Multi UAV Coverage Path Planning in Urban Environments

Anais Estendidos do XII Simpósio Brasileiro de Robótica e XVII Simpósio Latino Americano de Robótica (SBR/LARS Estendido 2020)

Coverage Path Planning (CPP) problem is a motion planning subtopic in robotics, where it is necessary to build a path for a robot to explore every location in a given scenario. Unmanned Aerial Vehicles (UAV) have been employed in several applications related to the CPP problem. However, one of the significant limitations of UAVs is endurance, especially in multi-rotors. Minimizing energy consumption is pivotal to prolong and guarantee coverage. Thus, this work proposes energy-aware coverage path planning solutions for regular and irregular-shaped areas containing full and partial information. We consider aspects such as distance, time, turning maneuvers, and optimal speed in the UAV’s energy consumption. We propose an energy-aware spiral algorithm called E-Spiral to perform missions over regular-shaped areas. Next, we explore an energy-aware grid-based solution called EG-CPP for mapping missions over irregular-shaped areas containing no-fly zones. Finally, we present an energy-aware...

Sensors (Basel, Switzerland), 2021

Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square—for the planning phase—and a simple priority-based speed control—as the method for conflict resolution—is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles ...

Field and Service Robotics

Planning for multi-robot coverage seeks to determine collision-free paths for a fleet of robots, enabling them to collectively observe points of interest in an environment. Persistent coverage is a variant of traditional coverage where coveragelevels in the environment decay over time. Thus, robots have to continuously revisit parts of the environment to maintain a desired coverage-level. Facilitating this in the real world demands we tackle numerous subproblems. While there exist standard solutions to these subproblems, there is no complete framework that addresses all of their individual challenges as a whole in a practical setting. We adapt and combine these solutions to present a planning framework for persistent coverage with multiple unmanned aerial vehicles (UAVs). Specifically, we run a continuous loop of goal assignment and globally deconflicting, kinodynamic path planning for multiple UAVs. We evaluate our framework in simulation as well as the real world. In particular, we demonstrate that (i) our framework exhibits graceful coverage-given sufficient resources, we maintain persistent coverage; if resources are insufficient (e.g., having too few UAVs for a given size of the enviornment), coverage-levels decay slowly and (ii) planning with global deconfliction in our framework incurs a negligibly higher price compared to other weaker, more local collision-checking schemes.

This paper proposes a path-planning approach to enable a team of unmanned aerial vehicles (UAVs) to efficiently conduct surveillance of sensitive areas. The proposedapproach, termed PARCov (Planner for Autonomous Risk-sensitive Coverage), seeks to maximize the area covered by the sensors mounted on each UAV while maintaining high sensor data quality and minimizing detection risk. PARCov uses a dynamic grid to keep track of the parts of the space that have been surveyed and the times that they were last surveyed. This information is then used to move the UAVs toward areas that have not been covered in a long time. Moreover, a nonlinear optimization formulation is used to determine the altitude at which each UAV flies. The efficiency and scalability of PARCov is demonstrated in simulation using complex environments and an increasing number of UAVs to conduct risk-sensitive surveillance.

Drones

This work focuses on the efficiency improvement of grid-based Coverage Path Planning (CPP) methodologies in real-world applications with UAVs. While several sophisticated approaches are met in literature, grid-based methods are not commonly used in real-life operations. This happens mostly due to the error that is introduced during the region’s representation on the grid, a step mandatory for such methods, that can have a great negative impact on their overall coverage efficiency. A previous work on UAVs’ coverage operations for remote sensing, has introduced a novel optimization procedure for finding the optimal relative placement between the region of interest and the grid, improving the coverage and resource utilization efficiency of the generated trajectories, but still, incorporating flaws that can affect certain aspects of the method’s effectiveness. This work goes one step forward and introduces a CPP method, that provides three different ad-hoc coverage modes: the Geo-fenced...

Recent developments of autonomous unmanned vehicles and sensor systems allow automated approaches to surveillance with minimal human supervision or intervention. Indeed as the number of vehicles increases it becomes increasingly difficult and even impossible for direct human supervision. Our work addresses the generation of team flight plans and controllers that enable a heterogeneous team of UXVs to acquire information for spatial and temporal situational awareness. The paper describes our overall framework and algorithms for the computation of trajectories that maximize spatio-temporal coverage while satisfying hard constraints such as collision avoidance and specifications on initial and final positions.

2010

Multiple moving targets in an urban area are to be tracked simultaneously by unmanned aerial vehicles. It is assumed that the moving targets try to avoid the camera field of view of the aircraft by changing their velocities and/or hiding behind buildings. The number of aircraft is much smaller than the number of targets, in general. In order to track as many targets as possible, firstly the targets are grouped into a number of subgroups by maximising the modularity, which is solved efficiently by the power iteration. Secondly, circular optimal paths are assigned to maximise the visibility of the area, given shapes and locations of the ground obstacles, where the computational complexity is reduced using a novel random sampling method. Finally, the aircraft transition paths from the current positions to the desired path are obtained by solving a discrete minimum weighted path length problem.

International Journal of Advanced Robotic Systems, 2013

In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a Mixed Integer Linear Programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP). The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1) each target is visited exactly by one of the chosen UAVs (2) the total distan...

Applied Sciences

Unmanned aerial vehicles (UAVs) are used widely for data collection in wireless sensor networks (WSNs). UAVs visit the sensors to collect the data. UAV-aided data collection is a challenging problem because different paths of a UAV, i.e., visiting orders of sensors, affect energy consumption and data delivery times. The problem becomes more difficult when there are obstacles in the path of the UAV. Thus, the UAV needs to take a detour to avoid them, resulting in different travel distances and times. Therefore, this study formulated the obstacle-aware path planning problem of UAVs, i.e., the obstacle-constrained distance minimization (OCDM) problem, as an integer linear programming problem (ILP) to minimize the total traveling distances of all UAVs while considering the UAVs’ flight time constraints. First, a possible detour-points-selection algorithm called vector rotation-angle-based obstacle avoidance (VRAOA) is proposed to find the detour points around each obstacle in the enviro...

Journal of Intelligent & Robotic Systems, 2014

Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path the objectives are to maximize the collected information (CI) from desired regions (DR), while avoiding flying over forbidden regions (FR) and reaching the destination. The path planning problem for a single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators: pull-to-desired-region (PTDR), push-from-forbidden-region (PFFR), and pull-to-final-point (PTFP). In addition to these newly proposed operators, standard mutation and crossover operators are used. The initial population seed-path is obtained by both utilizing the pattern search method and solving the traveling salesman problem (TSP). Using this seed-path the initial population of paths is generated by randomly selected heading angles. It should be emphasized that all of the paths in population in any generation of the genetic algorithm (GA) are constructed using the dynamical mathematical model of a UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm is tested with different scenarios, and the results are presented in Section VI. Although there are previous studies in this field, the focus here is on maximizing the CI instead of minimizing the total mission time. In addition it is observed that the proposed operators generate better paths than classical evolutionary operators.