Navigating autonomous ground robots through un-structured and uneven terrains without relying on prior maps poses significant challenges due to irregular slopes, occlusions, and stability constraints. Path planning algorithms such as Agoraphilic* employ fixed force shaping functions to influence the robot’s movement, but these static strategies can lead to inefficient paths or trapping in cluttered environments. To address these limitations, this paper presents a novel dynamic force shaping method that adapts in real time to the robot’s local free space distribution. The proposed method utilizes a bell-shaped membership function whose form is dynamically adjusted using two parameters derived from the surrounding free space forces. When the path to the goal is unobstructed, the function sharpens to focus the navigation force toward the goal. Conversely, in constrained or cluttered areas, the function broadens to bias movement toward alternative, safer escape routes. Experiment results across diverse terrain scenarios confirm that the dynamic shaping method enhances both efficiency and robustness, enabling safer and more adaptive mapless navigation. This advancement significantly strengthens the Agoraphilic* algorithm applicability in complex real-world environments.

This paper introduces a novel path planning algorithm, Agoraphilic*, designed to address the challenges in uneven terrain environments. Unlike the traditional Agoraphilic algorithm, which is limited to navigating in 2D planes, the new algorithm extends the traditional Agoraphilic algorithm capabilities to multi-planar terrains. Building upon the basic principles of the Agoraphilic algorithm, Agoraphilic* utilizes free space searching and generates attractive forces based on available free spaces and the goal direction. While the traditional Agoraphilic algorithm estimates free spaces using distance data captured from 2D plane distance sensors, such as 2D LiDAR or ultrasonic sensors, Agoraphilic* algorithm estimates free spaces based on terrain profiles captured from 3D LiDAR or depth cameras. This adaptation equips the algorithm to navigate effectively in uneven terrain environments. By redefining the traditional Agoraphilic algorithm’s basic stages based on terrain profiles, Agoraphilic* algorithm facilitates local path planning in multi-terrain environments. The effectiveness of the proposed algorithm was validated through computer simulation.

This research presents a novel approach to the Agoraphilic local path planning algorithm by integrating semantic segmentation for enhanced terrain identification and free space navigation in complex environments. Traditional Agoraphilic algorithms, although effective in free space navigation, often struggle to accurately identify untraversable terrains such as mud or water, mistakenly considering them as navigable in ground robots. By leveraging a self-trained YOLOv8-seg network for semantic segmentation, our method identifies and labels traversable free spaces, such as roads, grass planes, footpaths and sand areas, using image input. This enhancement is applied across the core modules of the traditional Agoraphilic algorithm, resulting in a novel approach for effective navigation across challenging terrain conditions. The proposed semantic segmentation-based free space identification method is experimentally tested in real-world environments, and simulation tests validate the effectiveness of the improved Agoraphilic algorithm. This advancement represents a significant improvement in autonomous robot navigation, particularly in challenging and uneven terrains.

The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitations, namely its failures to account for initial conditions and to guarantee the temporal causality – a fundamental property of dynamical systems. This paper proposes a novel causal deep operator network (Causal-DeepONet) architecture for incorporating both the initial condition and the temporal causality into data-driven learning of dynamical systems, overcoming the limitations of the original DeepONet approach. This is achieved by adding an independent root network for the initial condition and independent branch networks conditioned, or switched on/off, by time-shifted step functions or sigmoid functions for expressing the temporal causality. The proposed architecture was evaluated and compared with two baseline deep neural network methods and the original DeepONet method on learning the thermal dynamics of a room in a building using real data. It was shown to not only achieve the best overall prediction accuracy but also enhance substantially the accuracy consistency in multistep predictions, which is crucial for predictive control.

The paper presents a practical approach for planning trajectories for multiple vehicles where both collision avoidance and minimum travelling time are simultaneously considered. It is first proposed to exploit the mixed-integer programming (MIP) approach to formulate the collision avoidance paradigm, where the linear dynamic models are utilized to derive the linear constraints. Moreover, travelling time of each vehicle is compromised among them and set to be minimized so that all the vehicles can practically reach the expected destinations at the shortest time. Unfortunately, the formulated optimization problem is NP-hard. In order to effectively address it, we propose to employ the alternating direction method of multipliers (ADMM), which can share the computational burdens to distributive optimization solvers. Thus, the proposed method can enable each vehicle to obtain an expected trajectory in a practical time. Convergence of the proposed algorithm is also discussed. To verify effectiveness of our approach, we implemented it in a numerical example, where the obtained results are highly promising.

