Robot Route Research Articles

Obstacle Avoidance and Navigation Planning of a Wheeled Mobile Robot using Amended Artificial Potential Field Method

Trajectory planning is a prime method in the research on mobile robot navigation. Sampling-based algorithms can generate trajectories which help the robot to reach the target avoiding obstacles. In this proposed work, an Enhanced Artificial Potential Field (E-APF) generates the trajectories for mobility of mobile robots and simultaneously guarantees the effectiveness and continuity of the trajectory. Aiming at the problem that the classical APF cannot adapt to the complex trajectory planning and fall a as prey to the local optimal solution, E-APF method is proposed for Wheeled Mobile Robot (WMR) route planning. In this research work, this method does not consider the influence of traditional attraction and repulsive forces. The repulsive potential is built by repulsive function for discretizing outline of an arbitrarily shaped obstacle with their boundary points. This process describes the workspace of the wheeled mobile robot more precisely. The reliability is proved for most of the cases by discussing the convergence of this proposed technique. Finally, an efficient obstacle avoidance based action has been performed in the chosen navigable trajectory. Trajectories that have been generated using the proposed E-APF satisfy constraints approach of the direction on both the starting and goal points. Consequently, the trajectories that are generated by the Wheeled Mobile Robot are geometrically and dynamically feasible. Simulation results performed confirm the viability of the proposed E-APF algorithm that it can be effectively utilized in trajectory planning of wheeled mobile robots and can be applied in real-time scenarios.

Solving an Agricultural Robot Routing Problem with Binary Particle Swarm Optimization and a Genetic Algorithm

Solving an Agricultural Robot Routing Problem with Binary Particle Swarm Optimization and a Genetic Algorithm

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Continuous learning route map for robot navigation using a growing-on-demand self-organizing neural network

This article proposes an experience-based route map continuous learning method and applies it into robot planning and navigation. First of all, the framework for robot route map learning and navigation is designed, which incorporates the four cyclic processes of planning, motion, perception, and extraction, enabling robot to constantly learn the information of the road experience and to obtain and improve the route map of the environment. Besides, a growing-on-demand self-organizing neural network learning algorithm is also proposed. This algorithm is based on growing neural gas algorithm, but it does not require presetting of network scale, and under the condition of dynamically growing input data, it can regulate the increase scale of network online in a self-adaptive and self-organized manner to obtain stable learning results. Finally, with robot roaming in an environment, this algorithm is used to conduct continuous learning of dynamically increasing route information, extract the topological structur...

Review Of Image Processing Technique On Biometric System

Object tracking in occlusion is the boon for our modern world. With the help of it, we can save our national property. We can prevent crime. It provides us healthy environment. Video observation is a dynamic examination theme in PC vision that tries to identify, perceive and track objects over an arrangement of pictures and it likewise makes an endeavor to comprehend and depict object conduct by supplanting the maturing old conventional technique for checking cameras by human administrators. Object recognition and following are vital and testing assignments in numerous PC vision applications, for example, investigation, vehicle route, and self-governing robot route. Object recognition includes finding objects in the edge of a video arrangement. Each tracking technique requires an article discovery instrument either in each edge or when the object first shows up in the video. Object tracking in occlusion is the procedure of finding an object or various objects after some time utilizing a camera.

Robot-assisted laparoscopic transperitoneal infrarenal lymphadenectomy in patients with locally advanced cervical cancer by single docking: Do we need a backup procedure?

To present our initial experience on the feasibility of robotic transperitoneal para-aortic lymphadenectomy up to left renal vein via single docking approach by high port insertion technique followed by left shoulder docking as a rescue backup procedure in surgically obstructed patients undergoing surgical staging because of locally advanced cervical cancer (LACC). Prospective observational preliminary study. Canadian Task Force classification II-3. Tertiary-care academic affiliated private hospital. Ten patients with LACC who underwent robotic transperitoneal infrarenal para-aortic lymphadenectomy between January 2012 and December 2014. All patients with pathologically proven cervical cancer underwent a PET/CT scanning in a similar fashion at the department of nuclear medicine. PET/CT scans were evaluated by the nuclear medicine specialist. Following pre-operative work-up, robot-assisted transperitoneal infrarenal para-aortic lymphadenectomy was performed up to left renal vein by the same experienced surgeon. Sections of 5mm were performed and stained with routine hematoxylin and eosin (H&E), and node count was done separately by experienced gynecopathologist. During the study period, 12 consecutive patients with LACC were counseled for pre-therapeutic robot-assisted transperitoneal para-aortic lymphadenectomy. Two patients declined the procedure and underwent standardized chemo-radiation therapy whereas remaining ten patients constituted the study group. In the study group, the median age was 46years (range 33-59years), and the median body mass index 28.5kg/m2 (range 18.5-35.1kg/m2). Clinical staging was stage IIB in four patients, IIIB in four, and IVA in one. Histopathological diagnosis was squamous cell carcinoma in nine patients, and adenocarcinoma in one. On PET/CT scans, seven out of ten patients were positive for pelvic lymph node metastasis. With respect to para-aortic area, only one of the ten patients had suspected metastasis in PET/CT. For nine patients with LACC, the median docking time was 6.5min (range 4-15min), and the median operating time for para-aortic lymphadenectomy was 120min (range 60-165min). The median trocar time was 14.5min (range 5-45min). In two out of ten patients, the surgical removal of whole lymphatic tissue between inferior mesenteric artery and left renal vein was not completely possible by a single docking of robotic column. Therefore, a new optic trocar was placed in the umbilicus and the robotic column was relocated over the left shoulder of the patient and residual lymphatic tissue measuring approximately 2cm in the long axis immediately below the left renal vein was removed and the surgery was completed up to the left renal vein. All para-aortic lymphadenectomies have been completed by robotic route. There were no intra-operative complications. No patient received a blood transfusion. Early post-operative grade 2 and 3a complications according to Dindo classification occurred in two patients: one symptomatic lymphocyst and one local infection on assistant port site in one patient. The patient with suspected para-aortic lymph node metastasis in PET/CT showed no metastatic disease on histopathologic exam of para-aortic lymph nodes. The patient with recurrent disease and negative para-aortic lymph nodes on frozen section examination underwent robot-assisted total pelvic exenteration. Five of the residual eight patients had histologically proven metastasis in the para-aortic lymph node(s). Treatment modification occurred in six patients related to pre-treatment staging surgery. According to pathological results, extended field radiation therapy has been added in five patients and it was omitted in one patient. The median time interval between surgery and initiation of radiotherapy was 12days (range 6-23days). Robotic transperitoneal infrarenal para-aortic lymphadenectomy up to left renal vein by high port insertion technique is a safe and feasible option for staging and treatment planning. However, technically, it is obstructed in a small group of patients and nodal staging surgery up to left renal vein can be completed by consecutive left shoulder docking approach as a backup rescue plan.

Comparison of Robotic and Other Minimally-Invasive Routes of Hysterectomy for Benign Indications

Background Despite a lack of evidence showing improved clinical outcomes with robotic-assisted hysterectomy over other minimally invasive routes for benign indications, this route has increased in popularity over the last decade. Objective We sought to compare clinical outcomes and estimated cost of robotic-assisted vs other routes of minimally invasive hysterectomy for benign indications. Study Design A statewide database was used to analyze utilization and outcomes of minimally invasive hysterectomy performed for benign indications from Jan. 1, 2013, through July 1, 2014. A 1-to-1 propensity score-match analysis was performed between women who had a hysterectomy with robotic assistance vs other minimally invasive routes (laparoscopic and vaginal, with or without laparoscopy). Perioperative outcomes, intraoperative bowel and bladder injury, 30-day postoperative complications, readmissions, and reoperations were compared. Cost estimates of hysterectomy routes, surgical site infection, and postoperative blood transfusion were derived from published data. Results In all, 8313 hysterectomy cases were identified: 4527 performed using robotic assistance and 3786 performed using other minimally invasive routes. A total of 1338 women from each group were successfully matched using propensity score matching. Robotic-assisted hysterectomies had lower estimated blood loss (94.2 ± 124.3 vs 175.3 ± 198.9 mL, P P P = .007), and shorter length of stay (14.1% [189] vs 21.9% [293] ≥2 days, P P = .01) and driven by lower rates of superficial surgical site infection (0.07% [1] vs 0.7% [9], P = .01) and blood transfusion (0.8% [11] vs 1.9% [25], P = .02). Major postoperative complications, intraoperative bowel and bladder injury, readmissions, and reoperations were similar between groups. Using hospital cost estimates of hysterectomy routes and considering the incremental costs associated with surgical site infections and blood transfusions, nonrobotic minimally invasive routes had an average net savings of $3269 per case, or 24% lower cost, compared to robotic-assisted hysterectomy ($10,160 vs $13,429). Conclusion Robotic-assisted laparoscopy does not decrease major morbidity following hysterectomy for benign indications when compared to other minimally invasive routes. While superficial surgical site infection and blood transfusion rates were statistically lower in the robotic-assisted group, in the absence of substantial reductions in clinically and financially burdensome complications, it will be challenging to find a scenario in which robotic-assisted hysterectomy is clinically superior and cost-effective.

