Path planning

Planning the path of an autonomous,agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and ...

Many universities and institutes experience difficulty in training people to work with expensive equipments. A common problem faced by educational institutions concerns the limited availability of expensive robotics equipments, with which students in the didactic program can work, in order to acquire valuable "hands on" experience. Therefore, the Robot Simulation Software (RSS) nowadays is paramount important. This paper presents the development of a visual software package where a AL5B Robot arm has been taken as a case study. It utilizes Matlab/Simulink and AutoCAD as the tools for testing motional characteristics of the AL5B Robot arm. A robot model will be developed; the Forward, Inverse Kinematics, Velocity Kinematics "Jacobian" and Path Planning problems will be implemented and tested.

In this study we present our initial idea for using genetic algorithms to help a controllable mobile robot to find an optimal path between a starting and ending point in a grid environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. GAs can overcome many problems encountered by traditional search techniques such as the gradient based methods. The proposed controlling algorithm allows four-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.

the Line follower robot is a mobile machine that can detect and follow the line drawn on the floor. Generally, the path is predefined and can be either visible like a black line on a white surface with a high contrasted color or it can be invisible like a magnetic field. Therefore, this kind of Robot should sense the line with its Infrared Ray (IR) sensors that installed under the robot. After that, the data is transmitted to the processor by specific transition buses. Hence, the processor is going to decide the proper commands and then it sends them to the driver and thus the path will be followed by the line follower robot.

This paper presents our findings of current applications of computer games in design visualization and education, and our solution to address interoperability between games and building models to enhance architectural visualization and education. We demonstrate our BIM-Game prototype that integrates Building Information Modelling (BIM) and gaming into architectural visualization. Our system supports an innovative Design-Play process that allows designers to play in their own designed environments with the capability for simulations of physical dynamics and user activities. The implementation of this BIM-Game provides a variety of connections among several fields including architecture, engineering, computer science, visualization, and game development. These connections bridge diverse areas such as building modelling, equipment simulation and visualization, character modelling and animation, collision detection, navigation, path planning, materials and lighting, and interaction through game controllers and user interfaces. The paper presents a framework for integrating BIM and games, and a sample experiment of real-time, interactive, and photorealistic walkthrough with a virtual user model to demonstrate the use and the potentials of the framework. We expect this research to extend the study and adaptability of computer games in design education.

Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of xed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of nite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.

We address the problem of hover flight and translational velocity regulation of a quad-rotorcraft UAV with the main objective of allowing the vehicle to navigate autonomously. This paper complements and improves previous researches considering multiple cameras systems, non conventional sensors, and which deal only with stabilizing the aerial vehicle in hover or in take-off and landing tasks. A vision system has been implemented to estimate the vehicle's altitude, lateral position, and forward velocity during flights. It is shown that, using visual information, it is possible to develop control strategies for different kinds of flying modes, such as hover flight and forward flight at constant velocity. The stability of the closed-loop system is ensured by implementing a hierarchical control strategy. In a first stage, the performance of the proposed methodologies was validated under a simulation environment, showing satisfactory and promising results. Next, real-time experimental applications, consisting of autonomous hover and forward flight at constant velocity were successfully achieved, validating the effectiveness of the proposed imaging algorithm and vision-based controller.

This paper addresses the issue of monitoring physical spatial phenomena of interest utilizing the information collected by a network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. The spatial phenomenon is statistically modelled by a Gaussian Markov Random Field (GMRF) with hyperpa-rameters that are learnt as the measurements accumulate over time. In this context, the GMRF approximately represents the spatial field on an irregular lattice of triangulation by exploiting a stochastic partial differential equation (SPDE) approach, which benefits remarkably in computation due to the sparsity of the precision matrix. A technique of the one-step-ahead forecast is employed to predict the future measurements that are required to find the optimal sampling locations. It is shown that optimizing the sampling path problem with the logarithm of the determinant either of a covariance matrix using a GP model or of a precision matrix using a GMRF model for mobile robotic wireless sensor networks (MRWSNs) even by a greedy algorithm is impractical. This paper proposes an efficient novel optimality criterion for the adaptive sampling strategy to find the most informative locations in taking future observations that minimize the uncertainty at unobserved locations. The computational complexity of our proposed method is linear, which makes the MRWSN scalable and practically feasible. The effectiveness of the proposed approach is compared and demonstrated using a pre-published data set with appealing results.

Recent advances in the area of mobile robotics caused growing attention of the armed forces, where the necessity for unmanned vehicles being able to carry out the “dull and dirty” operations, thus avoid endangering the life of the military personnel. UAV offers a great advantage in supplying reconnaissance data to the military personnel on the ground, thus lessening the life risk of the troops. In this paper we analyze various techniques for path planning and obstacle avoidance and cooperation issues for multiple mobile robots. We also present a generic dynamics and control model for steering a UAV along a collision free path from a start to a goal position.

Efficient path planning algorithms are a crucial issue for modern autonomous underwater vehicles. Classical path planning algorithms in artificial intelligence are not designed to deal with wide continuous environments prone to currents. We present a novel Fast Marching based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a continuous path from a discrete representation of the environment. Secondly we take underwater currents into account thanks to an anisotropic extension of the original Fast Marching algorithm. Thirdly, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic medias. Finally, a multiresolution method is introduced to speed up the overall path planning process.

In the past mobile robot research was often focused to various kinds of point-to-point transportation tasks. Service tasks, such as floor cleaning, require specific approaches for path planning and vehicle guidance in real indoor environments. This article discusses automatic planning of a feasible cleaning path considering a 2D-map as well as kinematic and geometric robot models. Path construction makes use of two typical motion patterns. Each pattern is defined by a sequence of subgoals indicating robot position and orientation. Results of automatic path planning are illustrated by realistic examples of typical robots and cleaning environments. Vehicle guidance includes initialization of robot location, path execution, accurate path tracking and detection of unexpected environmental changes. Path tracking is achieved by subgoal modification during cleaning motion using data from the dead-reckoning and landmark localization systems. If obstacles permanently block the preplanned path, an automatic map update and path replanning is performed. Experimental results with the mobile robot MACROBE confirm the feasibility of the developed planning and guidance system.

This paper proposes a model of approach behavior with which a robot can initiate conversation with people who are walking. We developed the model by learning from the failures in a simplistic approach behavior used in a real shopping mall. Sometimes people were unaware of the robot's presence, even when it spoke to them. Sometimes, people were not sure whether the robot was really trying to start a conversation, and they did not start talking with it even though they displayed interest. To prevent such failures, our model includes the following functions: predicting the walking behavior of people, choosing a target person, planning its approaching path, and nonverbally indicating its intention to initiate a conversation. The approach model was implemented and used in a real shopping mall. The field trial demonstrated that our model significantly improves the robot's performance in initiating conversations.

We introduce a method to generate whole body motion of a humanoid robot such that the resulted total linear/angular momenta become specified values. First, we derive a linear equation which gives the total momentum of a robot from its physical parameters, the base link speed and the joint speeds. Constraints between the legs and the environment are also considered. The whole body motion is calculated from a given momentum reference by using a pseudo-inverse of the inertia matrix. As examples, we generated the kicking and walking motions and tested on the actual humanoid robot HRP-2. This method, the Resolved Momentum Control, gives us a unified framework to generate various maneuver of humanoid robots. 0-7803-7860-1/03/$17.00

The purpose of this paper is to analyze and assess those factors that influence the extent of usage, type of usage (planning only versus planning and control), and selection of project management (PM) software by professionals in the field. Discriminant analysis is used to analyze hypotheses relating to these three issues. The analysis is based on the results of a random survey of members of the Project Management Institute. This research was guided by an organizing framework that considers environmental factors (demographics and work environment characteristics), intermediate factors (PM software usage category and historical use patterns), and outcomes (level of software package selected). The analysis reveals strong differences in extent of usage, type of usage, and software selection based on individually significant environmental and intermediate factors. It also provides strong support for the hypotheses relating to extent of software use and level of software package used, while providing qualified support for the hypothesis relating to software use for planning only versus planning and control. These results together validate the organizing framework. Adopters of PM software are advised to consider the findings concerning industry practice as well as their specific needs when selecting and deciding how to use PM software packages.

In this paper, a new approach is developed for solving the problem of mobile robot path planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms have been used widely for solving real world problems, especially in robotics since it has been proved to give reliable and efficient solutions due to its simple and well developed theory. However, most of the researchers who tried to use Q-learning for solving the mobile robot navigation problem dealt with static environments; they avoided using it for dynamic environments because it is a more complex problem that has infinite number of states. This great number of states makes the training for the intelligent agent very difficult. In this paper, the Q-learning algorithm was applied for solving the mobile robot navigation in dynamic environment problem by limiting the number of states based on a new definition for the states space. This has the effect of reducing the size of the Q-table and hence, increasing the speed of the navigation algorithm. The conducted experimental simulation scenarios indicate the strength of the new proposed approach for mobile robot navigation in dynamic environment. The results show that the new approach has a high Hit rate and that the robot succeeded to reach its target in a collision free path in most cases which is the most desirable feature in any navigation algorithm.

tially knozlln 3-0 environment while avoiding obstacles and minimizing path length. We use a hierarchical approach to path planning. W e distinguish between a global offline computation, based on a coarse known model of the environment and a local online computation, ba,sed o n the information coming from the vision system. A U A V builds and updates a virtual 3-0 model of the surrounding en-vironmerLt by yrocessing image sequemes and fusing t h e m .with sensor data. Based o n such a model the UAV iui.11 plan a path f r o m its current position t o the terrrvinal point. I t will then follow such path, getting more data f r o m the on-board cameras, and refining map and local path in real time.

In this paper, we present a novel robotic system that produces watercolour paintings by means of a 6-degree-of-freedom collaborative robot. After an analysis of traditional watercolour, different non-photorealistic rendering techniques are applied in order to elaborate digital images into artworks. Several algorithms, aimed at processing both the backgrounds and the details, are implemented. Then, the resulting rendering is converted into a sequence of trajectories that the robot reproduces on paper. During the process, the artist controlling the system can change both the algorithm parameters and the hardware variables (e.g. brush type, colour dilution, etc.) in order to obtain a different artistic rendering. The challenge is indeed not to faithfully reproduce an image but to introduce a personal and original contribution to the artwork. The robotic painting system described in this paper was named "Busker Robot" and it is the first automatic system that uses the watercolour technique for artistic rendering. It was installed at the "Algorithmic Arts and Robotics" exhibition during the international event "Trieste Next" (Trieste, Italy, September 2017) and won an Honorable Mention at the 2018 International Robotic Art Competition (RobotArt).

Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize localisation accuracy. The map building and exploration task is modeled using an Occupancy Grid (OG) with concurrent localisation performed using a feature-based Simultaneous Localisation And Mapping (SLAM) algorithm . Adaptive sensing aims at maximizing the map information by simultaneously maximizing the expected Shannon information gain (Mutual Information) on the OG map and minimizing the uncertainty of the vehicle pose and map feature uncertainty in the SLAM process. The resulting map building system is demonstrated in an indoor environment using data from a laser scanner mounted on a mobile platform.

This article introduces a new approach to Simultaneous Localization and Mapping (SLAM) which pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation which can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid discrete-continuous state space, which naturally combines metric and topological maps. There are two fundamental characteristics that set this work apart from previous ones: (i) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem; and (ii) the analytical formulation of belief distributions over hybrid maps, which allows us to maintain the spatial uncertainty in large spaces more accurately and efficiently than previous works. We also describe a practical implementation which aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30.000 m 2 , a 2Km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches.

