This paper studies the localization problem using noisy range-difference measurements, or equivalently time difference of arrival (TDOA) measurements. There is a reference sensor, and for each other sensor, the TDOA measurement is obtained with respect to the reference one. By minimizing the sum of squared errors, a nonconvex constrained least squares (CLS) problem is formulated. In this work, we focus on devising an algorithm to seek the global minimizer of the CLS problem, hoping that the numerical solution meets some precision requirement in terms of relative error. Based on the Lagrange multiplier method, we first branch the feasible Lagrange multiplier set into several subsets and develop a workflow in terms of if-then-else control structure to seek the global minimizer by searching for the optimal Lagrange multiplier. The execution order is carefully organized so that it is in line with the general principle of putting the flow that one normally understands to be executed first. We then dive into detailed searching methods in different cases and conduct computational error analysis, giving the error bound on the Lagrange multiplier, when we search for it, to meet the precision requirement on an approximate solution. Based on the above achievements, a programmable global minimizer seeking algorithm is proposed for the CLS problem. Simulations and experimental tests on a public dataset demonstrate the effectiveness of the proposed algorithm.

Overtaking the lead vehicle on two-way roads in the presence of several oncoming vehicles is a complex task for autonomous vehicles. In this paper, we formulate the overtaking behavior of an ego vehicle based on a deep reinforcement learning (DRL) method. First, a two-way urban road is created, wherein the ego vehicle aims to reach the destination safely and efficiently while considering multiple traffic participants. We use different intelligent driver model (IDM) parameters to account for different drivers' habits. Furthermore, we introduce different responses of other vehicles when the ego vehicle takes overtaking maneuver. Then, a hierarchical control framework is proposed to manage vehicles on the road, which supervises vehicle behaviors at the high layer and controls the motion at the lower layer. The DRL method named Proximal Policy Optimization is applied to derive the high-level decision-making policies. A self-attention mechanism is further introduced to improve the performance of our algorithm. Finally, the overtaking maneuvers of the ego vehicle in different training timesteps are analyzed and how the responses of other vehicles affect the ego one's overtaking behavior is investigated. Simulation results show that our approach can achieve good performance to deal with the two-way road autonomous overtaking task. Supplementary video is available at https://youtu.be/jPEGjM7cBuk.

In this paper, an algorithm on circular circumnavigation of nonholonomic agents is proposed. The agents are required to enclose a static target with predefined radius and circumferential speed. The algorithm completely relies on local bearing angle measurement. Cyclic pursuit is adapted to coordinate the agents and generate an even formation at the circle. Theoretical analysis on the stability of algorithm is given. The applicability and effectiveness of the algorithm is testified with simulations on the unmanned surface vessel (USV) platform.

Harmful algal blooms occur frequently and deteriorate water quality. A reliable method is proposed in this paper to track algal blooms using a set of autonomous surface robots. A satellite image indicates the existence and initial location of the algal bloom for the deployment of the robot system. The algal bloom area is approximated by a circle with time varying location and size. This circle is estimated and circumnavigated by the robots which are able to locally sense its boundary. A multi-agent control algorithm is proposed for the continuous monitoring of the dynamic evolution of the algal bloom. Such algorithm comprises of a decentralised least squares estimation of the target and a controller for circumnavigation. We prove the convergence of the robots to the circle and in equally spaced positions around it. Simulation results with data provided by the SINMOD ocean model are used to illustrate the theoretical results.