Environmental boundary tracking and estimation using multiple autonomous vehicles

In this paper, we develop a framework for environmental boundary tracking and estimation by considering the boundary as a hidden Markov model (HMM) with separated observations collected from multiple sensing vehicles. For each vehicle, a tracking algorithm is developed based on Page’s cumulative sum algorithm (CUSUM), a method for change-point detection, so that individual vehicles can autonomously track the boundary in a density field with measurement noise. Based on the data collected from sensing vehicles and prior knowledge of the dynamic model of boundary evolvement, we estimate the boundary by solving an optimization problem, in which prediction and current observation are considered in the cost function. Examples and simulation results are presented to verify the efficiency of this approach

Random consensus protocol in large-scale networks

One of the main performance issues for consensus protocols is the convergence speed. In this paper, we focus on the convergence behavior of discrete-time consensus protocols over large-scale sensor networks with uniformly random deployment, which are modelled as Poisson random graphs. Instead of using the random rewiring procedure, we introduce a deterministic principle to locate certain “chosen nodes” in the network and add “virtual” shortcuts among them so that the number of iterations to achieve average consensus drops dramatically. Simulation results are presented to verify the efficiency of this approach. Moreover, a random consensus protocol is proposed, in which virtual shortcuts are implemented by random routes

Optimal scheduling for refueling multiple autonomous aerial vehicles

The scheduling, for autonomous refueling, of multiple unmanned aerial vehicles (UAVs) is posed as a combinatorial optimization problem. An efficient dynamic programming (DP) algorithm is introduced for finding the optimal initial refueling sequence. The optimal sequence needs to be recalculated when conditions change, such as when UAVs join or leave the queue unexpectedly. We develop a systematic shuffle scheme to reconfigure the UAV sequence using the least amount of shuffle steps. A similarity metric over UAV sequences is introduced to quantify the reconfiguration effort which is treated as an additional cost and is integrated into the DP algorithm. Feasibility and limitations of this novel approach are also discussed

Scheduling and sequence reshuffle for autonomous aerial refueling of multiple UAVs

In this paper, we formulate the autonomous aerial refueling of multiple unmanned aerial vehicles (UAVs) as a scheduling problem. In order to find the optimal refueling sequence of UAVs, an efficient dynamic programming algorithm is introduced. When UAVs leave or join the queue, the optimal sequence needs to be recalculated. A systematic reshuffling method is developed such that the UAV sequence can be reconfigured by using the least amount of shuffle steps, where only one UAV changes its position in each step. By introducing a metric over UAV sequences, this reconfiguration effort is quantified and is treated as an additional cost which can be integrated into the dynamic programming algorithm

Estimation for Nonlinear Dynamical Systems over Packet-Dropping Networks

Two approaches, extended Kalman filter (EKF) and moving horizon estimation (MHE), are discussed for state estimation for nonlinear dynamical systems over packet-dropping networks. For EKF, we provide sufficient conditions that guarantee a bounded EKF error covariance. For MHE, a natural scheme on organizing the finite horizon window is proposed to handle intermittent observations. A nonlinear programming software package, SNOPT, is employed in MHE and the formulation for constraints is discussed in detail. Examples and simulation results are presented

Multi-Hop Relay Protocols for Fast Consensus Seeking

Consensus protocols are distributed algorithms in networked multi-agent systems. Based on the local information, agents automatically converge to a common consensus state and the convergence speed is determined by the algebraic connectivity of the communication network. In order to achieve a fast consensus seeking, we propose the multi-hop relay protocols, where each agent can expand its knowledge by employing multi-hop paths in the network.We demonstrate that multi-hop relay protocols can enlarge the algebraic connectivity without physically changing the network topology. Moreover, communication delays are discussed and a tradeoff is identified between the convergence speed and the time delay sensitivity

Coordinated Control for Networked Multi-Agent Systems

Coordination in networked multi-agent systems attracts significant interest in the realm of engineering. Typical examples include formations of unmanned aerial vehicles, automated highway systems, and sensor networks. One common feature for these systems is that coordinated behaviors are exhibited by interactions among agents where information exchange and manipulation are necessary. In this work, three relevant issues are investigated in detail: uniform strategy for multi-agent formation control, fast-converging consensus protocols, and packet-based state estimation over communication networks. Formation control of multi-agent systems involves harmony among local controller design, interaction topology analysis, and objective agreement among networked agents. We propose a novel control strategy so that each agent responds to neighbors' behaviors as well as acts towards the global goal. Thus, information flows for local interactions and global objective synchronization are studied separately. Using the tools from signal flow graphs and algebraic graph theory, we show that this new strategy eases the design of local controllers by relaxing stabilizing conditions. Robustness against the link failure and scalable disturbance resistance are also discussed based on small-gain theory. Experimental results on the Caltech multi-vehicle wireless testbed are provided to verify the feasibility and efficiency of this control strategy. Consensus protocols over communication networks are used to achieve agreement among agents. One important issue is the convergence speed. We propose multi-hop relay protocols for fast consensus seeking. Without physically changing the topology of the communication network, this type of distributed protocol increases the algebraic connectivity by employing multi-hop paths in the network. We also investigate the convergence behaviors of consensus protocols with communication delays. It is interesting that, the faster the protocol converges, the more sensitive it is to the delay. This tradeoff is identified when we investigate delay margins of multi-hop relay protocols using the frequency sweep method. Efficiently estimating the states of other agents over communication links is also discussed in this work. When information flows in the network, packet-based data is normally not retransmitted in order to satisfy real-time requirements. Thus, packet drops and random delays are inevitable. In this context, observation data that the estimator can receive is intermittent. In order to decrease the chance of losing packets and efficiently using the limited bandwidth, we introduce multiple description source codes to manipulate the data before transmission. Using modified algebraic Riccati equations, we show that multiple description codes improve the performance of Kalman filters over a large set of packet-dropping scenarios. This problem is also generalized to the case where observation data has an independent and identical static distribution over a finite set of observation noise. Moreover, Kalman filtering with bursty packet drops is also discussed based on the two-state Markov chain model.</p