
This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.
13 pages; double column; 6 figures; submitted to IEEE Transactions on Robotics and Automation
FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), 68W15, 49N90, 70E60, Optimization and Control (math.OC), 93B52, 51M20, FOS: Mathematics, 68W15; 49N90; 93B52; 51M20; 70E60, Mathematics - Optimization and Control
FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), 68W15, 49N90, 70E60, Optimization and Control (math.OC), 93B52, 51M20, FOS: Mathematics, 68W15; 49N90; 93B52; 51M20; 70E60, Mathematics - Optimization and Control
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2K | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 0.1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 0.01% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 0.1% |
