This research addresses a multi-robot coverage path plan strategy, where multiple Underwater Robots (UR) are deployed from a surface ship, so that they cover a bounded underwater 3D surveillance space. Suppose that each UR has a target detection sensor, such as active sonar, with a finite target detection range. The goal of a UR is to detect an underwater target inside its target detection range and to transmit the detection event to the surface ship through multi-hop communication link. Considering a mobile UR with a limited sensing-communication range, each UR maneuvers relying on local interaction only. Note that once cannot localize a UR in the global coordinate systems, since Global Navigation Satellite Systems (GNSS) cannot be used in underwater environments. Suppose that a target detection probability threshold is given by a network operator and that URs’ mobile network structure needs to satisfy the given target detection probability threshold. It is desirable to minimize the number of deployed URs, since we need to decrease the cost of the networked system. One thus computes the URs’ mobile network structure, such that the given target detection probability threshold is met with a small number of URs. This research addresses 3D multi-robot coverage path plan relying on local sonar sensing only, while not using global localization. To our knowledge, this coverage path plan is novel, since a small number of URs completes the surveillance space coverage task in GNSS-denied underwater 3D environments, while satisfying a given target detection probability threshold. In practical scenarios, random Deny-of-Service (DoS) attack can exist, blocking communication links among URs. To our knowledge, the proposed coverage path plan is novel, since it is robust to robot failures or random DoS attack whose consecutive duration time is bounded above. 3D computer simulations prove the effectiveness of the multi-UR path plan approach.

Multi-target tracking is an important scenario of sensor networks in obstacle-rich environments. We consider a priori known obstacles, which can block the line-of-sight between a sensor and a target. There is a remote processing center, where a target information is computed integrating measurements of multiple sensors. This article assumes that we can access the global positions of all active sonar sensors and the processing center. Considering cluttered environments, we track multiple targets by fusing the relative position measurements of each active sonar sensor. We let a sensor transmit power-consuming active sonar pulses for detecting a target, only in the case where the sensor is awaken. For tracking multiple targets while diminishing power consumption, we propose a sensor awakening scheme which awakens only a sensor with high probability of sensing a target. As far as we know, our paper is novel in tracking multiple targets utilizing active sonar networks in obstacle-rich workspace with cluttered measurements, such that each active sonar sensor can measure the relative position of a nearby target while reducing power consumption. MATLAB-based simulations demonstrate the outperformance of the proposed multi-target tracking scheme utilizing active sensor networks in a cluttered workspace.

This manuscript addresses the 3D localization of an underwater robot exploring underwater environments. For underwater robot’s 3D localization, the robot measures the Angle-Of-Arrival (AOA) of one or more underwater signal transmitters. It is assumed that the global position of a transmitter is known in advance. Considering a robot which can measure its depth, using AOA of at least one transmitter can make the robot position observable. Considering the case where there are one or more known transmitters, this manuscript addresses an underwater localization strategy to estimate the robot’s 3D location by utilizing the AOA measurements of transmitters. Our article utilizes the Kalman filter (KF) for robot position estimation in real time. In the measurement update step of the KF, the robot uses AOA measurements of one or more known transmitters. In underwater environments, measurement loss or outlier measurements can happen, and the robot may make mistakes in identifying a transmitter. The proposed KF thus applies a gating approach for handling these cases. To our knowledge, our article is novel in addressing the underwater robot’s 3D localization by measuring AOA of one or more known transmitters. The effectiveness of the proposed 3D localization is demonstrated utilizing MATLAB simulations.

This article considers 3-D underwater environments cluttered with obstacles. This article handles how to localize all transceiver nodes in sensor networks. Each node can measure its depth, while measuring time of arrival (TOA), and angle of arrival (AOA) of signals generated from its nearby node. Each node is heterogeneous, such that the sensing noise of a node may be distinct from those of other nodes. In the 3-D sensor network, only one reference node is localized utilizing global navigation satellite system (GNSS). Considering obstacle-rich 3-D environments, our problem is to localize all heterogeneous transceiver nodes in the network, such that the position of only a single reference node is known a priori. In this article, we consider a large scale obstacle-rich environment, such that there exists a transceiver which cannot measure a direct signal of the reference node due to a limited sensing range. Since only one reference node is localized, one localizes every transceiver node in a breadth-first manner, starting from the reference node. This article further shows that the proposed localization scheme is robust to random DoS attack whose consecutive duration time is bounded above. To the best of our knowledge, this article is novel in hybrid TOA-AOA localization of the entire heterogeneous 3-D networks, considering the case where only one reference node exists in 3-D obstacle-rich environments. The effectiveness of the proposed 3-D localization scheme is verified utilizing MATLAB simulations.

