cycle is adapted according to observed context (e.g, local traffic, other nodes duty cycles), is commonly used by Wireless Sensor Networks (WSNs) to reduce energy consumption. Currently, duty cycles are assigned for every node in the network because it is assumed that all nodes are potential participants in transmissions. Receiver-based protocols, on the other hand, involve transmissions only among a subset of network nodes. Therefore, duty cycling may be limited to those nodes while the other nodes remain in an extended low-power state. In this work, we develop a duty cycle assignment algorithm for the nodes that participate in transmissions by employing two phases. First, nodes that participate in transmissions are selected and marked as “heads.” Second, by acquiring information to estimate future network traffic, the heads are programmed to be awake only during incoming transmissions. Simulation evaluations show that they greatly improve energy efficiency without sacrificing packet delivery ratio or introducing delay compared with traditional methods for duty cycle assignment.

Multi-robot patrol is a fundamental application of multi-robot systems. While much theoretical work exists providing an understanding of the optimal patrol strategy for teams of coordinated homogeneous robots, little work exists on building and evaluating the performance of such systems for real. In this paper, we evaluate the performance of multirobot patrol in a practical outdoor distributed robotic system, and evaluate the effect of different coordination schemes on the performance of the robotic team. The multi-robot patrol algorithms evaluated vary in the level of robot coordination: no coordination, loose coordination, and tight coordination. In addition, we evaluate versions of these algorithms that distribute state information-either individual state, or entire team state (global-view state). Our experiments show that while tight coordination is theoretically optimal, it is not practical in practice. Instead, uncoordinated patrol performs best in terms of average waypoint visitation frequency, though loosely coordinated patrol that shares only individual state performed best in terms of worst-case frequency. Both are significantly better than a loosely coordinated algorithm based on sharing global-view state. We respond to this discrepancy between theory and practice, caused primarily by robot heterogeneity, by extending the theory to account for such heterogeneity, and find that the new theory accounts for the empirical results.

Developing cyber-physical systems (CPS) is challenging because correctness depends on both logical and physical states, which are collectively difficult to observe. The developer often need to repeatedly rerun the system while observing its behavior and tweak the hardware and software until it meets minimum requirements. This process is tedious, error-prone, and lacks rigor. To address this, we propose BRACE, A framework that simplifies the process by enabling developers to correlate cyber (i.e., logical) and physical properties of the system via assertions. This paper presents our initial investigation into the requirements and semantics of such assertions, which we call CPS assertions. We discusses our experience implementing and using the framework with a mobile robot, and highlight key future research challenges.

Autonomous intersection management (AIM) is a new intersection control protocol that exploits the capabilities of autonomous vehicles to control traffic at intersections in a way better than traffic signals and stop signs. A key assumption of this protocol is that vehicles can always follow their trajectories. But mechanical failures can occur in real life, causing vehicles to deviate from their trajectories. A previous approach for handling mechanical failure was to prevent vehicles from entering the intersection after the failure. However, this approach cannot prevent collisions among vehicles already in the intersection or too close to stop because (1) the lack of coordination among vehicles can cause collisions during the execution of evasive actions; and (2) the intersection may not have enough room for evasive actions. In this paper, we propose a preemptive approach that pre-computes evasion plans for several common types of mechanical failures before vehicles enter an intersection. This preemptive approach is necessary because there are situations in which vehicles cannot evade without pre-allocation of space for evasion. We present a modified AIM protocol and demonstrate the effectiveness of evasion plan execution on a miniature autonomous intersection testbed.

This article presents Agilla, a mobile agent middleware designed to support self-adaptive applications in wireless sensor networks. Agilla provides a programming model in which applications consist of evolving communities of agents that share a wireless sensor network. Coordination among the agents and access to physical resources are supported by a tuple space abstraction. Agents can dynamically enter and exit a network and can autonomously clone and migrate themselves in response to environmental changes. Agilla's ability to support self-adaptive applications in wireless sensor networks has been demonstrated in the context of several applications, including fire detection and tracking, monitoring cargo containers, and robot navigation. Agilla, the first mobile agent system to operate in resource-constrained wireless sensor platforms, was implemented on top of TinyOS. Agilla's feasibility and efficiency was demonstrated by experimental evaluation on two physical testbeds consisting of Mica2 and TelosB nodes.

No abstract available.

No abstract available.

Wireless sensor networks (WSNs) are difficult to program and usually run statically-installed software limiting its flexibility. To address this, we developed Agilla, a new middleware that increases network flexibility while simplifying application development. An Agilla network is deployed with no pre-installed application. Instead, users inject mobile agents that spread across nodes performing application-specific tasks. Each agent is autonomous, allowing multiple applications to share a network. Programming is simplified by allowing programmers to create agents using a high-level language. Linda-like tuple spaces are used for inter-agent communication and context discovery. This preserves each agent’s autonomy while providing a rich infrastructure for building complex applications, and marks the first time mobile agents and tuple spaces are used in a unified framework for WSNs. Our efforts resulted in an implementation for MICA2 motes and the development of several applications. The implementation consumes a mere 41.6KB of code and 3.59KB of data memory. An agent can migrate 5 hops in less than 1.1 seconds with 92% reliability. In this paper, we present Agilla and provide a detailed evaluation of its implementation, an empirical study of its overhead, and a case study demonstrating its use.

This paper presents MobiQuery, a spatiotemporal query service that allows mobile users to periodically gather information from their surrounding areas through a wireless sensor network. A key advantage of MobiQuery lies in its capability to meet stringent spatiotemporal performance constraints crucial to many applications. These constraints include query latency, data freshness and fidelity, and changing query areas due to user mobility. A novel just-in-time prefetching algorithm enables MobiQuery to maintain robust spatiotemporal guarantees even when nodes operate under extremely low duty cycles. Furthermore, it significantly reduces the storage cost and network contention caused by continuous queries from mobile users. We validate our approach through both theoretical analysis and simulation results under a range of realistic settings.

Agilla is a mobile agent middleware that facilitates the rapid deployment of adaptive applications in wireless sensor networks (WSNs). Agilla allows users to create and inject special programs called mobile agents that coordinate through local tuple spaces, and migrate across the WSN performing application-specific tasks. This fluidity of code and state has the potential to transform a WSN into a shared, general-purpose computing platform capable of running several autonomous applications at a time, allowing us to harness its full potential. We have implemented and evaluated a fire tracking application to determine how well Agilla achieves its goals. Fire is modeled by agents that gradually spread throughout the network, engulfing nodes by inserting fire tuples into their local tuple spaces. Fire tracker agents are then used to form a perimeter around the fire. Using Agilla, we were able to rapidly create and deploy 47 byte fire agents, and 100 byte tracker agents on a WSN consisting of 26 MICA2 motes. Our experiments show that the tracker agents can form an 8-node perimeter around a burning node within 6.5 seconds and that it can adapt to a fire spreading at a rate of 7 seconds per hop. We also present the lessons learned about the adequacy of Agilla's primitives, and regarding the efficiency, reliability, and adaptivity of mobile agents in a WSN.

No abstract available.

No abstract available.