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 ...

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.

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.

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).

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 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.

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.

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).

... 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.

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 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.