This study tackles locating a noncooperative source based on passive measurements of multiple sonar sensors, such that every sonar sensor can measure both frequency difference of arrival (FDOA) and time difference of arrival (TDOA) of the source’s sound signal. This article considers a scenario where the unknown carrier frequency of the source may vary as time elapses. Furthermore, there can be large errors in underwater sensor localization, since global positioning system (GPS) cannot be used in underwater environments. This study addresses FDOA-TDOA localization under the assumption that a sensor’s location error is bounded above and that the speed limit of the source is accessible a priori. As far as we know, this manuscript is novel in solving FDOA-TDOA localization, considering scenarios where a sensor’s location error exists and unknown carrier frequency can vary as time elapses. In addition, our research is novel in not requiring a prior information on the FDOA-TDOA measurement noise power. The effectiveness of the proposed hybrid FDOA-TDOA localization scheme is demonstrated by comparing it with the state-of-the-art hybrid FDOA-TDOA localization through extensive computer-based simulations.

This article introduces distributed network aggregation controllers to let all minimalist robots (MRs) aggregate close to a specified point. Here, one says that all MRs are aggregated close to a specified point, if they are within a certain hop distance from the specified point. Every MR is initially distributed in an unknown cluttered obstacle-rich environments, in which global position system signal is not available. This article assumes that each MR moves with a bounded speed and has a cheap signal intensity sensor with a limited field of view (FOV). An MR uses a cheap signal intensity sensor as a binary sensor for detection of another MR inside the FOV. Aggregation controls are proposed in this study, so that network connectivity is assured while an MR moves. In practice, random Deny-of-Service (DoS) attack can exist, blocking communication between MRs in multiagent system. One shows that the proposed aggregation controls are robust to random DoS attack whose consecutive duration time is bounded above. As far as we know, this article is novel in addressing how to make all MRs aggregate close to a specified point, considering obstacle-rich environments. Moreover, this article is novel in using a signal intensity sensor with a limited FOV for distributed aggregation of multiagent systems. It is proved that all MRs aggregate close to a specified point, while assuring network connectivity in cluttered environments. We demonstrate the effectiveness of our approach utilizing extensive MATLAB simulations in obstacle-rich environments. In MATLAB simulations, we run Monte Carlo simulations, while varying the simulation parameters. In all Monte Carlo simulations, all MRs succeed in aggregation tasks while maintaining network connectivity.

This paper considers how to improve the localization accuracy of a target in 3D underwater environments using a set of sonar sensors. This research considers bistatic sonar sensors composed of one sound source and multiple omnidirectional hydrophones in 3D underwater environments. We consider an underwater omnidirectional hydrophone, which measures bistatic range of the underwater target. We consider 3D underwater environments, such that the depth of a hydrophone or the source can be different from that of the target. The omnidirectional hydrophone estimates the underwater target location by processing bistatic range in sonar measurements. This research considers the practical scenario in which a target location information isn’t accessible a priori. As far as we know, our paper is novel in addressing how to deploy multiple omnidirectional hydrophones processing bistatic range measurements, for minimizing the uncertainty on the 3D underwater target location. MATLAB-based simulations are employed to verify the proposed 3D hydrophone deployment strategy.

This study handles tracking multiple objects under bearings-only measurements of multiple heterogeneous sonar sensors in cluttered underwater environments. In order to track multiple objects in clutter environments, we apply Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter. In the GM-PHD filter, the Extended Kalman Filter (EKF) is applied, since the EKF can cope with non-linearity of bearings-only measurements. However, range uncertainty of the object is large for bearings-only measurements, which can generate poor performance in the GM-PHD. The bearing measurements of any two passive sonar sensors can intersect at one point, and the point provides the viable location of an object. Hence, if an intersection point of two bearing sensors satisfies the maximum sensing range constraint, then the point can generate an object sample in our GM-PHD Kalman filter. In addition, every object sample is updated under the measurement update step of the EKF. To the best of our knowledge, this paper is novel in tracking multiple objects in clutter, under bearings-only measurements of multiple heterogeneous sonar sensors. The efficacy of the proposed GM-PHD Kalman filter is demonstrated under MATLAB simulations.

