This work extends the principle of dual control for exploitation and exploration to robotic assembly tasks, where successful assembly requires both accurate state estimation and informed control actions to reduce uncertainty. The assembly task is formulated as an active inference problem in which the robot maintains a probabilistic belief of target location and executes actions that simultaneously reduce uncertainty (exploration) and drive the system towards alignment(exploitation). The framework is validated on an industrial UR10e robot for circular and square peg-in-hole insertions, representing distinct geometric contact conditions. To realize this, a physics based measurement model is derived that relates contact forces to relative peg-hole position to support Bayesian estimation of the target state. Performance is evaluated under two uncertainty regimes: (i) small initial error (low bias and uncertainty) and (ii) large initial error (high bias and uncertainty), and compared against a single step model predictive control and an entropy-based path planning strategy. Experimental results demonstrate that the proposed approach achieves robust and reliable insertions across a wide range of initial conditions, highlighting the potential of the proposed work for robotic assembly.

Large language models guided evolutionary frameworks for program synthesis have demonstrated strong results across combinatorial benchmarks; however, such frameworks have not been widely explored for robotic control systems. This article presents an automated framework for synthesizing control logic and optimizing numerical coefficients, enabling the deployment of generated controllers onto robotic platforms. To produce deployable controllers, the framework separates control logic synthesis from numerical parameter optimization, with controller coefficients optimized via particle swarm optimization using a Markov decision process reward signal. The resulting controllers are evaluated in a custom uncrewed aerial vehicles (UAV) simulation environment, validated using PX4 software-in-the-loop, and subsequently deployed on a physical UAV. Controller performance is evaluated on lemniscate and lissajous trajectory-tracking tasks and compared against proportional–integral–derivative with disturbance observer and linear quadratic regulator baselines. Across both trajectories, the framework-generated control law achieves improved tracking accuracy relative to the baseline controllers, with a minimum reduction in mean squared error of approximately 38% in real-world experiments. These results demonstrate the feasibility of deploying automatically synthesized control logic on a physical UAV, bridging automated program synthesis and real-world control deployment.

Searching and tracking for mobile targets with multiple mobile sensing platforms is a relevant capability for many real-world scenarios such as search and rescue and surveillance. This paper presents an approach to performing searching and tracking for a single target of interest using multiple mobile sensor systems in urban environments. Formulating the searching and tracking problem as a partially observable Markov decision process, a coverage control objective inspired reward function is designed, to create a single objective function for both searching and tracking behaviours. Furthermore, the approach alleviates differentiability requirements in typical coverage control approaches. Visibility informed Bernoulli filter provides a suitable tracking algorithm estimating both probability that a target exists and its state. Road network information is used to inform the information coverage objective for scenario tailored search behaviour. Monte Carlo tree search is adopted to implement the algorithm non-myopically and balance exploitation of immediate rewards with exploration of potential future gains. Contribution to the open source Stone Soup framework creates several new components for the toolkit, where the flexibility of the approach can be leveraged with the established wealth of tracking and filtering algorithms. Simulated Urban Mobility software package is integrated with Stone Soup to create a realistic urban simulation scenario with two sensing agents. Through Monte Carlo simulations, the algorithm demonstrates efficient search behaviour, good target tracking performance and effective coverage of the target once found.

Maintaining superiority on the battlefield is vital in ensuring mission success. The use of advanced technologies such as Autonomy and Artificial Intelligence offers the ability for the human to let the machines do the heavy lifting of tasks; especially those that require rapid processing of complex information and within a dynamic environment. This paper outlines the MASTER SOUP project that explores and demonstrates the use of AI to manage multiple sensors, whilst also introducing a new method to assist the multidisciplinary design team to better understand how the system deals with decisions that occur during the mission. The use of a method utilizing decision strings is outlined and discussed; whilst using previous studies that have theorized its use, this paper is the first to employ this approach. By adopting this method, it was found that it was beneficial to both Human Factors and AI Engineers in terms of designing the Human-Machine Teaming concept. The use of decision strings provides an intuitive methodology for identifying and deconstructing the nature of decisions within the Human-Machine Team. Further to this it can be used to the benefit of all members of the design team to facilitate the elicitation of design requirements that provide benefits across multidisciplinary fields. It is noted that this approach is new, we hope it will allow other researchers to adopt this methodology and contribute to the validation of this important method used within multidisciplinary design teams.

Source term estimation of a hazardous release remains a topic of significant interest in the robotics and state estimation communities, with application to many safety critical scenarios including gas or nuclear release, locating suspicious smells or response to emergency incidents. Limited sensing resources and time constraints mean that deciding on how to act in order to improve efficiency of estimation is also of significant interest. This paper has two main focuses: a sequential Monte Carlo technique for performing source term estimation from gas concentration measurements taken on a mobile sensor platform and a Monte Carlo tree search (MCTS) framework to perform sensor motion planning to maximise Kullback-Leibler divergence (KLD). Both algorithms are implemented in the open source tracking and estimation framework: Stone Soup, creating several key contributions to this Python based toolkit. The presented algorithm demonstrates superior performance when compared to a greedy myopic alternative when considering source position estimation error, release rate error and successful rate performance measures.

Robotic gas distribution mapping improves the understanding of a hazardous gas dispersion while putting the human operator out of danger. Generating an accurate gas distribution map quickly is of utmost importance in situations such as gas leaks and industrial incidents, so that the efficient use of resources in response to incidents can be facilitated. In this paper, to incorporate the operational requirement on map granularity, we propose a low-to-high resolution path planner that first guides a single robots to quickly and sparsely sample the region of interest to generate a low resolution gas distribution map, followed by high resolution sampling informed by the low resolution map as a prior. The low resolution prior acts as a coverage survey allowing the algorithm to perform a relatively exploitative search of high concentration regions, resulting in overall shorter mission times. The proposed framework is designed to iteratively identify the next best T locations to sample, which prioritises the potentially high reward locations, while ensuring that the robot can travel to and sample the chosen locations within a user specified map update cycle. We present a simulation study to demonstrate the alternating exploration-exploitation like behaviour along with bench-marking its performance in contrast to the traditional sampling path planners and various reward functions.

A congestion-aware path planning method is pre-sented for mobile robots during long-term deployment in human occupied environments. With known spatial-temporal crowd patterns, the robot will navigate to its destination via less congested areas. Traditional traffic-aware routing methods do not consider spatial-temporal anomalies of macroscopic crowd behaviour that can deviate from the predicted crowd spatial distribution. The proposed method improves long-term path planning adaptivity by integrating a partially updated memory (PUM) model that utilizes observed anomalies to generate a multi-layer crowd density map to improve estimation accuracy. Using this map, we are able to generate a path that has less chance to encounter the crowded areas. Simulation results show that our method outperforms the benchmark congestion-aware routing method in terms of reducing the probability of robot's proximity to dense crowds.

This article addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. A degradation variable described by the Wiener process is proposed to describe the phenomenon of sensor degradation that is often encountered in engineering practice. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed, which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed, where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements.

A method was previously developed by this author to optimise the flight path of a fixed wing UAV performing aerial surveys of complex concave agricultural fields. This relies heavily on a flight time in wind prediction model as its cost function. This paper aims to validate this model by comparing flight test results with the model prediction. There are a number of assumptions that this model relies on. The major assumption is that wind is steady and uniform over the small area and time scales involved in a survey. To show that this is reasonable, wind fields measurements will be taken from a multi rotor UAV with an ultrasonic windspeed sensor.

As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs' autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then, we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. An indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically.