Deliberative capabilities are essential for intelligent aerial robotic applications in modern life such as package delivery and surveillance. This paper presents a real-time3Dpathplanningsolution for multirotoraerialrobots to obtain a feasible, optimal and collision-freepath in complex dynamicenvironments. High-level geometric primitives are employed to compactly represent the situation, which includes self-situation of the robot and situation of the obstacles in the environment. A probabilistic graph is utilized to sample the admissible space without taking into account the existing obstacles. Whenever a planning query is received, the generated probabilistic graph is then explored by an A discrete search algorithm with an artificial field map as cost function in order to obtain a raw optimal collision-freepath, which is subsequently shortened. Realistic simulations in V-REP simulator have been created to validate the proposed pathplanningsolution, integrating it into a fully autonomous multirotoraerial robotic system.
HCTNav needs less computational resources, especially less memory. Thus, HCTNav can be a good alternative for navigation in low-cost robots. In the work by Duchon et al., 17 both global and local pathplanning problems are tackled. The authors compared five different methods: A , focused D , incremental Phi , Basic Theta* and jump point search (JPS). They concluded that the JPS algorithm achieves near-optimal paths very quickly as compared to the other algorithms. Thus, if the real-time character is imperative in the robot application, JPS is the best choice. If there is no requirement of a realtime and the length of path plays a big role, then Basic Theta* algorithm is rec- ommended. Focused D* and incremental Phi are not appropriate for static environments. They can be used in dynamicenvironments with a small amount of obstacles. Chiang et al. 18 compared two pathplanning algorithms that have the same computational complexity Oðn logðnÞÞ (where n is the number of grid point): the fast marching method (FMM) and A . They tested the two algorithms on grid maps of sizes 40 40 up to 150 150. The authors claimed that A is faster than the other planners and gen- erates continuous but not smooth path, while FMM gener- ates the best path (smoothest and shortest) as the resolution of the map gets finer and finer. Other research works addressed the pathplanning problem in unknown environ- ments. Zaheer et al. 19 made a comparison study of five pathplanning algorithms in unstructured environments (A , RRT, PRM, artificial potential field (APF) and the pro- posed free configuration eigenspaces (FCE) pathplanning method). They analysed the performance of the algorithms in terms of computation time needed to find a solution, the distance travelled and the amount of turning by the auton- omous mobile robot and showed that the PRM technique provides a shorter path than RRT but RRT is faster and produces a smooth path with minimum direction changes
Abstract. This paper presents a new technique for computing collision- freenavigation motions from task-level commands for animated human characters in interactive virtual environments. The algorithm imple- mentation utilizes the hardware rendering pipeline commonly found on graphics accelerator cards to perform fast 2D motion planning. Given a 3D geometric description of an animated character and a level-terrain environment, collision-freenavigation paths can be computed between initial and goal locations at interactive rates. Speed is gained by leverag- ing the graphics hardware to quickly project the obstacle geometry into a 2D bitmap for planning. The bitmap may be searched by any number of standard dynamic programming techniques to produce a ﬁnal path. Cyclic motion capture data is used along with a simple proportional derivative controller to animate the character as it follows the computed path. The technique has been implemented on an SGI Indigo2 worksta- tion and runs at interactive rates. It allows for real-time modiﬁcation of the goal locations and obstacle positions for multiple characters in complex environments composed of more than 15,000 triangles.
considered closest to the robot and the known goal or optimal path position. Choosing the wrong LAP value consistently results in the situation shown in fig. 14. Note that in this case the algorithm will not fail but it will lose efficiency. One possible solution is not to use a LAP until some mapping of the environment has been made. Although a jerkier motion may be generated, the algorithm still works. Choosing the correct values of safety margins and steering angle is also essential. Large safety margins provide the robot with more safety but it is possible that a path may not be found when one exists. It is best if moderate values are used which can then be increased or decreased based on acquired knowledge. In our case the safety margins used were directly related to the speed of the robot.
There are essentially two reasons for taking this simplifying assumption. First, the kinematic model fully captures the essential nonlinearity of single-body wheeled robots, which stems from their nonholonomic nature. This is another fundamental difference with respect to the case of robotic manipulators, in which the main source of nonlinearity is the inertial coupling among multiple bodies. Second, in mobile robots it is typically not possible to directly command the wheel torques, because there are low-level wheel control loops integrated in the hardware or software architecture. Any such loop accepts as input a reference value for the wheel angular speed, which is then reproduced as accurately as possible by standard regulation actions (e.g., PID controllers). In this situation, the actual inputs available for high-level control are precisely these reference velocities.
Abstract: - This paper presents the use of a modified ant colony optimization algorithm and interactive web technologies for the problems of real-timepathplanning and navigation for an autonomous mobile robot. Assume that the mobile robot serves in an office building for delivering documents and packages, and all staff in different locations can assign tasks to the robot via a web-based application. Therefore, how to rapidly determine or update the pathplanning for the robot is a top priority. In this paper, we firstly apply interactive web technologies to develop the web-based application including two interfaces of client-side and server-side. The client-side is a graphical user interface for task assignments and monitoring real-time state of the mobile robot; moreover, the server-side interface combines the computation kernel of a modified ant colony optimization algorithm for generating a feasible pathplanning with a database management system for recording purpose. Secondly, a precise indoor localization system for the mobile robot using wireless sensor network and binocular vision is also presented. Finally, simulation results show that our proposed approach allows users to assign tasks to the mobile robot via the Internet, and then the mobile robot can complete these tasks along a feasible path that is generated by a modified ant colony optimization algorithm.
Résumé : La planification de mouvement est le domaine de l’informatique qui a trait au développement de techniques algorithmiques permettant la génération automatique de trajectoires pour un système mécanique. La nature d’un tel système varie selon les champs d’application. En animation par ordinateur il peut s’agir d’un avatar humanoïde. En biologie moléculaire cela peut être une protéine. Le domaine d’application de ces travaux étant la robotique aérienne, le système est ici un UAV (Unmanned Aerial Vehicle: véhicule aérien sans pilote) à quatre hélices appelé quadrirotor. Le problème de planification de mouvements consiste à calculer une série de mouvements qui amène le système d’une configuration initiale donnée à une configuration finale souhaitée sans générer de collisions avec son environnement, la plupart du temps connu à l’avance. Les méthodes habituelles explorent l’espace des configurations du système sans tenir compte de sa dynamique. Cependant, la force de poussée qui permet à un quadrirotor de voler est par construction parallèle aux axes de rotation des hélices, ce qui implique que certains mouvements ne peuvent pas être effectués. De plus, l’intensité de cette force de poussée, et donc l’accélération linéaire du centre de masse, sont limitées par les capacités physiques du robot. Pour toutes ces raisons, non seulement la position et l’orientation doivent être planifiées, mais les dérivées plus élevées doivent l’être également si l’on veut que le système physique soit en mesure de réellement exécuter le mouvement. Lorsque c’est le cas, on parle de planification kinodynamique de mouvements. Une distinction est faite entre le planificateur local et le planificateur global. Le premier est chargé de produire une trajectoire valide entre deux états du système sans nécessairement tenir compte des collisions. Le second est l’algorithme principal qui est chargé de résoudre le problème de planification de mouvement en explorant l’espace d’état du système. Il
The multi robot pathplanning problem can be categorized to either centralized approach or decoupled planner. The centralized approaches  deal with the robots as a single composite robot and any single robot pathplanning approach can be applied to it to find a solution. Theoretically, it is assumed complete, that it is find optimal solution if one exists. But it suffer from the complexity, that if the number of robots increased then the time needed to find the solution increased exponentially. A global cell decomposition approach was taken by , where in a unified configuration space representation the obstacles and other robots are incorporated. The algorithm first decomposed the free space into cells, and then it searches for a path through the resulting adjacency graph. An attractive potential fields used in  over the workspace which applied to a specific point on the robot body, and then these potentials are combined in configuration space to attract the whole robot toward the desired goal. An algorithm  is proposed to solve centralized multi robot pathplanning specifically on a graph have at least two empty vertices. Two primitives have been employed in this algorithm: push and swap. The former primitive used where a robot moves toward its goal until no progress can be made. The later allows two robots to swap positions without altering the position of any other robot. On the other hand, the decoupled multi robot pathplanning finds the path for each robot independently. It differs from the centralized in the completeness and the complexity. In some problems, there is no guarantee to find a solution even a one is exists. Planning for each robot individually make it less complexity than the centralized approaches. The decoupled approaches also categorized into prioritized [13, 14, 15, 16] planning and path coordination. The prioritized pathplanning was first proposed by , where priorities are assigned to each robot either from motion constraints or randomly.
In chapter 2 are presented the dynamic models of VTOL UAV and di ff erential wheel robot. The VTOL UAV is suitable to model the multi-propeller UAV used in the project, and we propose a novel strategy to model this class of vehicles with a modular approach, where the UAV is seen as composition of payload and actuators modules. This will al- low to model general multi-propeller vehicles with non-standard configurations. About the differential wheel robot, the model used is a standard unicycle model to define the kinematic, which is often su ffi cient to build accurate models and control strategies. In chapter 3 we propose the control law for the two robots of interest. For the VTOL UAV we propose an hybrid control law with a cascade approach, where the continuous position controller is in cascade with the hybrid attitude controller. It allows to over- come a topological obstruction for continuous law on compact manifolds such as SO(3) for the attitude, and allows to robustly execute acrobatic maneuvers such as flips and attitude recovery, useful for the aerial deployment task of the project. About the di ff er- ential wheel robot, a vectored speed control is proposed, with saturation on the velocity input to represent the constraints on the actuator. The proposed control allows to reach a waypoint in a 2D plane starting from any initial configuration or to track a desired position trajectory.
In this paper for the elimination of the above defects, spline interpolation technique is proposed. The nodal point which is given as a point of path will be defined to be a part of smoothed spline function. And numerical simulations are conducted for the pathplanning of the space robot to capture the target, in which the potential by solving the Laplace equation is applied and generates the smooth and continuous path by the spline interpolation from the initial to the final posture.
An AMR navigating through a crowd of people should require no human-operator and be classified as intelligent, Table 2.2. This is only an emergent intelligence, a result of the various interactions between the separate autonomous sub-systems. Harmonising how the data is used at each step is vital to ensure they are all pro- cessing data appropriately, allowing the subsequent stage to function. For an AMR with at least adaptive autonomy it’s movements are not deterministic, as it chooses its own motion patterns. How it behaves whilst in operation and how successfully it reaches its sub-goals and final goal is the important factor . Therefore, to ap- propriately evaluate the success of a AMR’s autonomy requires specific performance metric evaluation. Although evaluation criteria for specific scenarios has been previ- ously created (e.g. search and rescue ), there is no defined test criteria involving pedestrian navigation. The assumption that no collisions must occur can be made for obvious reasons, but how the robot should move through the crowd is completely dependent upon subjective design parameters. In response to this, an updated tax- onomy is proposed in Section 2.4.3, Table 2.5, following a review of how current AMR’s navigate with pedestrians.
We consider the general problem of planning the motion of an autonomous nonholonomic robot in two-dimensional space, the goal being to reach a given configuration while avoiding some set of obstacles. For many applications, the ability of the robot to react quickly is crucial, and a strong emphasis must be placed on the efficiency of pathplanning, which often has to be carried out with the limited amount of computing power available on the robot. The objective is then not to find an optimal solution, but to develop a method that can very quickly synthesize a trajectory for reaching the target in acceptable time, and that is consistent with the physical limitations of the robot (e.g., sufficient clearance is ensured with respect to obstacles, speed and acceleration bounds are respected at all times). In addition, in order to be able to deal with dynamicenvironments, absence of any preprocessing of obstacles information is preferred.
