Category Archives: Task Scheduling

Scheduling of communications between several nodes for better achieving real-time constraints in a distributed control system, and also a very detailed dynamical model of a wheeled vehicle

Naim Bajcinca, Wireless cars: A cyber-physical approach to vehicle dynamics control, Mechatronics, Volume 30, September 2015, Pages 261-274, ISSN 0957-4158, DOI: 10.1016/j.mechatronics.2015.04.016.

A non-conventional drive-by-wireless technology for guidance and control of a redundantly actuated electric car supported by an on-board wireless network of sensors, actuators and control units is proposed in this article. Several optimization-based distributed feedforward control schemes are developed for such powertrain infrastructures. In view of the limitations of the commercial off-the-shelf wireless communication technologies and the harshness of the in-vehicle environments, a pressing design and implementation aspect, in addition to the robustness against information loss, refers to fulfilling the hard real-time computational requirements. In this work, we address such problems by introducing several distributed event-based control schemes in conjunction with adaptive scheduling at the protocol level. Hereby we obtain a simple tuning mechanism to compromise between the outcome accuracy and computation efficiency (i.e., communication traffic intensity). Using simulative evaluations, we demonstrate the viability of the proposed algorithms and illustrate the impact of external interferences in an IEEE 802.15.4 based wireless communication solution.

The problem of monitoring events that can only be predicted stochastically, applied to mobile sensors for monitoring

Jingjin Yu; Karaman, S.; Rus, D., Persistent Monitoring of Events With Stochastic Arrivals at Multiple Stations, Robotics, IEEE Transactions on , vol.31, no.3, pp.521,535, June 2015, DOI: 10.1109/TRO.2015.2409453.

This paper introduces a new mobile sensor scheduling problem involving a single robot tasked to monitor several events of interest that are occurring at different locations (stations). Of particular interest is the monitoring of transient events of a stochastic nature, with applications ranging from natural phenomena (e.g., monitoring abnormal seismic activity around a volcano using a ground robot) to urban activities (e.g., monitoring early formations of traffic congestion using an aerial robot). Motivated by examples like these, this paper focuses on problems in which the precise occurrence times of the events are unknown apriori, but statistics for their interarrival times are available. In monitoring such events, the robot seeks to: (1) maximize the number of events observed and (2) minimize the delay between two consecutive observations of events occurring at the same location. This paper considers the case when a robot is tasked with optimizing the event observations in a balanced manner, following a cyclic patrolling route. To tackle this problem, first, assuming that the cyclic ordering of stations is known, we prove the existence and uniqueness of the optimal solution and show that the solution has desirable convergence rate and robustness. Our constructive proof also yields an efficient algorithm for computing the unique optimal solution with O(n) time complexity, in which n is the number of stations, with O(log n) time complexity for incrementally adding or removing stations. Except for the algorithm, our analysis remains valid when the cyclic order is unknown. We then provide a polynomial-time approximation scheme that computes for any ε > 0 a (1 + ε)-optimal solution for this more general, NP-hard problem.