In this paper, we present a novel approach for building an automation testing platform for a mobile game, namely Woody, by using machine learning algorithms. There are three essential components in our proposed system, including a testing platform, a game state recognition algorithm, and game agents. We implement our testing platform by using the Airtest IDE to have the possibility of testing multiple devices (e.g., tablets, iOS phones, and Android phones). For each screenshot taken on a testing device, we use the Adaptive Gaussian Thresholding algorithm to detect the game board and use the Mean Square Error function to predict the extract status of three blocks given in each turn. After that, we use a reinforcement learning approach for training three different levels of game agents to imitate the playing behaviors of different users in the game. The experimental results show that both game state recognition algorithms and game agents work very well. The results of this paper can give an additional contribution to the research community on using machine learning in the mobile gaming industry.

This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising.

Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising.

In order to achieve the ever increasing quantity and quality demands for agricultural products, technological innovations must be explored. In most cases, low cost and high quality sensing options are a top priority. This paper addresses the problem of predicting soil organic matter content in an agriculture field using information collected by a low-cost network of mobile, wireless and noisy sensors that can take discrete measurements in the environment. In this context, it is proposed that the spatial phenomenon of organic matter in soil to be monitored is modeled using Gaussian processes. The proposed model then enables the wireless sensor network to estimate the soil organic matter field at all unobserved locations of interest. The estimated values at predicted locations are highly comparable to those at corresponding points on a realistic image that is aerially taken by a very expensive and complex remote sensing system.

This paper addresses the problem of selecting the most informative sensor locations out of all possible sensing positions in predicting spatial phenomena by using a wireless sensor network. The spatial field is modeled by Gaussian Markov random fields (GMRFs), where sparsity of the precision matrix enables the network to benefit from computation. A new spatial sensor selection criterion is proposed based on mutual information (MI) between random variables at selected locations and those at unselected locations and interested but unlikely sensor placed positions, which enhances resulting prediction. The GMRF-based optimality criterion is then proven to be computationally and efficiently resolved, especially in a large-scale sensor network, by a polynomial time approximation algorithm. More importantly, with demonstrations of monotonicity and submodularity properties of the MI set function in the proposed selection criterion, our near-optimal solution is also guaranteed by at least within(1-1/e) of the optimal performance. The effectiveness of the proposed approach is compared and illustrated using two real-life large data sets with promising results.

This paper presents a distributed spatial estimation and prediction approach to address the centrally-computed scheme of Gaussian Process regression at each robotic sensor in resource-constrained networks of mobile, wireless and noisy agents monitoring physical phenomena of interest. A mobile sensor independently estimate its own parameters using collective measurements from itself and local neighboring agents as they navigate through the environment. A spatially-distributed prediction algorithm is designed utilizing methods of Jacobi over-relaxation and discrete-time average consensus to enable a robotic sensor to update its estimation of obtaining the global model parameters and recursively compute the global goal of inference. A distributed navigation strategy is also considered to drive sensors to the most uncertain locations enhancing the quality of prediction and learning parameters. Experimental results in a real-world data set illustrate the effectiveness of the proposed approach and is highly comparable to those of the centralized scheme.

This paper proposes a data driven machine learning model for spatial prediction of hydrogen sulfide (H2S) in a gravity sewer system. The gaseous H2S in the overhead of the gravity sewer is modelled using a Gaussian Process with a new covariance function due to constraints of sewer boundaries. The covariance function is proposed based on the distance between two locations computed along the lengths of the sewer network. A mutual information based strategy is used to choose the best k sensor measurements and their locations from among n potential sensor observations and their locations. This provably NP-hard combinatorial sensor selection problem is addressed by maximizing the mutual information between the selected locations and the locations that are not selected or do not have any sensor deployments. A proof-of-concept study was carried out comparing the spatial prediction of H2S with a complex model currently used by Sydney Water. The proposed approach is shown to be effective in both modelling and predicting the H2S spatial concentrations in sewers as well as identifying optimal number of H2S sensors and their locations for a required level of prediction accuracy.

This paper addresses the issue of monitoring physical spatial phenomena of interest utilizing the information collected by a network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. The spatial phenomenon is statistically modelled by a Gaussian Markov Random Field (GMRF) with hyperparameters that are learnt as the measurements accumulate over time. In this context, the GMRF approximately represents the spatial field on an irregular lattice of triangulation by exploiting a stochastic partial differential equation (SPDE) approach, which benefits remarkably in computation due to the sparsity of the precision matrix. A technique of the one-step-ahead forecast is employed to predict the future measurements that are required to find the optimal sampling locations. It is shown that optimizing the sampling path problem with the logarithm of the determinant either of a covariance matrix using a GP model or of a precision matrix using a GMRF model for mobile robotic wireless sensor networks (MRWSNs) even by a greedy algorithm is impractical. This paper proposes an efficient novel optimality criterion for the adaptive sampling strategy to find the most informative locations in taking future observations that minimize the uncertainty at unobserved locations. The computational complexity of our proposed method is linear, which makes the MRWSN scalable and practically feasible. The effectiveness of the proposed approach is compared and demonstrated using a pre-published data set with appealing results.