25 May 2016 Human-machine teaming for effective estimation and path planning
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Abstract
While traditional sensors provide accurate measurements of quantifiable information, humans provide better qualitative information and holistic assessments. Sensor fusion approaches that team humans and machines can take advantage of the benefits provided by each while mitigating the shortcomings. These two sensor sources can be fused together using Bayesian fusion, which assumes that there is a method of generating a probabilistic representation of the sensor measurement. This general framework of fusing estimates can also be applied to joint human-machine decision making. In the simple case, binary decisions can be fused by using a probability of taking an action versus inaction from each decision-making source. These are fused together to arrive at a final probability of taking an action, which would be taken if above a specified threshold. In the case of path planning, rather than binary decisions being fused, complex decisions can be fused by allowing the human and machine to interact with each other. For example, the human can draw a suggested path while the machine planning algorithm can refine it to avoid obstacles and remain dynamically feasible. Similarly, the human can revise a suggested path to achieve secondary goals not encoded in the algorithm such as avoiding dangerous areas in the environment.
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Michael J. McCourt, Michael J. McCourt, Siddhartha S. Mehta, Siddhartha S. Mehta, Emily A. Doucette, Emily A. Doucette, J. Willard Curtis, J. Willard Curtis, } "Human-machine teaming for effective estimation and path planning", Proc. SPIE 9836, Micro- and Nanotechnology Sensors, Systems, and Applications VIII, 98361W (25 May 2016); doi: 10.1117/12.2224121; https://doi.org/10.1117/12.2224121
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