An efficient bicriteria algorithm for stable robotic flow shop scheduling

We consider a flow shop for processing single type of parts serviced by a single robot. The robot transportation times are allowed to have small perturbations. We treat the robotic flow shop scheduling problem considering stability of its schedule where the robot route is fixed and the processing durations of parts are to be specified from given intervals. The stability radius of a schedule is defined as the largest quantity of variations in the transportation times within which the schedule can still be executed as expected. We consider the bicriteria optimization problem which consists of minimizing the cycle time and maximizing the stability radius. The objective is to handle the two criteria simultaneously, that is, to find their Pareto front. We propose a new strongly polynomial algorithm for finding the minimum cycle times for all possible values of stability radius with time complexity of O(m4), where m is the number of processing machines in the flow shop. This implies that we can find the entire Pareto front of the problem in O(m4) time.

Detecting static and dynamic novelties using dynamic neural network

This paper presents a dynamic neural network based novelty filter where a mobile robot explored in an environment and built a dynamic model of the robot normal sensory-motor values perceived from the environment. Afterwards, the acquired model of normality was used on the robot to predict expected values of the sensory-motor inputs during the patrolling. Novelties could be detected if the prediction error between model-predicted values and real observed values exceeded a certain novelty threshold. Because of high uncertainty while robot interactions with the environment, the network-prediction errors along the robot route were not normally distributed to define only one novelty threshold. Therefore region-specific novelty thresholds were estimated by the proposed novelty detection system. As a result, the robot is capable of selecting a local novelty threshold depending on where the robot currently occupied.To evaluate the proposed system, a set of real-world robotic experiments were carried out. Experimental results showed that the novelty filter was able to highlight unusual static and dynamic objects in the environment. Furthermore, the filter also produced reliable local novelty thresholds while the robot patrolled in the noisy environment.

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT–MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be processed on a set of alternative machines and additionally have to be transported between them by several transport robots. Hence, the FJSPT–MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the flexible job shop scheduling problem and the robot routing problem. This paper proposes hybrid metaheuristics based on clustered holonic multiagent model for the FJSPT–MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using three sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

Minimization of the production cycle in the presence of loops in the robot route

Minimization of the production cycle in the presence of loops in the robot route

Zmodyfikowana metoda pól potencjałowych do wyznaczania drogi robota mobilnego

Zmodyfikowana metoda pól potencjałowych do wyznaczania drogi robota mobilnego

Computer Vision for Mobile On-Ground Robotics

Autonomous mobile robotics needs reliable information on relief of underlying surface and location of obstacles. Planning the route of a mobile on-ground robot supposes mapping of a visible area with separating it into zones of good or conditional pass ability, impassability, and indefinite zones. It needs recognition of standard objects (marking, traffic signs) and types of surface (snow, sand, or water) as sources of evident or hidden obstacles.3D calculation requires large computer resources and leads to delays, limiting the velocity. Contouring of boundaries simplifies image decomposition to objects and defines key tasks of mapping such as image vectorization and recognition of objects. They must be divided as in algorithms so at a hardware level. An idea of process multisequencing results in division of data processing on several processors. Along with vector analysis of obstacles it leads to radical cut of 3D update time, ensuring the data supply for high-speed motion of robots.

Robot Routing Using Clustering-Based Parallel Genetic Algorithm with Migration

Robot Routing Using Clustering-Based Parallel Genetic Algorithm with Migration

Linguistic decision making for robot route learning.

