Receding horizon control of mobile robots for locating unknown wireless sensor networks

Abstract

Purpose The purpose of this paper is to propose a receding horizon control approach for the problem of locating unknown wireless sensor networks by using a mobile robot. Design/methodology/approach A control framework is used and consists of two levels: one is a decision level, while the other is a control level. In the decision level, a spatiotemporal probability occupancy grid method is used to give the possible positions of all nodes in sensor networks, where the posterior probability distributions of sensor nodes are estimated by capturing the transient signals. In the control level, a virtual robot is designed to move along the edge of obstacles such that the problem of obstacle avoidance can be transformed into a coordination problem of multiple robots. On the basis of the possible positions of sensor nodes and virtual robots, a receding horizon control approach is proposed to control mobile robots to locate sensor nodes, where a temporary target position method is utilized to avoid several special obstacles. Findings When the number of obstacles increases, the average localization errors between the actual locations and the estimated locations significantly increase. Originality/value The proposed control approach can guide the mobile robot to avoid obstacles and deal with the corresponding dynamical events so as to locate all sensor nodes for an unknown wireless network.

Signal Source Traversal Based on Quadrotors in Environments With Obstacles: A Data-Driven Receding Horizon Control Approach

“…Finally, according to Algorithm 1, Algorithm 2 describes the proposed data-driven receding horizon control approach. Remark 2: It should be pointed out that the proposed datadriven receding horizon control approach has two characteristics compared with our previous work [8]. One characteristic is that the design of cost functions in this paper considers the features of quadrotors while the design of cost functions in [8] is appropriate for the mobile robots.…”

“…For example, in ocean environments, unmanned underwater vehicles are usually required to sample some regions of interest in sequence such that the data on environmental attributes can be real-time obtained to establish the concentration distribution map of environmental attributes [17]. In land environments, in order to defend the monitoring functions of the sensor network from the parties with conflict of interest, mobile robots are required to search for and locate each sensor node [8]. In air environments, detecting gas concentration of each chimney with a certain height in factories is a dangerous task for humankind.…”

“…Moreover, the designed controller in [25] for signal source traversal cannot enable the mobile robot to avoid obstacles. As a result, the model predictive control (MPC) [1], [2], which is also regarded as receding horizon control (RHC), has been employed to deal with signal source traversal based on a single mobile robot [8]. Instead, the RHC approach in [8] does not well deal with the problem of signal source traversal based on a single quadrotor.…”

Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

“…This strategy can be combined with a sequential convex programming method to develop a closed-loop guidance control scheme that robustly drives a spacecraft maneuvering close to the target [29]. This strategy can also be used to control mobile robots to locate sensor nodes in unknown wireless sensor networks [30]. The strategy has proven to be effective to enhance the scheme quality and reduce the computational burden by dividing the optimization horizon into smaller time windows [31].…”

“…The curvature of the Lamé curve and its time-derivative. For a constant velocity, v c = 0.5 m/s, the actuated joint rates and their time-derivatives on different trajectories are calculated based on Equations(30) and(31), as shown inFigures 11 and 12, respectively.…”