Robot Route Research Articles

Urban mobile robot routing using fast search random tree method (RRT) in obstacle environments

Urban mobile robot routing using fast search random tree method (RRT) in obstacle environments

Efficient routing in robotic movable fulfillment systems with integer programming: A rolling horizon and heuristic approach

This paper addresses an integrated rack assignment and robot routing problem arising in robotic movable fulfillment systems (RMFS). This NP-hard planning task goes beyond current literature by simultaneously optimizing movable rack selection and multi-agent collision-free path finding, rather than decomposing them. A mixed integer programming (MIP) model with a new level-space-time network representation is proposed, jointly considering reusable racks, robot-rack pairings, storage repositioning, and collision avoidance. To improve computational efficiency, a fast rolling horizon heuristic and greedy algorithm are developed. Extensive experiments demonstrate that the integrated method's solutions can improve by 30 % upon conventional decomposed approaches. Intriguing test cases reveal the model, suggesting non-intuitive robot carryover policies that are unfound by separate selection and routing methods. This indicates potential optimization benefits from explicitly coordinating task assignment, scheduling, and routing decisions in complex automated warehousing systems. The rolling horizon heuristic solutions approach optimality with much greater efficiency than directly solving one large MIP, validating its practical value. This research provides useful integrated modeling insights, efficient solution algorithms, and decision support for efficiently controlling next-generation robotic movable fulfillment systems.

Design of Routes for Collaborative Robots in the Automobile Painting Process through a Comparison of Perturbative Heuristics for Iterated Local Search

Design of Routes for Collaborative Robots in the Automobile Painting Process through a Comparison of Perturbative Heuristics for Iterated Local Search

Dynamic robot routing optimization: State–space decomposition for operations research-informed reinforcement learning

There is a growing interest in implementing artificial intelligence for operations research in the industrial environment. While numerous classic operations research solvers ensure optimal solutions, they often struggle with real-time dynamic objectives and environments, such as dynamic routing problems, which require periodic algorithmic recalibration. To deal with dynamic environments, deep reinforcement learning has shown great potential with its capability as a self-learning and optimizing mechanism. However, the real-world applications of reinforcement learning are relatively limited due to lengthy training time and inefficiency in high-dimensional state spaces. In this study, we introduce two methods to enhance reinforcement learning for dynamic routing optimization. The first method involves transferring knowledge from classic operations research solvers to reinforcement learning during training, which accelerates exploration and reduces lengthy training time. The second method uses a state–space decomposer to transform the high-dimensional state space into a low-dimensional latent space, which allows the reinforcement learning agent to learn efficiently in the latent space. Lastly, we demonstrate the applicability of our approach in an industrial application of an automated welding process, where our approach identifies the shortest welding pathway of an industrial robotic arm to weld a set of dynamically changing target nodes, poses and sizes. The suggested method cuts computation time by 25% to 50% compared to classic routing algorithms.

A new industrially magnetic capsule MedRobot integrated with smart motion controller

Capsule medical robots with unique application advantages have broad prospects for gastrointestinal detection, targeted drug delivery, medical assistance, and other fields. However, due to the complexity of the gastrointestinal environment and the limitations of the space magnetic field, robust motion control of capsule robots is still a challenge. Using a permanent magnet (NdFeB) as a space magnetic source, a capsule robot manipulation instrument with an integrated motion intelligent control approach is proposed in this work. First of all, a steering fixture controlling a permanent magnet is designed, and a manipulation instrument capable of five degrees of motion freedom is built with low cost and high accuracy. Furthermore, an integrated motion control approach, consisting of a primary motion subsystem and an auxiliary motion subsystem, is presented, where the motion route of the capsule robot is planned. A parallel optimization strategy is adopted to improve the traditional Gray Wolf Optimization Algorithm, the improved version of which is utilized to tune the parameters of controllers. Finally, some experiments of the designed capsule robot are carried out in different planes and slopes, as well as simulated stomach and real pig stomach, respectively. The results show that the motion characteristics are continuous and stable, and the average position error is 4.2 mm, which meets the application requirements.

Order sequencing, tote scheduling, and robot routing optimization in multi-tote storage and retrieval autonomous mobile robot systems

This study explores a novel multi-tote storage and retrieval autonomous mobile robot system, where multi-tote autonomous mobile robots transport totes (or ‘SKU bins’) for order picking. We investigate the joint optimisation problem of order sequencing, tote scheduling, and robot routing in a single workstation equipped with the capacity to accommodate multiple order bins and parallel totes. The inbound and outbound streams of SKUs stored in totes are crucial for order picking at the workstation, as they jointly affect the picking performance efficiency through synchronization with the order sequence. To address this synchronization problem, we formulate a Mixed-Integer Linear Programming (MILP) model and propose a two-stage hybrid heuristic combining a variable neighbourhood search (VNS) algorithm and a refinement model. This model further improves the VNS solution with partially fixed SKU inbound stream and problem-tailored inequalities. Our numerical studies highlight the superior performance of the proposed hybrid heuristic, where the VNS solution surpasses the benchmark VNS-Simplified by a significant margin of 14%. The rearrangement process enhances the VNS performance by reducing 33.8% of SKU revisits. We also offer managerial insights, suggesting that the maximum number of order bins and parallel totes, exhibit boundary points where adding capacity yields limited marginal improvements.

Analysis and Construction of Hardware Accelerators for Calculating the Shortest Path in Real-Time Robot Route Planning

This study introduces an optimization approach for calculating the shortest path in mobile robot route planning. The proposed solution targets real-time processing requirements by offering a high-performance alternative. This is achieved by embedding in the dedicated hardware an architecture which emphasizes parallelism. Through improvements in parallel exploration techniques, our solution aims to present not only a boost in performance but also a dynamic adaptation to graph changes, accommodating randomly occurring edge insertions or deletions as environmental conditions fluctuate. We present the developed architecture alongside its results. Our method efficiently updates obstacle matrices, resulting in a remarkable 120-fold improvement for 1024-node graphs. When utilizing a cost-effective device like the Cyclone IV E, it achieves approximately 12 times the performance of software applications.

Detecting Environmental Conditions in Cultivation Lands Using Bionic Devices

Climate change has an impact on crops, fruits, vegetables, and pest infestation, hence agricultural output is a top priority for most nations. As a result, professional growers have the problem of reaching maximum output results, and greenhouses have emerged as an excellent choice for ensuring these results. Farmers can employ innovative technologies inside greenhouses to prevent insect damage to plants and increase indoor growth through climate management. However, in order to manage agricultural fields and greenhouses successfully, farmers must now use Industry 4.0 technology such as robotics, Internet of Things devices, machine learning software, and so on. In this setting, sensor deployment is critical for gathering data and obtaining knowledge to help farmers make decisions. As a practical option for small farms, this research proposes an autonomous robot that drives along greenhouse crop paths with previously specified routes and can collect environmental data provided by a wireless sensor network when the farmer has no prior knowledge of the crop. An unsupervised learning method is used to cluster the optimal, standard, and deficient sectors of a greenhouse in order to identify improper crop development patterns. Finally, a user interface is built to assist farmers in planning the robot's route and distance while gathering sensor data to monitor crop conditions.

Глобальное планирование маршрута мобильного робота на основе графовых методов

The problem of global route planning for a mobile robot between two given points in a known area with static obstacles is considered. To solve the problem of constructing a route in an area with a large number of obstacles of complex shape, an integrated approach based on graph theory methods is proposed. It includes the Voronoi diagram, visibility graph and the Dijkstra's algorithm. At the first stage, the study area is represented as a polygonal object, the space outside the object is considered as obstacles. Next, to get a safe distance from obstacles, an internal buffer of the polygonal object is built using the Minkowski difference. Then the vertices of the polygon are compacted, and the Voronoi polygons are constructed from the resulting vertices. The median axis of the polygon is calculated from the Voronoi polygons. Then the Dijkstra's algorithm is applied to calculate the shortest path. The resulting path is used to construct a visibility graph, and the Dijkstra's algorithm is reapplied to the resulting graph. The proposed approach allows to build a route that is optimal in terms of length and distance to obstacles. It significantly reduces the computational complexity of constructing a visibility graph. The approach was implemented in the freely distributed QGIS geographic information system for planning the route of a mobile robot in an aquatic environment. The results of the experiment showed that the Voronoi diagram reduced the number of vertices required to construct the visibility graph by 8.3 times, while the visibility graph improved the path obtained from the Voronoi diagram by 8%. The proposed approach can be used for global planning of routes for mobile robots in various environments.

Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment

AbstractThe loading and unloading operations of smart logistic application robots depend largely on their perception system. However, there is a paucity of study on the evaluation of Lidar maps and their SLAM algorithms in complex environment navigation system. In the proposed work, the Lidar information is finetuned using binary occupancy grid approach and implemented Improved Self-Adaptive Learning Particle Swarm Optimization (ISALPSO) algorithm for path prediction. The approach makes use of 2D Lidar mapping to determine the most efficient route for a mobile robot in logistical applications. The Hector SLAM method is used in the Robot Operating System (ROS) platform to implement mobile robot real-time location and map building, which is subsequently transformed into a binary occupancy grid. To show the path navigation findings of the proposed methodologies, a navigational model has been created in the MATLAB 2D virtual environment using 2D Lidar mapping point data. The ISALPSO algorithm adapts its parameters inertia weight, acceleration coefficients, learning coefficients, mutation factor, and swarm size, based on the performance of the generated path. In comparison to the other five PSO variants, the ISALPSO algorithm has a considerably shorter path, a quick convergence rate, and requires less time to compute the distance between the locations of transporting and unloading environments, based on the simulation results that was generated and its validation using a 2D Lidar environment. The efficiency and effectiveness of path planning for mobile robots in logistic applications are validated using Quanser hardware interfaced with 2D Lidar and operated in environment 3 using proposed algorithm for production of optimal path.

Path Planning of a Mobile Delivery Robot Operating in a Multi-Story Building Based on a Predefined Navigation Tree.

Planning the path of a mobile robot that must transport and deliver small packages inside a multi-story building is a problem that requires a combination of spatial and operational information, such as the location of origin and destination points and how to interact with elevators. This paper presents a solution to this problem, which has been formulated under the following assumptions: (1) the map of the building's floors is available; (2) the position of all origin and destination points is known; (3) the mobile robot has sensors to self-localize on the floors; (4) the building is equipped with remotely controlled elevators; and (5) all doors expected in a delivery route will be open. We start by defining a static navigation tree describing the weighted paths in a multi-story building. We then proceed to describe how this navigation tree can be used to plan the route of a mobile robot and estimate the total length of any delivery route using Dijkstra's algorithm. Finally, we show simulated routing results that demonstrate the effectiveness of this proposal when applied to an autonomous delivery robot operating in a multi-story building.

Constraint programming approaches to electric vehicle and robot routing problems

Constraint programming approaches to electric vehicle and robot routing problems

Virtual grid layout with direction constraints for autonomous mobile robot routing performance improvement

Virtual grid layout with direction constraints for autonomous mobile robot routing performance improvement

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work’s main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future.

Robotic management of isolated endometriosis of sciatic nerve: a reproducible approach that can guide through the labyrinth of pelvic neuroanatomy

To present the robotic, standardized, and reproducible surgical technique we routinely use in our center to manage isolated endometriosis of the sciatic nerve. Surgical video article. Tertiary referral center. A 36-year-old woman suffering from left-sided sciatica pain was diagnosed with an isolated endometriotic nodule of the left sciatic nerve at preoperative assessment. The patient included in this video gave consent for publication of the video and posting of the video online, including on social media, the journal website, scientific literature websites (such as PubMed, ScienceDirect, and Scopus), and other applicable sites. Complete removal of the isolated endometriotic nodule of the sciatic nerve may be performed through a stepwise robotic approach. The surgery starts laterally with the opening of the iliolumbar space between the external iliac vessels and the psoas muscle, as well as the identification of the genitofemoral and obturator nerves. The lumbosacral trunk and emergence of the sciatic nerve were then identified medially and caudally to the obturator nerve. The surgery moves medially with the anterograde dissection of both the internal iliac artery and vein, which allows a safe approach to the posterior and medial limits of the nodule. Ligation of branches of internal iliac vessels directed toward the nodule may be necessary during this step. Isolation and ligation of obturator vessels are frequently required to obtain a bloodless dissection of the lateral limit of the nodule from the lateral pelvic wall. The complete removal of the nodule was then achieved using an alternating approach to all limits of the nodule previously identified, with subsequent release of the sciatic nerve. Description of the relevant pelvic neuroanatomy and the evaluation of robotic routes in the field of pelvic neurosurgery. The use of standardized techniques together with the advantages of a robotic route can make the radical excision of isolated endometriosis of the sciatic nerve reproducible, feasible, and safe. Because of the complexity of neuroanatomy and the risk of severe complications, this surgery remains challenging, and patients affected by deep infiltrating endometriosis involving retroperitoneal neural structures should be referred to multidisciplinary management in expert centers.

