Collision prediction based q-learning for mobile robot navigation in unknown dynamic environments

Q-learning (QL) approach is constantly used for mobile robot (MR) navigation in unknown dynamic environment because of its simplicity and well-developed theory. However, its salient downside is the curse of dimensionality problem, where it incurs a huge computational power and memory requirement. This problem is aggravated in complex environments. In this paper, a collision prediction based QL (CPQL) scheme is presented to MR navigation in a dynamic environment based on collision prediction between the robot and a group of static and dynamic obstacles. In the proposed scheme, a novel definition of environment states is presented to apply QL to unknown dynamic environments with compact state space, satisfactory robot turning angles, and adequate speed gradation. The key feature of the proposed CPQL scheme pertains to constructing a state—action pair based on two factors. The first factor is the region of predicting the position of collision between the robot and an obstacle, and the second is the region of the obstacle related to robot position. Simulation analysis and results show the superiority of CPQL in terms of learning convergence, obstacle avoidance, and smooth navigation path compared with state-of-the-art MR navigation schemes. Hence, CPQL proves its authenticity and suitability for real-time navigation in complex and dynamic environments

Collision prediction based genetic network programming-reinforcement learning for mobile robot navigation in unknown dynamic environments

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance

Genetic network programming-reinforcement learning based safe and smooth mobile robot navigation in unknown dynamic environments

Aims: The aims of this study were to identify the Fusarium isolates based on translation elongation factor (tef) 1α sequence, to determine the genetic diversity among isolates and species using selected microsatellite markers and to examine the pathogenicity of Fusarium isolates causing fruit rot disease of banana. Methods and Results: One‐hundred and thirteen microfungi isolates were obtained from fruit rot infected banana in Peninsular Malaysia. However, this study was focused on the dominant number of the discovered microfungi that belongs to the genus Fusarium; 48 isolates of the microfungi have been identified belonging to 11 species of Fusarium, namely Fusarium incarnatum, Fusarium equiseti, Fusarium camptoceras, Fusarium solani, Fusarium concolor, Fusarium oxysporum, Fusarium proliferatum, Fusarium verticillioides, Fusarium sacchari, Fusarium concentricum and Fusarium fujikuroi. All Fusarium isolates were grouped into their respective clades indicating their similarities and differences in genetic diversity among isolates. Out of 48 Fusarium isolates tested, 42 isolates caused the fruit rot symptom at different levels of severity based on Disease Severity Index (DSI). The most virulent isolate was F. proliferatum B2433B with DSI of 100%. Conclusions: All the isolated Fusarium species were successfully identified and some of them were confirmed as the causal agents of pre‐ and postharvest fruit rot in banana across Peninsular Malaysia. Significance and Impact of the Study: Our results will provide additional information regarding new report of Fusarium species in causing banana fruit rot and in the search of potential biocontrol agent of the disease

A discrete-event traffic simulation model for multilane-multiple intersection

In this paper, the model is simplified by proposing a decomposed queuing theory to multilane-multiple intersection. The concept of queuing theory looks promising due to its simplicity and its combination with standard approach techniques. This can expand the model to be more reliable in dealing with problem associated to multilane-multiple intersections. The proposed model is simple, flexible and fit to any real case study with different structure of topology. The traffic model is developed using discrete-event simulation (DES) framework for computation and analysis the model. Simulation results have shown good correlation between the proposed traffic model and data taken from real traffic situations, and have confirmed that the proposed modeling technique based on queuing theory and standard decomposition method can be realized. In addition, simulation results give a confirmation of the model capability to correctly predict traffic performance measures

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications