Turn and Orientation Sensitive A* for Autonomous Vehicles in Intelligent Material Handling Systems

Autonomous mobile robots are taking on more tasks in warehouses, speeding up operations and reducing accidents that claim many lives each year. This work proposes a dynamic path planning algorithm, based on A* search method for large autonomous mobile robots such as forklifts, and generates an optimized, time-efficient path. Simulation results of the proposed turn and orientation sensitive A* algorithm show that it has a 94% success rate of computing a better or similar path compared to that of default A*. The generated paths are smoother, have fewer turns, resulting in faster execution of tasks. The method also robustly handles unexpected obstacles in the path

Voronoi-based trajectory optimization for UGV path planning

© 2017 IEEE. Optimal path planning in dynamic environments for an unmanned vehicle is a complex task of mobile robotics that requires an integrated approach. This paper describes a path planning algorithm, which allows to build a preliminary motion trajectory using global information about environment, and then dynamically adjust the path in real-time by varying objective function weights. We introduce a set of key parameters for path optimization and the algorithm implementation in MATLAB. The developed algorithm is suitable for fast and robust trajectory tuning to a dynamically changing environment and is capable to provide efficient planning for mobile robots

Analysis and Development of Computational Intelligence based Navigational Controllers for Multiple Mobile Robots

Navigational path planning problems of the mobile robots have received considerable attention over the past few decades. The navigation problem of mobile robots are consisting of following three aspects i.e. locomotion, path planning and map building. Based on these three aspects path planning algorithm for a mobile robot is formulated, which is capable of finding an optimal collision free path from the start point to the target point in a given environment. The main objective of the dissertation is to investigate the advanced methodologies for both single and multiple mobile robots navigation in highly cluttered environments using computational intelligence approach. Firstly, three different standalone computational intelligence approaches based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Cuckoo Search (CS) algorithm and Invasive Weed Optimization (IWO) are presented to address the problem of path planning in unknown environments. Next two different hybrid approaches are developed using CS-ANFIS and IWO-ANFIS to solve the mobile robot navigation problems. The performance of each intelligent navigational controller is demonstrated through simulation results using MATLAB. Experimental results are conducted in the laboratory, using real mobile robots to validate the versatility and effectiveness of the proposed navigation techniques. Comparison studies show, that there are good agreement between them. During the analysis of results, it is noticed that CS-ANFIS and IWO-ANFIS hybrid navigational controllers perform better compared to other discussed navigational controllers. The results obtained from the proposed navigation techniques are validated by comparison with the results from other intelligent techniques such as Fuzzy logic, Neural Network, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and other hybrid algorithms. By investigating the results, finally it is concluded that the proposed navigational methodologies are efficient and robust in the sense, that they can be effectively implemented to solve the path optimization problems of mobile robot in any complex environment

An Optimal and Energy Efficient Multi-Sensor Collision-Free Path Planning Algorithm for a Mobile Robot in Dynamic Environments

There has been a remarkable growth in many different real-time systems in the area of autonomous mobile robots. This paper focuses on the collaboration of efficient multi-sensor systems to create new optimal motion planning for mobile robots. A proposed algorithm is used based on a new model to produce the shortest and most energy-efficient path from a given initial point to a goal point. The distance and time traveled, in addition to the consumed energy, have an asymptotic complexity of O(nlogn), where n is the number of obstacles. Real time experiments are performed to demonstrate the accuracy and energy efficiency of the proposed motion planning algorithm.https://doi.org/10.3390/robotics602000

Fast path planning for precision weeding

Agricultural robots have the potential to reduce herbicide use in agriculture and horticulture through autonomous precision weeding. One of the main challenges is how to efficiently plan paths for a robot arm such that many individual weeds can be processed quickly. This paper considers an abstract weeding task among obstacles and proposes an efficient online path planning algorithm for an industrial manipulator mounted to a mobile robot chassis. The algorithm is based on a multi-query approach, inspired by industrial bin-picking, where a database of high-quality paths is computed offline and paths are then selected and adapted online. We present a preliminary implementation using a 6-DOF arm and report results from simulation experiments designed to evaluate system performance with varying database and obstacle sizes. We also validate the approach using a Universal Robots UR5 manipulator and ROS interface

Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system

Coverage and connectivity are the two main functionalities of wireless sensor network. Stochastic node deployment or random deployment almost always cause hole in sensing coverage and cause redundant nodes in area. In the other hand precise deployment of nodes in large area is very time consuming and even impossible in hazardous environment. One of solution for this problem is using mobile robots with concern on exploration algorithm for mobile robot. In this work an autonomous deployment method for wireless sensor nodes is proposed via multi-robot system which robots are considered as node carrier. Developing an exploration algorithm based on spanning tree is the main contribution and this exploration algorithm is performing fast localization of sensor nodes in energy efficient manner. Employing multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor nodes deployment. A novel improvement of this technique in deployment of nodes is having obstacle avoidance mechanism without concern on shape and size of obstacle. The results show using spanning tree exploration along with multi-robot system helps to have fast deployment behind efficiency in energy

ENG 1001G-012: Composition and Language

Koordiniranim gibanjem više mobilnih robota umjesto jednoga može se postići značajno povećanje brzine, točnosti i učinkovitosti izvođenja zadataka u velikom broju primjena. Primjeri takvih primjena mogu se naći u inteligentnim transportnim sustavima, zadacima prekrivanja prostora, kooperativnom prijenosu tereta, logističkim sustavima, proizvodnim pogonima itd. Sigurno gibanje mobilnih robota u formaciji konvoj u inteligentnim transportnim sustavima moguće je ako se zadovolji stabilnost konvoja. Stabilnost konvoja modela razlike brzine postignuta je odgođenim povratnim informacijama koje uključuje razliku trenutačne i odgođene udaljenosti između mobilnih robota i razliku trenutačne i odgođene brzine mobilnog robota. Postizanje izvršenja zadataka prekrivanja prostora i kooperativnog prijenosa tereta u najkraćem mogućem vremenu vrlo je izazovan problem. Riješen je planiranjem referentne vremenski optimalne trajektorije formacije mobilnih robota u zakrivljenom koordinatnom sustavu pomoću algoritma optimalnog skaliranja u vremenu, koji vremenski optimalno izmjenjuje najveća i najmanja ubrzanja formacije. Koordinirano gibanje više mobilnih robota u istom radnom prostoru u logističkim sustavima i proizvodnim pogonima ostvareno je primjenom pristupa razdvojenog planiranja gibanja uz unaprijed definirane putanje mobilnih robota i dodavanjem početnog vremena kašnjenja izračunanim predefiniranim trajektorijama mobilnih robota. Slijeđenje zadanih trajektorija za mobilne robote s diferencijalnim pogonom postignuto je algoritmom s nepromjenjivim koeficijentima zasnovanim na kinematičkom modelu mobilnog robota, nelinearnoj dinamici pogreške slijeđenja i Lyapunovljevoj teoriji stabilnosti.Nowadays, the use of mobile robots has become increasingly important for many purposes: medical services, civil transport, domestic work, military, commercial cleaning, sales of consumer goods, agricultural and forestry work, browsing hard to reach and dangerous areas for human, digging ore, construction work, loading/unloading and manipulating materials, outer space and underwater research, space supervision, entertainment etc. New challenges in the field of mobile robotics include controlling a group of mobile robots in an efficient way. Inspiration is found in nature where many animal communities apply cooperative behaviour patterns to achieve a common goal. The most common arguments for the application of a group of mobile robots in comparison to only one mobile robot are increase in speed and accuracy and efficiency of performing tasks. A group of mobile robots is able to execute tasks impossible for a single mobile robot, e.g. transporting or repositioning large objects. Also, a group of mobile robots is more robust to failure than a single mobile robot (one mobile robot can take over the tasks of another mobile robot in case of failure). Since mobile robots can have a variety of roles, the same group of mobile robots can be employed for many different objectives, e.g. intelligent transport systems, areas coverage, cooperative transportation, logistics, etc. In order to achieve the benefits of multi-robot mobile systems it is necessary to solve additional problems in terms of control, which are not present in systems with a single mobile robot, e.g. communication between robots, distribution of tasks, assigning priorities, taking into account kinematic and dynamic constraints of all the mobile robots in the group to successfully plan feasible paths and trajectories for all of them, etc. A mobile robot is a mechanical system and as such is subject to motion equations that follow the laws of physics. Therefore, for each movement, in accordance with kinematic and dynamic constraints, there must be at least one set of input values that affect the motion. Kinematic constraints of mobile robots are the result of limitations in movement of drive wheels and drive configurations. Dynamic constraints of mobile robots refer to limiting