An Intelligent Path Planning Mechanism for Firefighting in Wireless Sensor and Actor Networks

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using Wireless Sensor and Actor Networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy Q-learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for a rapid and effective fire response. When compared to the traditional QL, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

Optimization of Mobile Robotic Relay Operation for Minimal Average Wait Time

This paper considers trajectory planning for a mobile robot which persistently relays data between pairs of far-away communication nodes. Data accumulates stochastically at each source, and the robot must move to appropriate positions to enable data offload to the corresponding destination. The robot needs to minimize the average time that data waits at a source before being serviced. We are interested in finding optimal robotic routing policies consisting of 1) locations where the robot stops to relay (relay positions) and 2) conditional transition probabilities that determine the sequence in which the pairs are serviced. We first pose this problem as a non-convex problem that optimizes over both relay positions and transition probabilities. To find approximate solutions, we propose a novel algorithm which alternately optimizes relay positions and transition probabilities. For the former, we find efficient convex partitions of the non-convex relay regions, then formulate a mixed-integer second-order cone problem. For the latter, we find optimal transition probabilities via sequential least squares programming. We extensively analyze the proposed approach and mathematically characterize important system properties related to the robot’s long-term energy consumption and service rate. Finally, through extensive simulation with real channel parameters, we verify the efficacy of our approach.

Optimization of the Storage Spaces and the Storing Route of the Pharmaceutical Logistics Robot

Auto drug distribution systems are used popularly to replace pharmacists when drugs are distributed in pharmacies. The Cartesian robot is usually used as the recovery mechanism. Under non-dynamic storage location conditions, generally, the selected planning route of the Cartesian robot is definite, which makes it difficult to optimize. In this paper, storage spaces were distributed for different drugs, and the route of storing was broken down into multiple path optimization problems for limited pick points. The path was chosen by an improved ant colony algorithm. Experiments showed that the algorithm can plan an effective storing route in the simulation and actual operation of the robot. The time spent on the route by improved ant colony algorithm sequence (IACS) was less than the time spent of route by random sequence (RS) and the time spent of route by traditional ant colony algorithm sequence (ACS); compared with RS, the optimized rate of restoring time with iacs can improve by 22.04% in simulation and 7.35% in operation. Compared with ACS, the optimized rate of restoring time with iacs was even more than 4.70% in simulation and 1.57% in operation. To the Cartesian robot, the optimization has certain guiding significance of the application on the 3D for improving quality.

Vaginal Natural Orifice Transluminal Endoscopic Surgery Revolution: The Next Frontier in Gynecologic Minimally Invasive Surgery.

Despite guidelines that assert that the vaginal route for benign hysterectomy is preferred as the most minimally invasive approach, rates of vaginal hysterectomy remain very low in the United States. Vaginal natural orifice transluminal endoscopic surgery (vNOTES) might reverse the trend. Potential advantages of vNOTES compared to traditional laparoscopic and robotic approaches include the potential for less pain, decreased operative time, improved cosmesis, and decreased risks. Importantly, vNOTES might allow for the conversion of laparoscopic and robotic routes back to vaginal due to surgeon factors.

Dynamic Robot Routing and Destination Assignment Policies for Robotic Sorting Systems

Dynamic Robot Routing and Destination Assignment Policies for Robotic Sorting Systems