535 research outputs found
Coordinated multi-robot formation control
Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201
Learning a Group-Aware Policy for Robot Navigation
Human-aware robot navigation promises a range of applications in which mobile
robots bring versatile assistance to people in common human environments. While
prior research has mostly focused on modeling pedestrians as independent,
intentional individuals, people move in groups; consequently, it is imperative
for mobile robots to respect human groups when navigating around people. This
paper explores learning group-aware navigation policies based on dynamic group
formation using deep reinforcement learning. Through simulation experiments, we
show that group-aware policies, compared to baseline policies that neglect
human groups, achieve greater robot navigation performance (e.g., fewer
collisions), minimize violation of social norms and discomfort, and reduce the
robot's movement impact on pedestrians. Our results contribute to the
development of social navigation and the integration of mobile robots into
human environments.Comment: 8 pages, 4 figure
A Survey on Aerial Swarm Robotics
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas
Self-organizing robot formations using velocity potential fields commands for material transfer
Mobile robot formations differ in accordance with the mission, environment, and robot abilities. In the case of decentralized control, the ability to achieve the shapes of these formations needs to be built in the controllers of each autonomous robot. In this paper, self-organizing formations control for material transfer is investigated, as an alternative to automatic guided vehicles. Leader–follower approach is applied for controllers design to drive the robots toward the goal. The results confirm the ability of velocity potential approach for motion control of both self-organizing formations
Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots
Cooperative object transportation using multiple robots has been intensively
studied in the control and robotics literature, but most approaches are either
only applicable to omnidirectional robots or lack a complete navigation and
decision-making framework that operates in real time. This paper presents an
autonomous nonholonomic multi-robot system and an end-to-end hierarchical
autonomy framework for collaborative luggage trolley transportation. This
framework finds kinematic-feasible paths, computes online motion plans, and
provides feedback that enables the multi-robot system to handle long lines of
luggage trolleys and navigate obstacles and pedestrians while dealing with
multiple inherently complex and coupled constraints. We demonstrate the
designed collaborative trolley transportation system through practical
transportation tasks, and the experiment results reveal their effectiveness and
reliability in complex and dynamic environments
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