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In many automated motion planning systems, vehicles are tasked with tracking a reference path or trajectory that is safe by design. However, due to various uncertainties, real vehicles may deviate from such references, potentially leading to collisions. This paper presents rigorous reachable set bounding methods for rapidly enclosing the set of possible deviations under uncertainty, which is critical information for online safety verification. The proposed approach applies recent advances in the theory of differential inequalities that exploit redundant model equations to achieve sharp bounds using only simple interval calculations. These methods have been shown to produce very sharp bounds at low cost for nonlinear systems in other application domains, but they rely on problem-specific insights to identify appropriate redundant equations, which makes them difficult to generalize and automate. Here, we demonstrate the application of these methods to tracking problems for the first time using three representative case studies. We find that defining redundant equations in terms of Lyapunov-like functions is particularly effective. The results show that this technique can produce effective bounds with computational times that are orders of magnitude less than the planned time horizon, making this a promising approach for online safety verification. This performance, however, comes at the cost of low generalizability, specifically due to the need for problem-specific insights and advantageous problem structure, such as the existence of appropriate Lyapunov-like functions.
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Dynamic grouping of cooperating vehicles using a receding horizon controller for ground target search and track missions
Teams of unmanned vehicles are capable of accomplishing a wide variety of mission objectives, such as searching for and tracking targets. In this paper, a receding horizon control is utilized with information based reward measures to accomplish these two competing mission objectives. This approach for cooperatively searching and tracking has proven to be effective in past work. However, it is not generally scalable for large numbers of vehicles due to the computational expense required when generating joint path decisions. This paper proposes a method to dynamically group vehicles with neighbors that have intersecting decision spaces, thus reducing computational cost while still maintaining reasonable performance. Each vehicle also decides its ideal event horizon based upon inferred knowledge of the operational environment, further reducing cost.
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- Award ID(s):
- 1650547
- PAR ID:
- 10053416
- Date Published:
- Journal Name:
- Control Technology and Applications (CCTA), 2017 IEEE Conference
- Page Range / eLocation ID:
- 1855 to 1860
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCTA.2017.8062726