the permitted velocity and acceleration, and they are caused by the actuator limitations. Motion control of mobile robots is a complex process. It includes working task planning, reference path and trajectory planning, and tracking the reference trajectory. The focus of this thesis is put on multi-robot land mobile systems and applications in intelligent traffic systems, areas coverage, cooperative transportation of large objects and coordination in the common working environment. All algorithms presented in this thesis are first tested in Matlab® and then experimentally validated on real robots. Experiments were performed using robot soccer platform at the Department of Control and Computer Engineering, Faculty of Electrical Engineering and Computing, University of Zagreb, which is ideal for testing various mobile robot algorithms. It consists of a team of five radio-controlled micro robots of size 0.075 m cubed with differential drive. The playground is of size 2.2×1.8 m. Above the centre of the playground, Basler a301fc IEEE-1394 Bayer digital colour camera with resolution of 656×494 pixels and with maximal frame rate of 80 fps is mounted perpendicular to the playground. The height of the camera to the playground is 2.40 m. A wide angle 6 mm lens is used. Although robot soccer platform is very practical for experiments with formations of mobile robots, it has some technical limitations: (i) relatively large noise in the measured position and velocity of the robot; (ii) delay in the communication between the control computer and microprocessors of the mobile robots and (iii) delay in measurements due to vision (the time required to grab the image from the camera and the time required for image processing). Efficient motion of mobile robots (vehicles) in a convoy formation is very important in transportation of people and goods. The key research problem is ensuring the string stability. In the background of this study are traffic safety and adaptive cruise control system in modern vehicles. The adaptive cruise control system primarily aims to reduce the driver’s effort, which is achieved by controlling the velocity of vehicle according to a predefined control law. Typically, sensors such as radar and lidar are used to measure the relative distance and relative velocity between vehicles. The working principle of adaptive cruise control system is based on these informations. String stability refers to the stability of a series of ''interconnected'' vehicles. The attribute ''interconnected'' does not indicate the physical connection of the vehicles but vehicles moving in a convoy formation where every vehicle in the convoy follows the preceding vehicle at a safe distance. The behaviour of each vehicle in the convoy must be such that the oscillatory behaviour due to a change of velocity of the leading vehicle of the convoy does not increase towards the end of the convoy. Otherwise, unstable convoy may cause collisions between vehicles. In this thesis, a deterministic microscopic full velocity difference model is considered. In the case where the values of model parameters do not meet the requirement of string stability, full velocity difference model should be expanded with additional control signals in order to satisfy string stability. Two additional control signals are based on the difference of the current and the delayed (in a defined time in the past) relative distance between the vehicle, and the difference of the current and the delayed vehicle velocity. The proposed method for achieving string stability is based on the delayed-feedback control. The delayed-feedback control is one of the feasible methods of controlling unstable and chaotic motions. The string stability has been examined by ∞–norm of transfer function of distance between vehicles. The stability of the transfer function is examined using a delay-independent stability criterion, which significantly simplifies the stability test, since characteristic equation of transfer function is transcendental and has infinite number of roots. It should be noted that the adaptive cruise control system is studied in one-dimensional space, e.g. vehicles in the convoy moving on an infinitely long straight road. Using formations of mobile robots, it is possible to significantly increase efficiency of numerous tasks such as areas coverage or cooperative transportation of large objects. However, achieving minimal time of executing tasks is a very challenging problem. The formation of mobile robots observed in this study is most similar to the formation with a leading mobile robot, but instead of a leading mobile robot the reference point of the formation and its reference path are defined. The formation of mobile robots is defined in the curved coordinate system so mobile robots maintain distance in relation to a reference point formation in this coordinate system. The curvature of the coordinate system is actually instantaneous centre of curvature of the formation’s reference trajectory. This results in changing the formation shape during cornering. This has implications in possible applications. For example, a square formation of mobile robots cannot handle rectangular solid object as it moves