Summary form only given. We discuss a novel framework for integrated sensing and resource management in a distributed intelligent sensor network with sensor nodes whose useful active lifetime is significantly shorter than the required lifetime of the sensor system. Past sensor network research has focused on security and communication; but largely ignored the overall dynamic resource management issue of such distributed systems. Our contribution is in integrated control optimization and resource management algorithms to ensure proper functioning of distributed sensors in extremely limited bandwidth, power, and storage. In this paper, we present a novel genetic algorithm for real-time system control and resource management that handles multiple conflicting objectives and constraints in the distributed system. Our framework is suitable for dynamic environments where the desired system performance and resource usage changes dynamically while being constrained by limited amount of resources.

In this paper, we evaluate the performance of two candidate formulations for distributed motion planning of robot collectives within an Artificial Potential Field (APF) framework. We exploit the parallel between the formulation of motion planning for group of robots coupled by constraints and the forward dynamics simulation of constrained multibody systems to develop the candidate approaches. We compare and contrast these approaches on the basis of ease of formulation, distribution of computation and overall computational accuracy. Traditionally penalty formulations have enjoyed a prominent position in motion planning of robot collectives due to their ease of formulation, decentralization and scalability. However, the instabilities introduced in the form of “formulation stiffness” at the algorithm development stage have the potential to hinder the subsequent control. Representative results from the distributed motion planning for a group of 3 point-mass robots moving in formation to a desired target location are used to highlight the differences.

In this paper, we present a geometric method for motion planning for a fleet of differentially-driven wheeled mobile robots moving in formation, which explicitly takes into account their nonholonomic constraints. The relative position within the formation induces different motion plans for each individual wheeled mobile robot (WMR). We can quantitatively evaluate the performance of such induced motion plans using suitable metrics defined for the motions of each WMR. These performance metrics in cumulative form or individual form are used to optimize the overall formation (i.e., their relative positions) for performing a given task. The approach is well suited for online implementation and is demonstrated using case studies.

In this paper, we examine creation of a decentralized kinematic control scheme for a composite system of two (or more) wheeled mobile manipulators that can team up to cooperatively transport a common payload. Each mobile manipulator module consists of a differentially driven wheeled mobile robot (WMR) with a mounted planar two-degree-of-freedom (d.o.f) manipulator. A composite multi-degree-of-freedom system is formed when a payload is placed at the end effectors of multiple such modules with significant advantages. However, the nonholonomic/holonomic constraints and active/passive components within the composite vehicle need careful treatment for realizing the payload transport task. Hence, we first verify that arbitrary desired end-effector motions can be accommodated, within the feasible motion distributions of the articulations and the wheeled base. Then, we develop motion-plans by which this desired end-effector motion could be actively realized, using only the limited active motion-distribution of the differentially driven wheels. Finally, we deploy this in the form of a two-level hierarchical control framework, with an upper-level planning of the steerable active vector-fields and a lower-level posture stabilization control of the individual WMRs. Preliminary experimental results from the decentralized-control implementation for a two-module composite vehicle are also presented.

We discuss a novel control methodology for power management in heterogeneous distributed sensor networks. We present an event based control optimization formulation of the resource management problem and discuss a method to adaptively change the desired system performance of the sensor network in response to events. This functionality is critical in field-deployable sensor networks where continuous operation is expensive and system adaptation is critical for extended operation in the face of dynamic external events. We show results on various synthetic heterogeneous sensor networks where only partially accurate information about the sensing system is available and illustrate the efficacy of the control algorithm in handling such incorrect models with a negligible increase in transmission of the optimal control settings. We show that the run-time performance of the control algorithm scales quadratically with increasing number of sensors.