Ergodicity-Based Cooperative Multiagent Area Coverage via a Potential Field

Robust Near-Optimal Coordination in Uncertain Multiagent Networks With Motion Constraints

This article addresses the robust coordination problem for nonlinear uncertain second-order multiagent networks with motion constraints, including velocity saturation and collision avoidance. A single-critic neural network-based approximate dynamic programming approach and exact estimation of unknown dynamics are employed to learn online the optimal value function and controller. By incorporating avoidance penalties into tracking variable, constructing a novel value function, and designing of suitable learning algorithms, multiagent coordination and collision avoidance are achieved simultaneously. We show that the developed feedback-based coordination strategy guarantees uniformly ultimately bounded convergence of the closed-loop dynamical stability and all underlying motion constraints are always strictly obeyed. The effectiveness of the proposed collision-free coordination law is finally illustrated using numerical simulations.

Motion Control for Autonomous Heterogeneous Multiagent Area Search in Uncertain Conditions

Using multiple mobile robots in search missions offers a lot of benefits, but one needs a suitable and competent motion control algorithm that is able to consider sensor characteristics, the uncertainty of target detection, and complexity of needed maneuvers in order to make a multiagent search autonomous. This article provides a methodology for an autonomous 2-D search using multiple unmanned (aerial or possibly other) vehicles. The proposed methodology relies on an accurate calculation of target occurrence probability distribution based on the initial estimated target distribution and continuous action of spatial variant search agent sensors. The core of the autonomous search process is a high-level motion control for multiple search agents which utilizes the probabilistic model of target occurrence via a heat equation-driven area coverage (HEDAC) method. This centralized motion control algorithm is tailored for handling a group of search agents that are heterogeneous in both motion and sensing characteristics. The motion of agents is directed by the gradient of the potential field which provides a near-ergodic exploration of the search space. The proposed method is tested on three realistic search mission simulations and compared with three alternative methods, where HEDAC outperforms all alternatives in all tests. Conventional search strategies need about double the time to achieve the proportionate detection rate when compared to HEDAC controlled search. The scalability test showed that increasing the number of an HEDAC controlled search agents, although somewhat deteriorating the search efficiency, provides needed speed-up of the search. This study shows the flexibility and competence of the proposed method and gives a strong foundation for possible real-world applications.

Heterogeneity-Aware Graph Partitioning for Distributed Deployment of Multiagent Systems

In this work, we examine the distributed coverage control problem for deploying a team of heterogeneous robots with nonlinear dynamics in a partially known environment modeled as a weighted mixed graph. By defining an optimal tracking control problem, using a discounted cost function and state-dependent Riccati equation (SDRE) approach, a new partitioning algorithm is proposed to capture the heterogeneity in robots dynamics. The considered partitioning cost, which is a state-dependent proximity metric, penalizes both the tracking error and the control input energy that occurs during the movement of a robot, on a straight line, to an arbitrary node of the graph in a predefined finite time. We show that the size of the subgraph associated with each robot depends on its resources and capabilities in comparison to its neighbors. Also, a distributed deployment strategy is proposed to optimally distribute robots aiming at persistently monitoring specified regions of interest. Finally, a series of simulations and experimental studies is carried out to demonstrate the viability and efficacy of the proposed methodology in deploying heterogeneous multiagent systems.

Finite-Time Coverage Control for Multiagent Systems With Unidirectional Motion on a Closed Curve

The finite-time coverage control problem for networked mobile agents with continuous-time dynamics and unidirectional motion constraint on a closed curve is addressed in this article. The objective is to minimize a coverage cost function in finite time which characterizes the largest arrival time from the multiagent system to any point on the curve. Low gain feedback is employed to design the distributed coverage control laws for the agents with different input constraints due to their limited movement capabilities. An upper bound on each low gain is also provided by showing that the distance between the neighboring agents is always lower and upper bounded. Under the proposed control laws, the networked mobile agents can be driven to the optimal configuration, minimizing the coverage cost function in finite time. Due to the unidirectional motion of the agents, there exists at least one agent which remains static throughout the coverage task. As a result, a less conservative upper bound on the low gains is derived, which provides the possibility of better convergence rates of the proposed coverage control laws.

A Backseat Control Architecture for a Slocum Glider

Adaptive sampling provides an innovative and favorable method of improving the effectiveness of underwater vehicles in collecting data. Adaptive sampling works by controlling an underwater vehicle by using measurements from sensors and states of the vehicle. A backseat driver system was developed in this work and installed on a Slocum glider to equip it with an ability to perform adaptive sampling tasks underwater. This backseat driver communicated with the main vehicle control system of the glider through a robot operating system (ROS) interface. The external control algorithms were implemented through ROS nodes, which subscribed simulated sensor measurements and states of the glider and published desired states to the glider. The glider was set up in simulation mode to test the performance of the backseat driver as integrated into the control architecture of the glider. Results from the tests revealed that the backseat driver could effectively instruct the depth, heading, and waypoints as well as activate or deactivate behaviors adaptively. The developed backseat driver will be tested in future field experiments with sensors included and safety rules implemented before being applied in adaptive sampling missions such as adaptive oil spill sampling.

Search strategy in a complex and dynamic environment: the MH370 case

Search and detection of objects on the ocean surface is a challenging task due to the complexity of the drift dynamics and lack of known optimal solutions for the path of the search agents. This challenge was highlighted by the unsuccessful search for Malaysian Flight 370 (MH370) which disappeared on March 8, 2014. In this paper, we propose an improvement of a search algorithm rooted in the ergodic theory of dynamical systems which can accommodate complex geometries and uncertainties of the drifting search areas on the ocean surface. We illustrate the effectiveness of this algorithm in a computational replication of the conducted search for MH370. We compare the algorithms using many realizations with random initial positions, and analyze the influence of the stochastic drift on the search success. In comparison to conventional search methods, the proposed algorithm leads to an order of magnitude improvement in success rate over the time period of the actual search operation. Simulations of the proposed search control also indicate that the initial success rate of finding debris increases in the event of delayed search commencement. This is due to the existence of convergence zones in the search area which leads to local aggregation of debris in those zones and hence reduction of the effective size of the area to be searched.

Indoor Localization Based on Infrared Angle of Arrival Sensor Network

Accurate, inexpensive, and reliable real-time indoor localization holds the key to the full potential of the context-aware applications and location-based Internet of Things (IoT) services. State-of-the-art indoor localization systems are coping with the complex non-line-of-sight (NLOS) signal propagation which hinders the use of proven multiangulation and multilateration methods, as well as with prohibitive installation costs, computational demands, and energy requirements. In this paper, we present a novel sensor utilizing low-range infrared (IR) signal in the line-of-sight (LOS) context providing high precision angle-of-arrival (AoA) estimation. The proposed sensor is used in the pragmatic solution to the localization problem that avoids NLOS propagation issues by exploiting the powerful concept of the wireless sensor network (WSN). To demonstrate the proposed solution, we applied it in the challenging context of the supermarket cart navigation. In this specific use case, a proof-of-concept navigation system was implemented with the following components: IR-AoA sensor prototype and the corresponding WSN used for cart localization, server-side application programming interface (API), and client application suite consisting of smartphone and smartwatch applications. The localization performance of the proposed solution was assessed in, altogether, four evaluation procedures, including both empirical and simulation settings. The evaluation outcomes are ranging from centimeter-level accuracy achieved in static-1D context up to 1 m mean localization error obtained for a mobile cart moving at 140 cm/s in a 2D setup. These results show that, for the supermarket context, appropriate localization accuracy can be achieved, along with the real-time navigation support, using readily available IR technology with inexpensive hardware components.