This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

Despite the growing popularity of swarm-based applications of drones, there is still a lack of approaches to maximize the safety of swarms of drones by minimizing the risks of drone collisions. In this paper, we present an approach that uses optimization, learning, and automatic immediate responses (reflexes) of drones to ensure safe operations of swarms of drones. The proposed approach integrates a high-performance dynamic evolutionary algorithm and a reinforcement learning algorithm to generate safe and efficient drone routes and then augments the generated routes with dynamically computed drone reflexes to prevent collisions with unforeseen obstacles in the flying zone. We also present a parallel implementation of the proposed approach and evaluate it against two benchmarks. The results show that the proposed approach maximizes safety and generates highly efficient drone routes.

The Robot Operating System (ROS) provides operating system-like services to operate robots. Mapping, localization, and autonomous navigation in an indoor environment are popular issues in the field of autonomous robots. Autonomous navigation in a dynamic environment is not only challenging but also uncovers many indoor environmental factors which affect the process of mapping and navigation. The presented work describes how a ROS-based control system is used with a Pioneer 3-DX robot for indoor mapping, localization, and autonomous navigation. Mapping of different challenging environments is presented in this work. Moreover, some factors associated with indoor environments that can affect mapping, localization, and automatic navigation, are also presented. For experiments, three environments (one artificial and two real) have been tested. Some implementation was done in C and Python.

The main objective of an Unmanned-Aerial-Vehicle (UAV) is to provide an operator with services from its payload. Currently, to get these UAV services, one extra human operator is required to navigate the UAV. Many techniques have been investigated to increase UAV navigation autonomy at the Path Planning level. The most challenging aspect of this task is the re-planning requirement, which comes from the fact that UAVs are called upon to fly in unknown environments. One technique that out performs the others in path planning is the Genetic Algorithm (GA) method because of its capacity to explore the solution space while preserving the best solutions already found. However, because the GA tends to be slow due to its iterative process that involves many candidate solutions, the approach has not been actively pursued for real time systems. This paper presents the research that we have done to improve the GA computation time in order to obtain a path planning generator that can recompile a path in real-time, as unforeseen events are met by the UAV. The paper details how we achieved parallelism with a Field Programmable Gate Array (FPGA) implementation of the GA. Our FPGA implementation not only results in an excellent autonomous path planner, but it also provides the design foundations of a hardware chip that could be added to an UAV platform.

This paper presents a realtime, collision-free motion coordination and navigation system for an Unmanned Aerial Vehicle (UAV) fleet. The proposed system uses geographical locations of the UAVs and of the successfully detected, static and moving obstacles to predict and avoid: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-to-moving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and Complex Event Processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee a risk of a collision in realtime and proactively find best ways to avoid the predicted collisions in order to ensure safety of the entire fleet. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments. The results demonstrate that the proposed system successfully predicts and avoids all three kinds of collisions in realtime. Moreover, it generates efficient UAV routes, has an excellent runtime performance, efficiently scales to large-sized problem instances involving dozens of UAVs and obstacles, and is suitable for some densely populated, cluttered flying zones.

Path planning in mobile robots must ensure optimality of the path. The optimality achieved may be in path, time, energy consumed etc. Path planning in robots also depends on the environment in which it operates like, static or dynamic, known or unknown etc. Global path planning using A* algorithm and genetic algorithm is investigated in this paper. A known dynamic environment, in which a control station will compute the shortest path and communicate to the mobile robot and the mobile robot, will traverse through this path to reach the goal. The control station will keep track of the path traversed by the robot. The mobile robot navigates through the shortest path and if the robot detects any obstacle in the destined path, the mobile robot will update the information about the environment and this information together with the current location will be communicated to the control station. Then the control station, with the updated map of the environment and new starting location and destination recalculates the new shortest path, if any, and will communicate to the mobile robot so that it can reach the destination. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results demonstrate that the technique effectively calculates the shortest path in known dynamic environment and allows the robot to quickly accomplish the mission.

This paper addresses the issue of monitoring physical spatial phenomena of interest using information collected by a resource-constrained network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. We first propose an efficient novel optimality criterion for designing a sampling strategy to find the most informative locations in taking future observations in order to minimize the uncertainty at all unobserved locations of interest. This solution is proven to be within bounds. The computational complexity of this proposition is shown to be practically feasible. We then prove that under a certain condition of monotonicity property the approximate entropy at resulting locations obtained by our proposed algorithm is within 1 − 1 e of the optimum, which is then utilized as a stopping criterion for the sampling algorithm. The criterion enables the prediction results to be within user-defined accuracies by controlling the number of mobile sensors. The effectiveness of the proposed method is illustrated using a pre-published data set.

Unmanned Aerial Vehicle (UAV) is defined as aircraft without the onboard presence of pilots. UAVs have been used to perform intelligence, surveillance, and reconnaissance missions. The UAVs are not limited to military operations, they can also be used in commercial applications such as telecommunications, ground traffic control, search and rescue operations, crop monitoring, etc. In this paper, we propose a swarm intelligence-based method for UAVs' route optimization. The team of UAVs is used for area coverage with the defined set of waypoints. The problem can be interpreted as a well-known Traveling Salesman Problem where the task is to find the route of minimal length such that all the waypoints are visited only once. We applied the Ant System algorithm and compared it with the Nearest Neighbor Search. The experimental results confirm the effectiveness of our method, especially for a large number of waypoints.

A new variant of particle swarm optimization (PSO), named phase angle-encoded and quantum-behaved particle swarm optimization (θ-QPSO), is proposed. Six versions of θ-QPSO using different mappings are presented and compared through their application to solve continuous function optimization problems. Several representative benchmark functions are selected as testing functions. The real-valued genetic algorithm (GA), differential evolution (DE), standard particle swarm optimization (PSO), phase angle-encoded particle swarm optimization (θ-PSO), quantum-behaved particle swarm optimization (QPSO), and θ-QPSO are tested and compared with each other on the selected unimodal and multimodal functions. To corroborate the results obtained on the benchmark functions, a new route planner for unmanned aerial vehicle (UAV) is designed to generate a safe and flyable path in the presence of different threat environments based on the θ-QPSO algorithm. The PSO, θ-PSO, and QPSO are presented and compared with the θ-QPSO algorithm as well as GA and DE through the UAV path planning application. Each particle in swarm represents a potential path in search space. To prune the search space, constraints are incorporated into the pre-specified cost function, which is used to evaluate whether a particle is good or not. Experimental results demonstrated good performance of the θ-QPSO in planning a safe and flyable path for UAV when compared with the GA, DE, and three other PSO-based algorithms.

Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch.

This paper presents algorithmic solutions for the complete coverage path planning problem using a team of mobile robots. Multiple robots decrease the time to complete the coverage, but maximal efficiency is only achieved if the number of regions covered multiple times is minimized. A set of multi-robot coverage algorithms is presented that minimize repeat coverage. The algorithms use the same planar cellbased decomposition as the Boustrophedon single robot coverage algorithm, but provide extensions to handle how robots cover a single cell, and how robots are allocated among cells. Specifically, for the coverage task our choice of multi-robot policy strongly depends on the type of communication that exists between the robots. When the robots operate under the line-of-sight communication restriction, keeping them as a team helps to minimize repeat coverage. When communication between the robots is available without any restrictions, the robots are initially distributed through space, and each one is allocated a virtually-bounded area to cover. A greedy auction mechanism is used for task/cell allocation among the robots. Experimental results from different simulated and real environments that illustrate our approach for different communication conditions are presented.

This paper presents the design and implementation of a vision-based navigation and landing algorithm for an autonomous helicopter. The vision system allows to define target areas from a high resolution aerial or satellite image to determine the waypoints of the navigation trajectory or the landing area. The helicopter is required to navigate from an initial position to a final position in a partially known environment using GPS and vision, to locate a landing target (a helipad of a known shape or a natural landmark) and to land on it. The vision system, using a feature-based image matching algorithm, finds the area and gives feedbacks to the control system for autonomous landing. Vision is used for accurate target detection, recognition and tracking. The helicopter updates its landing target parameters owing to vision and uses an on board behavior-based controller to follow a path to the landing site. Results show the appropriateness of the vision-based approach that does not require any artificial landmark (e.g., helipad) and is quite robust to occlusions, light variations and seasonal changes (e.g., brown or green leaves).

We address the problem of online path planning for optimal sensing with a mobile robot. The objective of the robot is to learn the most about its pose and the environment given time constraints. We use a POMDP with a utility function that depends on the belief state to model the finite horizon planning problem. We replan as the robot progresses throughout the environment. The POMDP is highdimensional, continuous, non-differentiable, nonlinear, non-Gaussian and must be solved in real-time. Most existing techniques for stochastic planning and reinforcement learning are therefore inapplicable. To solve this extremely complex problem, we propose a Bayesian optimization method that dynamically trades off exploration (minimizing uncertainty in unknown parts of the policy space) and exploitation (capitalizing on the current best solution). We demonstrate our approach with a visually-guide mobile robot. The solution proposed here is also applicable to other closelyrelated domains, including active vision, sequential experimental design, dynamic sensing and calibration with mobile sensors.

... In approximate cellular decomposition, the re-gion is approximated with a grid that covers the re ... From this study, it was concluded that 16% of the driving distance could be saved ... Ryerson and Zhang (2006) have proposed using genetic algorithms to solve the coverage path plan ...

This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (UAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of UAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angle-encoded Particle Swarm Optimization (theta-PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of-Things (IoT) for multi-directional transmission and reception of data between the UAVs, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach.

In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.

This paper proposes a new approach using improved particle swarm optimization (PSO) to optimize the path of a mobile robot through an environment containing static obstacles. Relative to many optimization methods that produce nonsmooth paths, the PSO method developed in this paper can generate smooth paths, which are more preferable for designing continuous control technologies to realize path following using mobile robots. To reduce computational cost of optimization, the stochastic PSO (S-PSO) with high exploration ability is developed, so that a swarm with small size can accomplish path planning. Simulation results validate the proposed algorithm in a mobile robot path planning.

Robot navigation remains as one of the unsolved problems of computer science and robotics. The problem is in the development of an algorithm which would allow the mobile robot to find its way around a room, or maze and at the same time dealing with any obstacles. Researchers had further decomposed the problem into several modules in view of the navigation problem. These are such as obstacle avoidance, wall following, path planning, etc. Acquiring robots in the facility incurs cost. Generally, it will not be cheap. Thus, it may be costly to perform tests on the real robot if it is damaged during its operation. In addition, the robot needs to be handled carefully and calibrations need to be performed. As such, simulators have come to play an important role to verify these algorithms before it is tested on the real robot. In this paper, a flexible simulation is developed for the Khepera II robot using Webots. The developed simulation is equipped with various controls to allow the user ...

This paper addresses real-time navigation for crowds moving on multilayered and uneven terrains. The objective is to animate thousands of characters in real-time on large-scale environments without taking care of motion details: a common goal being given to a homogeneous crowd, the proposed method automatically breaks the homogeneity of the crowd by spreading characters on various routes reaching the goal; the animation effect is displayed at a view point that does not require to address character interaction locally. Our approach is based on a space structuring technique that automatically decomposes multilayered or uneven terrains into navigation corridors giving rise to a navigation graph supporting the search.

This study focuses on the implementation and demonstration of the Real Time Path Planner (RTPP). It is an AI guidance system that was developed for an operational DoD unmanned aerial target control system. The RTPP is tested using the 6-DOF target simulator of Drone Formation Control System (DFCS). The RTPP uses the A* algorithm to generate the obstacle free routes.