This study proposes a new Gaussian Mixture Filter (GMF) to improve the estimation performance for the au-tonomous robotic radio signal source search and localization problem in unknown environments. The proposed filter is first tested with a benchmark numerical problem to validate the performance with other state-of-the-practice approaches such as Particle Filter (PF) and Particle Gaussian Mixture (PGM) filters. Then the proposed approach is tested and compared against PF and PGM filters in real-world robotic field experiments to validate its impact for real-world applications. The considered real-world scenarios have partial observability with the range-only measurement and uncertainty with the measurement model. The results show that the proposed filter can handle this partial observability effectively whilst showing improved performance compared to PF, reducing the computation requirements while demonstrating improved robustness over compared techniques.

This paper proposes a communication signal strength prediction and validation framework for the use of advanced air mobility. Advanced air mobility, including urban air mobility and unmanned aerial vehicles, requires a scalable, safe, and seamless communication infrastructure different from conventional aircraft. This paper proposes a hybrid regression-based prediction method that combines synthetic data generated from a ray-tracing model and real flight test data in the urban airspace and uses k-fold cross-validation to evaluate the predicted signal strength. The results show that the proposed framework provides reliable performance indices, effectively mitigating the insufficiency of flight data. This research will enable evaluating the communication infrastructure and identifying high-risk areas for advanced air mobility stakeholders.

This paper proposes a deep reinforcement learning-based neighbor selection algorithm designed to enhance the Augmented Cucker-Smale flocking control model for non-holonomic agents. By retaining the control layer, we introduce an additional neighbor selection layer that precedes the control. This layer employs deep reinforcement learning to train the system for faster flock convergence. The actor network within this layer generates probability distributions that determine the inclusion of each neighbor in the control layer. The numerical simulations confirm that the algorithm significantly speeds up flock formation by optimally selecting neighbors for the controller.

This study presents a recursive algorithm for solving the regularised least squares problem for online identification of rigid body dynamic model parameters with emphasis on the physical consistency of estimated inertial parameters. One of the geometric approaches is to use a regulariser that represents how close the pseudo-inertia matrix is to a given reference on the feasible manifold in the regression problem. The proposed extension enables memory-efficient online learning in addition to the benefits of geometry-aware convex regularisation using the log-determinant divergence of the pseudo-inertia matrix. Also, the recursive version endows the estimator with the capability to deal with time-variation of parameters by introducing an optional forgetting mechanism. The characteristics of the recursive regularised least squares algorithm is demonstrated using the MIT Cheetah 3 leg swinging experiment dataset and compared to the existing batch optimisation method.

This study seeks towards a better understanding of multi-vehicle routing problems (VRPs) under restricted observability. Unlike most prior research that assumes full knowledge of tasks and vehicles, this paper addresses VRPs where each vehicle’s observation is confined to the k-nearest neighbourhood. Vehicles make decisions based on localized policies in a decentralized manner. We theoretically demonstrate that for the imitation policy, the upper bound of the optimality gap diminishes as the neighbourhood range expands. Subsequently, we employed a multi-agent cross-observability policy optimization (MACOPO) algorithm to solve the VRPs with restricted observability. The algorithm optimizes a cross-entropy term by leveraging a fully observable expert to guide the training. Empirical results supported both the theoretical findings and the effectiveness of the multi-agent learning algorithm.

A Line of Sight (LOS) angular acceleration-based three-dimensional (3-D) trajectory planning method is designed for aerial vehicles under constrained arrival without range measurement. To achieve this goal, a polynomial in time with arbitrary (greater than 3) orders including a single tuning parameter is developed to create the reference LOS profiles. By revealing the 3-D relation between the lead angle and reference LOS polynomials, the single parameter’s safe range is determined to create feasible LOS profiles that ensure the onboard sensor’s Field of View (FOV) is within its limit. The open-loop acceleration command is derived with time-only information, and the closed-loop command is constructed using the desired LOS angular acceleration profiles. Theoretical analysis proves that the actual LOS profiles perform the same as their desired ones. Moreover, the initial acceleration saturation is avoided, and the final acceleration and LOS rate are zero. The method does not involve the relative range detection, model linearization, switching logic, multiple design parameters, large scale optimization, and the all-covering numerical routine. Finally, extensive simulations are conducted to verify effectiveness of the proposed method.

This article proposes a distributed estimation algorithm that uses local information about the neighbors through sensing or communication to design an estimation-based cooperative control of the stochastic multiagent system (MAS). The proposed distributed estimation algorithm solely relies on local sensing information rather than exchanging estimated state information from other agents, as is commonly required in conventional distributed estimation methods, reducing communication overhead. Furthermore, the proposed method allows interactions between all agents, including non-neighboring agents, by establishing a virtual fully connected network with the MAS state information independently estimated by each agent. The stability of the proposed distributed estimation algorithm is theoretically verified. Numerical simulations demonstrate the enhanced performance of the estimation-based linear and nonlinear control. In particular, using the virtual fully connected network concept in the MAS with the sensing/communication range, the flock configuration can be tightly controlled within the desired boundary, which cannot be achieved through the conventional flocking methods.

A passive fault tolerant control scheme is proposed for the full reentry trajectory tracking of a hypersonic vehicle in the presence of modeling uncertainties, external disturbances, and actuator faults. To achieve this goal, the attitude error dynamics with relative degree two is formulated first by ignoring the nonlinearities induced by the translational motions. Then, a multivariable twisting controller is developed as a benchmark to ensure the precise tracking task. Theoretical analysis with the Lyapunov method proves that the attitude tracking error and its first-order derivative can simultaneously converge to the origin exponentially. To depend less on the model knowledge and reduce the system uncertainties, an incremental twisting fault tolerant controller is derived based on the incremental nonlinear dynamic inversion control and the predesigned twisting controller. In this article, it is shown that not only the benefits of both incremental control and twisting control are inherited, but also their side effects are reduced. Notably, the proposed controller is user friendly in that only fixed gains and partial model knowledge are required. Numerical simulations in various cases and comparison studies are conducted to verify the effectiveness of the proposed method.

This paper investigates the problem of one-step ahead optimal active sensing strategy to minimise estimation errors with range-only measurements for non-manoeuvring target. The determinant of Fisher Information Matrix (FIM) is utilized as the objective function in the proposed optimisation problem since it quantifies the volume of uncertainty ellipsoid of any efficient estimator. In consideration of physical velocity and turning rate constraints, the optimal heading angle command that maximises the cost function is derived analytically. Simulations are conducted to validate the analytical findings.

In this paper, a multi-agent probabilistic optimization algorithm is applied to the problem of multi-vehicle coordination. The algorithm is known as “Probability Collectives” (PC) and has roots in Game Theory and Optimization theory. It is traditionally used for finding optimal solutions of NP-hard problems such as the travelling salesman problem. On the other end, the proposed PC formulation presented in this paper focuses on a minimal complexity implementation for solving the coordination problem in a time of the order of magnitude of 0.1. Besides time constraints, the emphasis in the design is put on ensuring that the algorithm always comes up with a feasible solution. Simulations show that both objectives are reached while having a decentralized algorithm, and flexible with respect to the type of situations it can deal with. Additional benefits of the PC algorithm include robustness to agent failure and the possibility to accommodate non-collaborative vehicles (market penetration of autonomous vehicles <; 100%).

This paper addresses the problem of maneuver recognition and behavior anomaly detection for generic targets by means of pattern matching techniques. The problem analysis is performed making specific reference to moving vehicles in a multi-lane road scenario, but the proposed technique can be easily extended to significantly different monitoring contexts. The potential extensions include, but are not limited to, public surveillance in train station or airport, road incidents and relative precursors detection, and vehicle trajectories monitoring. The overall proposed solution consists of a trajectory analysis tool and a string-matching method. This allows the integration of two different approaches, to detect both a priori defined patterns of interest and generic maneuver/behavior standing out from those regularly exhibited. The proposed string matching algorithm is newly developed in this paper, based on Regular Expressions. For generating reference patterns, a technique for the automatic definition of a dictionary of regular expressions matching the commonly observed target maneuvers is developed. The advantages of the proposed approach are extensively analyzed and tested by means of numerical simulations and experiments.

This paper considers the problem of two-dimensional constrained trajectory optimization of a point-mass aerial robot for constant-maneuvering target localization using bearing-only measurement. A performance metric that can be utilized in trajectory optimization to maximize target observability is proposed first based on geometric conditions. One-step optimal maneuver that maximizes the observability criterion is then derived analytically for moving targets. The heading angle constraint is also incorporated in the proposed optimal maneuver derivation to support practical application. Numerical simulations with some comparisons are presented to validate the analytical findings.

This paper describes a novel particle swarm optimizer algorithm. The focus of this study is how to improve the performance of the classical particle swarm optimization approach, i.e., how to enhance its convergence speed and capacity to solve complex problems while reducing the computational load. The proposed approach is based on an improvement of particle swarm optimization using evolutionary game theory. This method maintains the capability of the particle swarm optimizer to diversify the particles' exploration in the solution space. Moreover, the proposed approach provides an important ability to the optimization algorithm, that is, adaptation of the search direction, which improves the quality of the particles based on their experience. The proposed algorithm is tested on a representative set of continuous benchmark optimization problems and compared with some other classical optimization approaches. Based on the test results of each benchmark problem, its performance is analyzed and discussed.

This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a strongly connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the outcome, and additionally show that at least 50% of suboptimality can be guaranteed if social utilities are nondecreasing functions with respect to the number of coworking agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation.

In this paper we present a decentralised algorithm to solve the Task Allocation problem. Given a set of tasks and a set of robots each with its own utility function, this problem concerns the non-overlapping allocation of tasks to agents maximising the sum of the robot's utility functions. Our algorithm provides a constant factor approximation of (1 - 1/e ≈ 63%) for positive-valued monotone submodular utility functions, and of (1/e ≈ 37%) for positive-valued non-monotone submodular utility functions. In our algorithm, robots only rely on local utility function and neighbour to neighbour communications to find the solution. The algorithm proceeds in two steps. Initially, the robots use Maximum Consensus-based algorithm to find a relaxed solution of the problem using the multilinear extension of their utility functions. Finally, they follow a randomised rounding procedure to exchange valuations on sets of tasks in order to round the relaxed solution. To conclude, we provide experimental evidence of our claims for both monotone and non-monotone submodular functions.

This paper presents a new standoff tracking framework of a moving ground target using UAVs with a limited sensing capability such as sensor field-of-view and motion constraints. To maintain persistent track of the target even in case of target loss (out of surveillance) for a certain period, this study predicts the target existence area using the particle filter, and produces control commands to ensure that all predicted particles can be covered by the field-of-view of the UAV sensor at all times. To improve target prediction/estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as nonlinear inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control methods are applied for the standoff tracking and phase angle control, and the advantages and disadvantages of them are compared using numerical simulation results.