This paper presents a fully autonomous navigationsolution for urban service robots operating in pedestrian areas. In this context, the navigation framework will receive go to queries sent by some upper-level task allocation process, or directly by an operator. The go to query will indicate a goal point on the map coordinate frame. The system is designed as a collection of closely-interrelated modules. Some of them have been applied successfully on other robots during the URUS project demonstrations, while the lower-level modules are geared toward our Segway robots and take into account their special characteristics. The main contribution of this paper is the presentation of a set of techniques and principles that jointly yield a valuable experimental field report: (1) the consideration of real-world urban pedestrian environments, with inherent features such as ramps, steps, holes, and pedestrians and other dynamic obstacles, (2) the use of Segway-based platforms, which provide high mobility but create perception and control issues successfully addressed by our approach, (3) real-time3D localization, without relying on GPS, using state-of-the-art techniques for on-line computation of expected range observations, (4) the successful integration of all navigation software modules for real-time, high-level actions, and (5) extensive field experiments in two real-world urban pedestrian scenarios, accomplishing more than 6 Km of autonomous navigation with a high success rate.
Visibility graph approaches consider obstacle vertices, in the map, to be the nodes through which the robot can reach its desired position. They proceed to connect vertices that are visible to each other. Visible nodes are nodes with the property that straight line joining them does not intersect any obstacles. Vgraphs were first used in robot motion planning by . They guarantee that the robot will find the shortest path to its goal. A V*graph was introduced in , which reduced the number of considered vertices, thus reducing the computational complexity of the algorithm. In , a reduced Vgraph method was coupled with a curved weighed Dijkstra algorithm  and Simulated Annealing for autonomous mining applications. The main disadvantage of this approach is that it plans a route that forces the robot to pass, as close as possible, to any detected obstacles.
actively emitting laser light, and can detect the three-dimensional structure of the environment precisely. It is relatively more stable and is not easily interfered by the environment. Therefore, LiDAR is widely used in the development of smart cars. Researchers have accumulated a lot of experience in the application of LiDAR in the development of smart cars. Fernandes realized the detection of road areas by combining 3D laser point clouds into 2D reference planes by combining upsampling and sliding windows. Zhang first processed the original point cloud data to roughly distinguish between road and non-road areas, and then used the sliding window method to perform real-time edge detection and precise road segmentation. Yecheng Lyu et al. constructed the road segmentation problem as a semantic segmentation task using deep neural networks, and designed the algorithm on the FPGA to implement the real-time processing of the point cloud data. Instead of using dedicated hardware, Kiin chose to optimize the algorithm to improve the performance. They combined the model-based and region-based segmentation methods to implement the real-time drivable region detection under complex urban environments. Optimizing the algorithm avoids the use of additional hardware to guarantee real-time processing, thus reduces cost and makes the entire solution more light-weighted.
The aim of designing the language R EADYLOG is to create a G OLOG dialect which supports the programming of the high-level control of agents or robots in dynamicreal-time domains. The primary application has been robotic soccer. The robotic soccer domain has some specific char- acteristics which made the development of R EADYLOG necessary and influenced several design decisions: the robotic soccer domain is an unpredictable adversarial dynamicreal-time domain. This means that decisions have to be taken quickly and making plans for future courses of actions have a mid-term horizon. Planning ahead for the next minute does not make sense as the world changes unpredictably due to the uncertainty of the outcomes of own actions and the behaviors of the opposing players. The unpredictability of the actions of the agent calls for some notion of un- certainty. Actions succeed with a certain probability p or fail with a probability of 1 − p. Further, the soccer domain is a continuous domain, whereas the world in the situation calculus evolves from situation to situation. The agent programming language has to support a continuously changing world. The complexity of the domain also has an effect on the aspect of how to program the agents in practice. The idea of G OLOG is to combine agent programming with planning. Usually this means that a certain goal should be reached and it is proved that with the actions programmed the goal can be reached. As it is generally not obvious to the programmer which series of actions will end in scoring a goal, it seems helpful to use an optimization theory to evaluate different action choices and execute the most promising one w.r.t. the success probability and the optimization theory. Several other aspects influenced the language R EADYLOG . The knowledge the robot or agent has about its environment is incomplete. This means that the robot has to use its sensors permanently to gather knowledge about the environment. When the robot has to query its sensors frequently it becomes an issue as to how the new knowledge can be integrated fast enough.
