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.

This paper presents a novel machine learning-based Agoraphilic (free space attraction) navigation algorithm. The proposed algorithm is capable of undertaking local path planning for mobile robots in unknown dynamic environments with a moving goal. The inability to track and reach a moving goal is one of the common weaknesses of most existing navigation algorithms operating in dynamic environments. High uncertainty involved in dynamic environments is also another major challenge. The novel machine learning-based approach helps the proposed algorithm to successfully overcome these challenges. This paper also introduces the integrated modular-based architecture for free-space attraction-based algorithms. This allows the algorithm to incorporate ten different modules with miscellaneous algorithms to perform sub-tasks such as tracking, prediction, map generation, machine learning-based free space attraction force generation and robot motion command generation. The new modular-based architecture integrates those sub-modules to create the robot's driving force. This driving force is the single attractive force to pull the robot towards the moving goal via current free space leading to future free space passages. The proposed algorithm was experimentally tested under a dynamic environment. The experiment was focused on testing the behaviour of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that the Agoraphilic algorithm is successful in reaching a moving goal in an unknown dynamically cluttered environment. © 2022 IEEE.

This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched- marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations.

The six papers in this special section focus on the use of advanced mechatronics in research and industrial applications. Mechatronics has evolved during the last few decades to become well recognized as a philosophy of design and an engineering discipline. It has emerged through the philosophy of concurrent design of mechanics, electronics, and computer of a system for an integrated and efficient approach to system design. Most universities nowadays offer a degree in mechatronics. This stems from the fact that as technology progresses, the traditional barriers between engineering disciplines are dimensioning. Mechatronics evolution went through many stages to reach its current advanced stage.

Minimally invasive surgery (MIS) has many benefits, e.g., unnecessary trauma and blood loss can be minimized by opening tiny surgery scars, recovery process of patients can be lessened, and the possibilities of complications can be decreased. The natural orifice transluminal surgery (NOTES) is one of the MIS surgical methodologies. NOTES surgery is performed using an endoscope. This methodology does not rely on making holes to patients' body to insert the medical instruments. Although some medical robots for NOTES have been proposed, current robots mainly suffer from the absence/limitation of haptic feedback to the surgeon's hands. In order to overcome this problem, this paper proposes electro hydraulic transmission system (EHTS) based forceps. The EHTS is a remote actuation system using fluid energy. By using the EHTS, the classical position and force transmission losses will not be influenced by the tube's shape. The validity of the proposal and its advantages have been experimentally verified and presented in this paper.

System reliability modeling needs a large amount of data to estimate the parameters. In addition, reliability estimation is associated with uncertainty. This paper aims to propose a new method to evaluate the failure behavior and reliability of a large system using failure modes, effects, and criticality analysis (FMECA). Therefore, qualitative data based on the judgment of experts are used when data are not sufficient. The subjective data of failure modes and causes have been aggregated through the system to develop an overall failure index (OFI). This index not only represents the system reliability behavior, but also prioritizes corrective actions based on improvements in system failure. In addition, two optimization models are presented to select optimal actions subject to budget constraint. The associated costs of each corrective action are considered in risk evaluation. Finally, a case study of a manufacturing line is introduced to verify the applicability of the proposed method in industrial environments. The proposed method is compared with conventional FMECA approach. It is shown that the proposed method has a better performance in risk assessment. A sensitivity analysis is provided on the budget amount and the results are discussed.

A functional prototype of a friction-based object slippage detection gripper for robotic grasping and manipulation has been designed and built. Object grasping and manipulation experiments have been successfully performed to study the appropriateness of the methodology and the newly built slippage detection gripper. The main advantage of this slippage detection method is that slippage detection is an inherent capability of the sensing element, and not a derived capability like that of sensors based on vibration. This slippage detection and control strategy is simple by design and low in cost, but robust in function. It has the potential to be used in a variety of environments such as high temperatures, low temperatures and underwater. The robustness of the design makes it highly suitable for grasping and manipulating safely a large range of object weights and sizes.

This paper presents a newly developed parallel gripper prototype capable of sensing grasp force, tangential force and slippage. In this design the gripper itself is used as part of the sensing strategy rather than just being simply a structural support for other sensors. The sensing capability of this gripper is simple in design and reliable. The sensing strategy can be customised to specific applications such as the ability to handle large loads while maintaining its ability to detect slippage as reliably as when handling lighter loads.

