no code implementations • 31 Oct 2023 • Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano
For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning.
no code implementations • 5 Mar 2022 • Michael Hibbard, Abraham P. Vinod, Jesse Quattrociocchi, Ufuk Topcu
We introduce the Safely motion planner, a receding-horizon control framework, that simultaneously synthesizes both a trajectory for the robot to follow as well as a sensor selection strategy that prescribes trajectory-relevant obstacles to measure at each time step while respecting the sensing constraints of the robot.
no code implementations • 28 Feb 2021 • Yagiz Savas, Abolfazl Hashemi, Abraham P. Vinod, Brian M. Sadler, Ufuk Topcu
In such a setting, we develop a periodic transmission strategy, i. e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission.
no code implementations • 11 Nov 2020 • Franck Djeumou, Abraham P. Vinod, Eric Goubault, Sylvie Putot, Ufuk Topcu
Besides, $\texttt{DaTaControl}$ achieves near-optimal control and is suitable for real-time control of such systems.
no code implementations • 27 Sep 2020 • Franck Djeumou, Abraham P. Vinod, Eric Goubault, Sylvie Putot, Ufuk Topcu
We investigate the problem of data-driven, on-the-fly control of systems with unknown nonlinear dynamics where data from only a single finite-horizon trajectory and possibly side information on the dynamics are available.
no code implementations • 31 Jul 2020 • Suda Bharadwaj, Abraham P. Vinod, Rayna Dimitrova, Ufuk Topcu
We consider the problem of optimal reactive synthesis - compute a strategy that satisfies a mission specification in a dynamic environment, and optimizes a performance metric.
1 code implementation • 11 Oct 2018 • Abraham P. Vinod, Meeko M. K. Oishi
Of special interest is the stochastic reach set, the set of all initial states from which it is possible to stay in the target tube with a probability above a desired threshold.
Optimization and Control Systems and Control
1 code implementation • 19 Mar 2018 • Abraham P. Vinod, Meeko M. K. Oishi
We present theory and algorithms for the computation of probability-weighted "keep-out" sets to assure probabilistically safe navigation in the presence of multiple rigid body obstacles with stochastic dynamics.
Systems and Control Optimization and Control