Harmful marine spills, such as algae blooms and oil spills, damage ecosystems and threaten public health tremendously. Hence, an effective spill coverage and removal strategy will play a significant role in environmental protection. In recent years, low-cost water surface robots have emerged as a solution, with their efficacy verified at small scale. However, practical limitations, such as connectivity, scalability, and sensing and operation ranges significantly impair their large-scale use. To circumvent these limitations, we propose a novel asymptotic boundary shrink control strategy that enables collective coverage of a spill by autonomous robots featuring customized operation ranges. For each robot, a novel controller is implemented that relies only on local vision sensors with limited vision range. Moreover, the distributedness of this strategy allows any number of robots to be employed without inter-robot collisions. Finally, features of this approach including the convergence of robot motion during boundary shrink control, spill clearance rate, and the capability to work under limited ranges of vision and wireless connectivity are validated through extensive experiments with simulation.

This article considers a team of autonomous underwater vehicles (AUVs) such that each AUV measures the arrival time of sound wave emitted from the target. The goal of the AUV team is to capture the target based on the arrival time measurements. This article presents both the tracking controller for the AUV team and the algorithm to estimate the target location in real time. In this article, the AUVs chase the target while preserving a circle formation. The proposed approach is to control the center of the formation, such that the line-of-sight (LOS) between the target and the center is parallel at every sampling step. Moreover, we control the formation size adaptively to avoid the divergence of the target estimation. As far as we know, this article is unique in utilizing the multiple AUVs to track one target, considering the case in which every AUV measures the arrival time of sound wave emitted from the target. MATLAB simulations are used to verify the effectiveness of the proposed approach.

Multirobot rendezvous control and coordination strategies have garnered significant interest in recent years because of their potential applications in decentralized tasks. In this paper, we introduce a coordinate-free rendezvous control strategy to enable multiple robots to gather at different locations (dynamic leader robots) by tracking their hierarchy in a connected interaction graph. A key novelty in this strategy is the gathering of robots in different groups rather than at a single consensus point, motivated by autonomous multipoint recharging and flocking control problems. We show that the proposed rendezvous strategy guarantees convergence and maintains connectivity while accounting for practical considerations such as robots with limited speeds and an obstacle-rich environment. The algorithm is distributed and handles minor faults such as a broken immobile robot and a sudden link failure. In addition, we propose an approach that determines the locations of rendezvous points based on the connected interaction topology and indirectly optimizes the total energy consumption for rendezvous in all robots. Through extensive experiments with the Robotarium multirobot testbed, we verified and demonstrated the effectiveness of our approach and its properties.

We consider an unmanned surface vehicle (USV) whose mission is to approach a maneuvering target ship as fast as possible followed by monitoring the target closely. The USV is equipped with sensors to detect the relative position of the target ship. Also, we assume that the USV is faster than the target and is localized using global positioning system. To achieve the mission, we develop a control law to make the USV approach the designated point (e.g., stern, left side, or right side, etc.) of the target as fast as possible. Once the USV reaches the designated point of the target, the USV checks whether the speed of the USV is fast enough to circle around the target. In the case where the circling maneuver is not feasible, the USV keeps following the designated point of the target. Once the USV determines that the circling maneuver is feasible, then we make the USV circle around the target for close monitoring, while maintaining a designated distance from the target. We present MATLAB simulations to verify our approach.

An information (sensor) network can be used to monitor various 3-D environments, such as underwater environments. To build an information network in 3-D environments, we use multiple robots deploying information nodes. Our 3-D networking strategy results in the network without coverage holes and does not require the global localization of a node or a robot. We assume that as the size of each sensor coverage decreases, the power consumption of the sensor decreases. Thus, once the 3-D network is built by multiple robots, each node searches for the smallest size of its sensing coverage while assuring that there is no hole in the global coverage. In this way, we save the power consumption while preserving the global coverage. Note that each node searches for its optimal sensing coverage by accessing the relative positions of its neighbors only once. This implies that energy consumption of searching for the optimal coverage is very low. Using MATLAB simulations, we verify the scalability and effectiveness of our strategies in 3-D environments.

Monitoring of large complex environments, such as underwater environments, is an important task in surveillance. An information (sensor) network can be built to achieve the task. To build an information network in an unknown workspace, we use multiple robots deploying information nodes. While robots build the network, they localize themselves as well as deployed nodes in the global coordinate system. Our multirobot networking strategy is as follows: each robot iteratively visits a frontier, which borders an unsensed area, until all areas are explored. As multiple robots explore the workspace, a robot must avoid colliding with another robot as well as with an obstacle. Hence, we introduce collision avoidance control laws and integrate the control laws with our cooperative networking strategy. Using MATLAB simulations, we verify the scalability and effectiveness of both our networking strategy and the collision avoidance control laws.