In this paper, dynamic simulation methods for reliability evaluation of common industry-based control system architectures are investigated. Control system design often employs complex reliability structures in the forms of several levels of software and hardware redundancies, hot and cold standby systems. This is required in order to achieve certain plant availability and safety functions. Control system maintenance requires expert knowledge due to the complexity of troubleshooting steps involved with a hardware or software failures of a large system. Hence, it is crucial to understand the effect of recovery time on reliability and on overall availability in a critical control system. Dynamic Fault Tree Analysis (DFTA), Markov Chains and Reliability Block Diagrams (RBD) are presented and a block library is introduced for addressing the aforementioned modelling problems. In order to be able to evaluate dynamic fault trees and Markov Chains, Monte Carlo simulation has been used. An industry-based case study is presented, where critical failures of a redundant Programmable Logic Controller (PLC) system are identified by a Failure Mode and Effect Analysis (FMEA). The bottom up process of modelling control system reliability is discussed.

Optimization of robotic workcells is a growing concern in automated manufacturing systems. This study develops a methodology to maximize the production rate of a multifunction robot (MFR) operating within a rotationally arranged robotic cell. An MFR is able to perform additional special operations while in transit between transferring parts from adjacent processing stages. Considering the free-pickup scenario, the cycle time formulas are initially developed for small-scale cells where an MFR interacts with either two or three machines. A methodology for finding the optimality regions of all possible permutations is presented. The results are then extended to the no-wait pickup scenario in which all parts must be processed from the input hopper to the output hopper, without any interruption either on or between machines. This analysis enables insightful evaluation of the productivity improvements of MFRs in real-life robotized workcells.

The objective of this study is to maximize the production rate of a rotationally arranged robotic cell by finding an optimal move cycle for a multi-function robot (MFR). Using this class of industrial robots makes the cell much more sophisticated than classical one where robot is considered to be only capable of handling material in work cells. MFR is able not only to transfer the part between two adjacent processing stages but also to perform a special operation in transit. The cycle time formulas are developed for small-scale cells where a robot interacts with either two or three machines. Also, this paper presents a methodology for finding the optimality regions of all possible cycles. This analysis results in enhancing the managerial skill for evaluating the productivity improvements of MFRs in robotized workcells.

A reliability optimization model is proposed in this paper for multi-state weighted k-out-of-n systems. In this model, income generated by components through each functioning period is used as a reliability index. Therefore, time value of money is used in the presented optimization model to estimate both system's reliability and cost. The system reliability is evaluated by Universal Generating Function (UGF). The model's objective function is to maximize the net present value (NPV) of the system. Therefore, it would maximize the system reliability and minimize the system cost simultaneously. A numerical example is presented in this paper to illustrate the model by finding the optimal design of the system, and the best time for maintenance plan. Also, a discussion is provided based on the result.

The current objective of this robotics research study is mainly to develop a cheap and not cumbersome systems for special applications. A PROMIS (Pprime RObot for Minimally Invasive Surgery) system is designed with this philosophy. The developed system is composed of master, slave and control system for collaborative operation between the surgeon and the robot. A parallel robot haptic device is used as master to teleoperate a serial robot with three degrees of freedom (dof). The advantage of this method is the possibility of coupling master and slave with non-matching kinematics through a kinematic transformation. The developed control system architecture is presented in this paper. The control architecture was designed to control the teleoperation system where the master and slave robots do not have homothetic kinematics. Finally, the viability of the developed control is demonstrated by the experimental results.

In tele-operation applications, especially in surgery, haptic devices need to exhibit a high degree of rigidity and accuracy. This paper deals with the development of a spherical parallel manipulator (SPM) that enables the satisfaction of those characteristics. The parallel architecture enables the use of this kind of robot as master in a tele-operation system. Moreover, the SPM has a center of rotation that makes it a natural candidate and more adapted to minimally invasive surgery application. However, the parallel manipulator presents a poor behavior in singular configuration.

This paper presents an investigative study in which data were gathered and used in underground mining to improve the planned maintenance program and reliability of the shearer loader in the underground mining. A cost effective maintenance and operation strategy and practices is mandatory to meet the production demand and the required level of service of this critical asset of the plant. The study conducted and presented in this paper includes a detailed review of failure history data and the use of analytical technique available to understand failure characteristics and its effect on the throughput and the overall performance of the longwall operation. This is to achieve further productivity increases to meet business goals. Analytical tools such as Failure Mode and Effect and Criticality Analysis (FMECA) and Weibull analysis were used in this investigation. The study has highlighted the criticality of some failures and the actions needed in this industrial case to improve the reliability and planned maintenance for a better mining productivity.