in curved path. The dynamic model of the mobile robot takes into account intrinsic constraints originating from the robot actuator limits and extrinsic constraints resulting from the limited adhesion force between ground and wheels of the mobile robot such as wheel slip and tip over of the mobile robot. Both types of constraints are important for planning at high velocities. The problem of planning reference time-optimal trajectory for a formation of mobile robot is solved by decoupled approach which could be defined as a problem of planning reference time-optimal trajectories after predefined smooth G2 continuous path of the reference point of formation. First, a path of formation of mobile robots in workspace is found, and then a velocity profile in accordance with the specified criteria and dynamic constraints of mobile robots is calculated. It is assumed that the path of each mobile robot in the formation is feasible and that kinematic constraints of the mobile robot on the path are satisfied. G2 continuous path provides the ability to pass the mobile robot trajectory with a non-zero velocity, thus enabling fast movement of the mobile robot without stopping. G2 path consists of straight lines and clothoids. Planning reference time-optimal trajectory for the formation of mobile robots is based on the optimal time-scaling algorithm providing that formation always moves at its maximum or minimum accelerations. This means that at least one mobile robot of the formation moves at its maximum or minimum accelerations. Emphasis is placed on static formations of mobile robots. Also, one example of dynamic formation of the mobile robots was shown. When multiple mobile robots independently perform tasks in the same workspace, the key problem is the planning collision free coordinated motion of multiple mobile robots sharing the same workspace. This problem is solved using the decoupled approach. First step is to plan individual path of each mobile robot, e.g. predefined path, using methods for planning paths for individual mobile robots in workspace with static obstacles. Second step is to plan a velocity profile for each mobile robot on its predefined path, e.g. predefined trajectory. Third step is to modify predefined trajectory for each mobile robot making sure that collisions between mobile robots in the workspace are avoided. To successfully coordinate motion of multiple mobile robots, a common approach is to assign priority level to each of them. A mobile robot with highest priority takes into account only static obstacles while other mobile robots have to take into account also dynamic obstacles, which are mobile robots with higher priorities. Dynamic obstacles avoidance is based on avoiding time-obstacles in a collision map. Time-obstacles are constructed based on the time mobile robots would spend in possible area of collision. A lower priority mobile robot efficiently avoids time-obstacles, e.g. mobile robot with higher priority level, by inserting a calculated start-up delay time to its predefined trajectory along the predefined path. By applying the same principle to all lower priority robots, a collision free motion coordination of multiple mobile robots can be achieved. It should be noted that the predefined paths of mobile robots do not change during their movements. The change is only in the allocation of time of motion of mobile robots on their predefined paths. A proposed method is computationally fast and intuitive and ensures that always only one mobile robot can be in the possible area of collision. The input of the trajectory tracking algorithm is a feasible planned trajectory, where feasibility refers to the ability of a mobile robot to actually track the planned trajectory. This means that the planned trajectory respects kinematic and dynamic constraints of the mobile robot. The trajectory tracking algorithm is needed because in reality there are many sources of potential errors such as imperfections of a mobile robot model or external disturbances like uneven ground, delayed command control, an imperfect measurement of the state of mobile robots and so on. In this thesis, it is proposed a trajectory tracking algorithm with constant gains for nonholonomic mobile robot with differential drive, which is based on kinematic model of mobile robot, nonlinear dynamics of the tracking error and Lyapunov stability theory. The scientific contributions of the thesis are: 1. Algorithm for mobile robot control in the formation of a convoy that ensures string stability using the delayed-feedback control, based on the difference between current and delayed distance between mobile robots and the difference between current and delayed velocity of a mobile robot. 2. Algorithm for planning smooth time optimal trajectory for a formation of mobile robots with kinematic and dynamic constraints on predefined paths. 3. Algorithm for planning of collision free motion coordination of multiple mobile robots sharing the same workspace on predefined paths assigning initial delay time for predefined trajectories of mobile robots. 4. Algorithm for trajectory tracking for mobile robots with kinematic and dynamic constraints based on Lyapunov stability theory