programmed cinematics, short movie-like sequences. Matinee's single biggest drawback for use in modern real-time architectural walkthrough production is that it is not interactive during scene playback; once initiated in a game level (or walkthrough setting), the Matinee scenes progress outside of user control (other than to optionally terminate the sequence). In other words, during the pre-programmed run of the Matinee scene(s), the walkthrough participant cannot directly control his or her virtual avatar, called a "Pawn." Architectural Cinematographer, on the other hand, was designed to provide camera viewpoint manipulation flexibility comparable to what Matinee can offer, while preserving user interactivity. Changes of camera position, view angle, field of view (i.e., zooms), scene cropping, filter overlay effects, and more, can be accomplished in a level walkthrough while the Pawn remains under user control, enhancing the real-time qualities that help keep a user involved in the virtual environment (i.e. the sense of agency derived from spatial navigation). AC, therefore, is a more specialized tool than Matinee. Additionally, while Matinee requires learning and using a specific sub-system interface of the Unreal Level Editor, Architectural Cinematographer's Actors are placed in level just as are those used for other level-building and editing purposes.
There are a number of disaster based or emergency applications especially within high rise building in urban areas best be served by shortest path routine. A system that could provide a route network within a building helps to locate safety routes for example to people in fire and rescue missions and other emergency operations. Other navigation applications in this domain like pedestrian navigation, and even for car navigation on roads or in racing circuit using computer game engine is also worth to mention. Balstrom (2001) listed some of the applications that need this kind of routine e.g. searching the nearest restaurant, a petrol station, jungle trekking on the mountain/hills, locating the emergency exit doors during fire (emergency) in a building (Meijers et al., 2005). It has been recognized that Dijsktra’s algorithm could be used for this sort of applications that is by extending to three-dimensional (3D) environment. Shortest path network for multilevel buildings thus the connectivity between levels or floors are certainly be very useful and interesting to be investigated. Several works have been done on Dijsktra’s algorithm for 3D environment such as Karas et al. (2006). Other related work such as Meijers et al. (2005) and Pu and Zlatanova (2005) focused on indoor navigation routing. However, it is interesting to note that works on inexpensive 3D game engine is getting ground compares to fully operational commercial game engine like Quake. Fritsch (2003) investigated the Quake game engine for his indoor navigation system. In this chapter the focuses is on having 3D shortest route together with inexpensive game engine for our navigation system.
Regarding the input image quality used for visual localization (section 1.2.1 ), Milford ( 2013 ) poses the question ‘How low can you go?’. The author captured omnidirectional images while driving with a car in both rural and urban environments. As it turns out, using a sequence-based method — similar to the localization algorithm presented in chapter 4 — visual localization can be performed using input images with a low resolution and reduced depth (bits per pixel). By using a high number of images per sequence, the reduced information in the images (downscaled panoramic images with a resolution of 8 × 4 and 2 bit depth) can be compensated for; it is shown that a sequence length of 50 images could still successfully be used for visual localization. In both cases, around 80% of all locations were matched correctly. This coincides with our ﬁndings throughout this work: We mostly used low frequencies (commonly around L = 20 bands) in our tests performed for visual localization (chapter 4 ), the visual 3D compass (chapter 5 ), and 3D-warping (chapter 6 ). This emphasizes one of the strengths of holistic methods in contrast to feature-based methods: Using the complete image information instead of point features, only low requirements regarding the image quality are necessary. Initial tests of our proposed methods show that for a lower number of bands the performance only decreases slightly. However, the performance of all tested methods decreased noticeably as soon as L = 10 bands or less are used. In future works, more extensive tests could be performed to ﬁnd lower and upper band limits for our suggested methods in varying outdoor environments.