We introduce zePPeLIN, a distributed system designed to address the challenges of pathplanning in large, cluttered, dynamic environments. The objective is to define a sequence of instructions to precisely move a grou...
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We introduce zePPeLIN, a distributed system designed to address the challenges of pathplanning in large, cluttered, dynamic environments. The objective is to define a sequence of instructions to precisely move a ground object (e.g., a mobile robot) from an initial to a final configuration in an environment. zePPeLIN is based on a set of wirelessly networked overhead cameras. While each camera only covers a limited environment portion, the camera set fully covers the environment through the union of its fields of view. pathplanning is performed in a fully distributed and cooperative way, based on potential diffusion over local Voronoi skeletons and local message exchanging. Additionally, the control of the moving object is fully distributed: it receives movement instructions from each camera when it enters that camera's field of view. The overall task is made particularly challenging by intrinsic errors in the overlap in cameras' fields of view. We study the performance of the system as a function of these errors, as well as its scalability for the size and density of the camera network. We also propose a few heuristics to improve performance and computational and communication efficiency. The reported results include both extensive simulation experiments and validation using a real camera network planning for a two-robot system.
Environment Intelligence is becoming ubiquitous. Wireless sensor networks supported environment intelligence is providing an opportunity to service robot navigation to reduce the complexity of conventional centralized...
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ISBN:
(纸本)9781424423835
Environment Intelligence is becoming ubiquitous. Wireless sensor networks supported environment intelligence is providing an opportunity to service robot navigation to reduce the complexity of conventional centralized and on-board map-building, localization, path-planning and motion control, whilst superior performance can be expected. In terms of robot pathplanning in a dynamic environment, distributed environment intelligence can take into account both global and local perceptions for path generation and adaptation, which results in better predictability and more prompt reaction. This paper proposes a snake based and distributed path planning algorithm for robot navigation in an intelligent environment with distributed wireless visual sensors. Via communication links between sensors, segments of a path, as an elastic band from start position to goal position, interact each other to react to repulsive forces from obstacles whilst maintain compliance. However, the compliance has to be subject to the robot kinematic constraints and the elastic hand may change to rigid or even to a broken state. A state machine is then presented to manage the state switch and control over the network. Simulations and experiments showed that the proposed distributed snake scheme can adapt to dynamic changes in the environment and satisfy the kinematic curvature constraints for the whole path.
In this paper, we address the problem of distributed path planning for large teams of hundreds of robots in constrained environments. We introduce two distributed prioritized planning algorithms: an efficient, complet...
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ISBN:
(纸本)9781424466757
In this paper, we address the problem of distributed path planning for large teams of hundreds of robots in constrained environments. We introduce two distributed prioritized planning algorithms: an efficient, complete method which is shown to converge to the centralized prioritized planner solution, and a sparse method in which robots discover collisions probabilistically. planning is divided into a number of iterations, during which every robot simultaneously and independently computes a planning solution based on other robots' path information from the previous iteration. paths are exchanged in ways that exploit the cooperative nature of the team and a statistical phenomenon known as the "birthday paradox". Performance is measured in simulated 2D environments with teams of up to 240 robots. We find that in moderately constrained environments, these methods generate solutions of similar quality to a centralized prioritized planner, but display interesting communication and planning time characteristics.
Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection m...
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Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the distributed Next-Best-path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV's next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes.
Uncrewed aerial vehicles (UAVs), also known as drones, are ubiquitous and their use cases extend today from governmental applications to civil applications such as the agricultural, medical, and transport sectors, etc...
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Uncrewed aerial vehicles (UAVs), also known as drones, are ubiquitous and their use cases extend today from governmental applications to civil applications such as the agricultural, medical, and transport sectors, etc. In accordance with the requirements in terms of demand, it is possible to carry out various missions involving several types of UAVs as well as various onboard sensors. According to the complexity of the mission, some configurations are required both in terms of hardware and software. This task becomes even more complex when the system is composed of autonomous UAVs that collaborate with each other without the assistance of an operator. Several factors must be considered, such as the complexity of the mission, the types of UAVs, the communication architecture, the routing protocol, the coordination of tasks, and many other factors related to the environment. Unfortunately, although there are many research works that address the use cases of multi-UAV systems, there is a gap in the literature regarding the difficulties involved with the implementation of these systems from scratch. This review article seeks to examine and understand the communication issues related to the implementation from scratch of autonomous multi-UAV systems for collaborative decisions. The manuscript will also provide a formal definition of the ecosystem of a multi-UAV system, as well as a comparative study of UAV types and related works that highlight the use cases of multi-UAV systems. In addition to the mathematical modeling of the collaborative target detection problem in distributed environments, this article establishes a comparative study of communication architectures and routing protocols in a UAV network. After reading this review paper, readers will benefit from the multicriteria decision-making roadmaps to choose the right architectures and routing protocols adapted for specific missions. The open challenges and future directions described in this manuscript can be u
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