Marsupial 로봇 팀의 효율적인 배치 및 회수를 위한 경로 계획에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 이범희.This dissertation presents time-efficient approaches to path planning for initial deployment and collection of a heterogeneous marsupial robot team consists of a large-scale carrier robot and multiple mobile robots. Although much research has been conducted about task allocation and path planning of multi-robot systems, the path planning problems for deployment and collection of a marsupial robot team have not been fully addressed. The objectives of the problems are to minimize the duration that mobile robots require to reach their assigned task locations and return from those locations. Taking the small mobile robot's energy constraint into account, a large-scale carrier robot, which is faster than a mobile robot, is considered for efficient deployment and collection. The carrier robot oversees transporting, deploying, and retrieving of mobile robots, whereas the mobile robots are responsible for carrying out given tasks such as reconnaissance and search and rescue. The path planning methods are introduced in both an open space without obstacles and a roadmap graph which avoids obstacles. For the both cases, determining optimal path requires enormous search space whose computational complexity is equivalent to solving a combinatorial optimization problem. To reduce the amount of computation, both task locations and mobile robots to be collected are divided into a number of clusters according to their geographical adjacency and their energies. Next, the cluster are sorted based on the location of the carrier robot. Then, an efficient path for the carrier robot can be generated by traveling to each deploying and loading location relevant to each cluster. The feasibility of the proposed algorithms is demonstrated through several simulations in various environments including three-dimensional space and dynamic task environment. Finally, the performance of the proposed algorithms is also demonstrated by comparing with other simple methods.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.1.1 Multi-robot system 1 1.1.2 Marsupial robot team 3 1.2 Contributions of the thesis 9 Chapter 2 Related Work 13 2.1 Multi-robot path planning 14 2.2 Multi-robot exploration 14 2.3 Multi-robot task allocation 15 2.4 Simultaneous localization and mapping 15 2.5 Motion planning of collective swarm 16 2.6 Marsupial robot team 18 2.6.1 Multi-robot deployment 18 2.6.2 Marsupial robot 19 2.7 Robot collection 23 2.8 Roadmap generation 24 2.8.1 Geometric algorithms 24 2.8.2 Sampling-based algorithms 25 2.9 Novelty of the thesis 26 Chapter 3 Preliminaries 27 3.1 Notation 27 3.2 Assumptions 29 3.3 Clustering algorithm 30 3.4 Minimum bounded circle and sphere of a cluster 32 Chapter 4 Deployment of a Marsupial Robot Team 35 4.1 Problem definition 35 4.2 Complexity analysis 37 4.3 Optimal deployment path planning for two tasks 38 4.3.1 Deployment for two tasks in 2D space 39 4.3.2 Deployment for two tasks in 3D space 41 4.4 Path planning algorithm of a marsupial robot team for deployment 42 4.5 Simulation result 49 4.5.1 Simulation setup 49 4.5.2 Deployment scenarios in 2D space 50 4.5.3 Deployment scenarios in 3D space 57 4.5.4 Deployment in a dynamic environment 60 4.6 Performance evaluation 62 4.6.1 Computation time 62 4.6.2 Efficiency of the path 64 Chapter 5 Collection of a Marsupial Robot Team 67 5.1 Problem definition 68 5.2 Complexity analysis 70 5.3 Optimal collection path planning for two rovers 71 5.3.1 Collection for two rovers in 2D space 71 5.3.2 Collection for two rovers in 3D space 75 5.4 Path planning algorithm of a marsupial robot team for collection 76 5.5 Simulation result 83 5.5.1 Collection scenarios in 2D space 83 5.5.2 Collection scenarios in 3D space 88 5.5.3 Collection in a dynamic environment 91 5.6 Performance evaluation 93 5.6.1 Computation time 93 5.6.2 Efficiency of the path 95 Chapter 6 Deployment of a Marsupial Robot Team using a Graph 99 6.1 Problem definition 99 6.2 Framework 101 6.3 Probabilistic roadmap generation 102 6.3.1 Global PRM 103 6.3.2 Local PRM 105 6.4 Path planning strategy 105 6.4.1 Clustering scheme 106 6.4.2 Determining deployment locations 109 6.4.3 Path smoothing 113 6.4.4 Path planning algorithm for a marsupial robot team 115 6.5 Simulation result 116 6.5.1 Outdoor space without obstacle 116 6.5.2 Outdoor space with obstacles 118 6.5.3 Office area 119 6.5.4 University research building 122 Chapter 7 Conclusion 125 Bibliography 129 초록 151Docto