One of the major problems in sensor fusion is that sensors frequently provide spurious observations which are difficult to predict and model. The spurious measurements from sensors must be identified and eliminated since their incorporation in the fusion pool might lead to inaccurate estimation. This paper presents a unified sensor fusion strategy based on a modified Bayesian approach that can automatically identify the inconsistency in sensor measurements so that the spurious measurements can be eliminated from the data fusion process. The proposed method adds a term to the commonly used Bayesian formulation. This term is an estimate of the probability that the data is not spurious, based upon the measured data and the unknown value of the true state. In fusing two measurements, it has the effect of increasing the variance of the posterior distribution when measurement from one of the sensors is inconsistent with respect to the other. The increase or decrease in variance can be estimated using the information theoretic measure "entropy." The proposed strategy was verified with the help of extensive computations performed on simulated data from three sensors. A comparison was made between two different fusion schemes: centralized fusion in which data obtained from all sensors were fused simultaneously, and a decentralized or sequential Bayesian scheme that proved useful for identifying and eliminating spurious data from the fusion process. The simulations verified that the proposed strategy was able to identify spurious sensor measurements and eliminate them from the fusion process, thus leading to a better overall estimate of the true state. The proposed strategy was also validated with the help of experiments performed using stereo vision cameras, one infrared proximity sensor, and one laser proximity sensor. The information from these three sensing sources was fused to obtain an occupancy profile of the robotic workspace.Index Terms-Bayesian approach, decentralized fusion, sensor fusion, sequential fusion, spurious data.
This paper presents the formulation and application of a strategy for the determination of an optimal trajectory for a multiple robotic configuration. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used as the optimization techniques and results obtained from them compared. First, the motivation for multiple robot control and the current state-of-art in the field of cooperating robots are briefly given. This is followed by a discussion of energy minimization techniques in the context of robotics, and finally, the principles of using genetic algorithms and simulated annealing as an optimization tool are included. The initial and final positions of the end effector are specified. Two cases, one of a single manipulator, and the other of two cooperating manipulators carrying a common payload illustrate the proposed approach. The GA and SA techniques identify the optimal trajectory based on minimum joint torque requirements. The simulations performed for both the cases show that although both the methods converge to the global minimum, the SA converges to solution faster than the GA. r
The Army Research Office (ARO) has been supporting projects focusing on basic research in the area of smart materials and adaptive structures over recent years. A major emphasis of the ARO's Structures and Dynamics Program has been on the theoretical, computational, and experimental analysis of smart structures and structural dynamics, damping, active control, and health monitoring as applied to rotorcraft, electromagnetic antenna structures, missiles, land vehicles, and weapon systems. The variety of research projects supported by the program have been primarily directed towards improving the ability to predict, control, and optimize the dynamic response of complex, multi-body deformable structures. The projects in the field of smart materials and adaptive structures have included multi-disciplinary research conducted by teams of several faculty members as well as research performed by individual investigators.This paper begins with a brief discussion of smart or active materials, i.e. materials having capabilities of sensing changes from the surrounding environment and actively responding to those inputs in an effective manner. Integrating these materials in structures makes them 'smart', i.e. it provides them with the capability to respond to the external stimuli to compensate for undesired effects and/or to enhance the desired effects. The terms 'active', 'smart', 'adaptive', and 'intelligent' are frequently used interchangeably in this context. This discussion is followed by illustrations from several current ARO-sponsored research projects related to smart materials and adaptive structures. A summary of significant results based upon these investigations is given next. Finally, directions of potential future research in the smart materials and adaptive structures area are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.