Machine learning enables the creation of a nonlinear mapping that describes robot-environment interaction, whereas computing linguistics make the interaction transparent. In this paper, we develop a novel application of a linguistic decision tree for a robot route learning problem by dynamically deciding the robot's behavior, which is decomposed into atomic actions in the context of a specified task. We examine the real-time performance of training and control of a linguistic decision tree, and explore the possibility of training a machine learning model in an adaptive system without dual CPUs for parallelization of training and control. A quantified evaluation approach is proposed, and a score is defined for the evaluation of a model's robustness regarding the quality of training data. Compared with the nonlinear system identification nonlinear auto-regressive moving average with eXogeneous inputs model structure with offline parameter estimation, the linguistic decision tree model with online linguistic ID3 learning achieves much better performance, robustness, and reliability.

Self-Tuning RBFNs Mobile Robot Systems through Bacterial Foraging Particle Swarm Optimization Learning Algorithm

A radial basis function neural networks (RBFNs) mobile robot control system is automatically developed with the image processing and learned by the bacterial foraging particle swarm optimization (BFPSO) algorithm in this paper. The image-based architecture of robot model is self-generated to travel the routing path in the dynamical and complicated environments. The visible omni-directional image sensors capture the surrounding environment to represent the behavior model of the mobile robot system. Three parameterize RBFNs model with the centers and spreads of each radial basis function, and the connection weights to solve the mobile robot path traveling and routing problems. Several free parameters of radial basis functions can be automatically tuned by the direct of the specified fitness function. In additional, the proper number of radial basis functions of the constructed RBFNs can be chosen by the defined fitness function which takes this factor into account. The desired multiple objectives of the RBFNs control system are proposed to simultaneously approach the shorter path and avoid the unexpected obstacles. Evaluations of PSO and BFPSO show that the developed RBFNs robot systems skip the obstacles and efficiently achieve the desired targets as soon as possible.

Mobile Robot for Life Science Automation

The paper presents a control system for mobile robots in distributed life science laboratories. The system covers all technical aspects of laboratory mobile robotics. In this system: (a) to get an accurate and low-cost robot localization, a method using a StarGazer module with a number of ceiling landmarks is utilized; (b) to have an expansible communication network, a standard IEEE 802.11g wireless network is adopted and a XML-based command protocol is designed for the communication between the remote side and the robot board side; (c) to realize a function of dynamic obstacle measurement and collision avoidance, an artificial potential field method based on a Microsoft Kinect sensor is used; and (d) to determine the shortest paths for transportation tasks, a hybrid planning strategy based on a Floyd algorithm and a Genetic Algorithm (GA) is proposed. Additionally, to make the traditional GA method suitable for the laboratory robot's routing, a series of optimized works are also provided in detail. Two experiments show that the proposed system and its control strategy are effective for a complex life science laboratory.

Better Approximation Algorithms for Grasp-and-Delivery Robot Routing Problems

Better Approximation Algorithms for Grasp-and-Delivery Robot Routing Problems

Performance Optimisation of Mobile Robots for Search-and-Rescue

This paper presents a team performance optimisation system for multiple mobile robots in search-and-rescue operations, in which refugees are first discovered and subsequently robots are dispatched to transport themto shelters. Coordination of mobile robots involves two fundamental issues, namely task allocation and motion planning. While task allocation assigns jobs to robots, motion planning generates routes for robots to execute the assigned jobs. Task allocation and motion planning together play a pivotal role in optimisation of the robot team performance. These two issues become more challenging in dynamic search-and-rescue environments, where the refugees are unpredictably discovered at different locations and the traffic conditions of rescue zones keep changing. Weaddress these two issues by proposing an auction-based closed-loop module for task allocation and a bio-inspired intelligent module for motion planning. The task allocation module is characterised with a closed-loop bid adjustment mechanism to improve the bid accuracy even in light of stochastic rescue requests. The motion planning module is bio-inspired intelligent in that it features detection of imminent neighbours and responsiveness of virtual force navigation in dynamic traffic conditions. Simulations show that the proposed system is a practical tool to optimise the dynamic operations of search-and-rescue by a team of mobilerobots.