An Improved Ant Colony Optimization Based on an Adaptive Heuristic Factor for the Traveling Salesman Problem

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc. In this paper, an improved ACO algorithm based on an adaptive heuristic factor (AHACO) is proposed to deal with the TSP. In the AHACO, three main improvements are proposed to improve the performance of the algorithm. First, the k-means algorithm is introduced to classify cities. The AHACO provides different movement strategies for different city classes, which improves the diversity of the population and improves the search ability of the algorithm. A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm. The proposed algorithm is tested in numerical experiments using 39 TSP instances, and results shows that the solution quality of the AHACO is 83.33% higher than that of the comparison algorithms on average. For large-scale TSP instances, the algorithm is also far better than the comparison algorithms.

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties. A novel feature of the method is the use of a deep meta-learning framework to learn a prior energy model, which can efficiently adapt to the local terrain conditions based on small quantities of exteroceptive and proprioceptive data. A meta-adaptive heuristic function is also proposed for the integration of the energy model into an A* path planner. The performance of the proposed approach is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains, and compared with alternative machine learning solutions. We provide evidence of the advantages of the proposed method to adapt to unforeseen terrain conditions, thereby yielding more informed estimations and energy-efficient paths, when navigating on unknown terrains.<div>Submitted for revision to IEEE Transaction on Cybernetics.</div>

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties. A novel feature of the method is the use of a deep meta-learning framework to learn a prior energy model, which can efficiently adapt to the local terrain conditions based on small quantities of exteroceptive and proprioceptive data. A meta-adaptive heuristic function is also proposed for the integration of the energy model into an A* path planner. The performance of the proposed approach is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains, and compared with alternative machine learning solutions. We provide evidence of the advantages of the proposed method to adapt to unforeseen terrain conditions, thereby yielding more informed estimations and energy-efficient paths, when navigating on unknown terrains.<div>Submitted for revision to IEEE Transaction on Cybernetics.</div>

Security robot for the prevention of workplace violence using the non-linear adaptive heuristic mathematical model

BACKGROUND: Nowadays, workplace violence is found to be a mental health hazard and considered a crucial topic. The collaboration between robots and humans is increasing with the growth of Industry 4.0. Therefore, the first problem that must be solved is human-machine security. Ensuring the safety of human beings is one of the main aspects of human-robotic interaction. This is not just about preventing collisions within a shared space among human beings and robots; it includes all possible means of harm for an individual, from physical contact to unpleasant or dangerous psychological effects. OBJECTIVE: In this paper, Non-linear Adaptive Heuristic Mathematical Model (NAHMM) has been proposed for the prevention of workplace violence using security Human-Robot Collaboration (HRC). Human-Robot Collaboration (HRC) is an area of research with a wide range of up-demands, future scenarios, and potential economic influence. HRC is an interdisciplinary field of research that encompasses cognitive sciences, classical robotics, and psychology. RESULTS: The robot can thus make the optimal decision between actions that expose its capabilities to the human being and take the best steps given the knowledge that is currently available to the human being. Further, the ideal policy can be measured carefully under certain observability assumptions. CONCLUSION: The system is shown on a collaborative robot and is compared to a state of the art security system. The device is experimentally demonstrated. The new system is being evaluated qualitatively and quantitatively.

An ensemble dynamic optimization based inverse adaptive heuristic critic in IaaS cloud computing for resource allocation

In the recent years increase in computer and mobile user’s, data storage has become a priority in all fields. Large- and Small-Scale businesses today thrive on their data and they spent a huge amount of money to maintain this data. Cloud Storage provides on– demand availability of IT services via Large Distributed Data Centers over High Speed Networks. Network Virtualization is been considered as a recent proliferation in cloud computing which emerges as a Multifaceted method towards future internet by facilitating shared resources. Provisioning of the Virtual Network is considered to be a major challenge in terms of creating NP hard problems, minimization of workflow processing time under control resource etc. In order to cope up with the challenges our work has proposed an Ensemble Dynamic Optimization based on Inverse Adaptive Heuristic Critic (IAHC) for overcoming the virtual network provisioning in cloud computing. Our approach gets observed from Expert Observation and provides an approximate solution when various workflows arrives online at various Window Time (WT). It also provides an Optimal Policy for predicting the effect of Resource Allocation of one task for Present as well as Future time Windows. In order to the above approaches it also avoids the high sample complexity and maintains the cost while scaling up to provide Resource Provision. Therefore, our work achieves an adequate policy towards Resource Allocation, reduces the Cost as well as Energy Consumption and deals with real time uncertainties to avoid the Virtual Network provisioning.

Path planning of mobile robot based on adaptive ant colony algorithm

The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.

Path planning for unmanned wheeled robot based on improved ant colony optimization

This research presents a simple and novel improved ant colony optimization for path planning of unmanned wheeled robot. Our main concern is to avoid the random deadlock situation and to reach at the destination using the shortest path, to decrease lost ants and improve the efficiency of solutions. The aforementioned reasons, we design an adaptive heuristic function by adopting the Euclidean distance between the ant and the target destination, in order to avoid the initial blindness and later singleness of ant path searching. The historical best path when appropriate to retain the previous effort would supersede the current worst path. Simulation results under random maps show that the improved ant colony optimization considerably increases the number of effective ants. During the searching process, the probability to find the optimal path increases, as well as the search speed. Moreover, we also compare the improved ant colony optimization performance with the simple ant colony optimization.