511 research outputs found
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
Comprehensive review on controller for leader-follower robotic system
985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies
Coordinated multi-robot formation control
Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201
Reactive Control Of Autonomous Dynamical Systems
This thesis mainly consists of five independent papers concerning the reactive control design of autonomous mobile robots in the context of target tracking and cooperative formation keeping with obstacle avoidance in the static/dynamic environment. Technical contents of this thesis are divided into three parts. The first part consists of the first two papers, which consider the target-tracking and obstacle avoidance in the static environment. Especially, in the static environment, a fundamental issue of reactive control design is the local minima problem(LMP) inherent in the potential field methods(PFMs). Through introducing a state-dependent planned goal, the first paper proposes a switching control strategy to tackle this problem. The control law for the planned goal is presented. When trapped into local minima, the robot can escape from local minima by following the planned goal. The proposed control law also takes into account the presence of possible saturation constraints. In addition, a time-varying continuous control law is proposed in the second paper to tackle this problem. Challenges of finding continuous control solutions of LMP are discussed and explicit design strategies are then proposed. The second part of this thesis deals with target-tracking and obstacle avoidance in the dynamic environment. In the third paper, a reactive control design is presented for omnidirectional mobile robots with limited sensor range to track targets while avoiding static and moving obstacles in a dynamically evolving environment. Towards this end, a multiiii objective control problem is formulated and control is synthesized by generating a potential field force for each objective and combining them through analysis and design. Different from standard potential field methods, the composite potential field described in this paper is time-varying and planned to account for moving obstacles and vehicle motion. In order to accommodate a larger class of mobile robots, the fourth paper proposes a reactive control design for unicycle-type mobile robots. With the relative motion among the mobile robot, targets, and obstacles being formulated in polar coordinates, kinematic control laws achieving target-tracking and obstacle avoidance are synthesized using Lyapunov based technique, and more importantly, the proposed control laws also take into account possible kinematic control saturation constraints. The third part of this thesis investigates the cooperative formation control with collision avoidance. In the fifth paper, firstly, the target tracking and collision avoidance problem for a single agent is studied. Instead of directly extending the single agent controls to the multiagents case, the single agent controls are incorporated with the cooperative control design presented in [1]. The proposed decentralized control is reactive, considers the formation feedback and changes in the communication networks. The proposed control is based on a potential field method, its inherent oscillation problem is also studied to improve group transient performance
A novel coordination framework for multi-robot systems
Having made great progress tackling the basic problems concerning single-robot systems, many researchers shifted their focus towards the study of multi-robot systems (MRS). MRS were shortly found to be a perfect t for tasks considered to be hard, complex or even impossible for a single robot to perform, e.g. spatially separate tasks. One core research problem of MRS is robots' coordinated motion planning and control. Arti cial potential elds (APFs) and virtual spring-damper bonds are among the most commonly used models to attack the trajectory planning problem of MRS coordination. However, although mathematically sound, these approaches fail to guarantee inter-robot collision-free path generation. This is particularly the case when robots' dynamics, nonholonomic constraints and complex geometry are taken into account. In this thesis, a novel bio-inspired collision avoidance framework via virtual shells is proposed and augmented into the high-level trajectory planner. Safe trajectories can hence be generated for the low-level controllers to track. Motion control is handled by the design of hierarchical controllers which utilize virtual inputs. Several distinct coordinated task scenarios for 2D and 3D environments are presented as a proof of concept. Simulations are conducted with groups of three, four, ve and ten nonholonomic mobile robots as well as groups of three and ve quadrotor UAVs. The performance of the overall improved coordination structure is veri ed with very promising result
Analysis of multi-agent systems under varying degrees of trust, cooperation, and competition
Multi-agent systems rely heavily on coordination and cooperation to achieve a variety of tasks. It is often assumed that these agents will be fully cooperative, or have reliable and equal performance among group members. Instead, we consider cooperation as a spectrum of possible interactions, ranging from performance variations within the group to adversarial agents. This thesis examines several scenarios where cooperation and performance are not guaranteed. Potential applications include sensor coverage, emergency response, wildlife management, tracking, and surveillance. We use geometric methods, such as Voronoi tessellations, for design insight and Lyapunov-based stability theory to analyze our proposed controllers. Performance is verified through simulations and experiments on a variety of ground and aerial robotic platforms. First, we consider the problem of Voronoi-based coverage control, where a group of robots must spread out over an environment to provide coverage. Our approach adapts online to sensing and actuation performance variations with the group. The robots have no prior knowledge of their relative performance, and in a distributed fashion, compensate by assigning weaker robots a smaller portion of the environment. Next, we consider the problem of multi-agent herding, akin to shepherding. Here, a group of dog-like robots must drive a herd of non-cooperative sheep-like agents around the environment. Our key insight in designing the control laws for the herders is to enforce geometrical relationships that allow for the combined system dynamics to reduce to a single nonholonomic vehicle. We also investigate the cooperative pursuit of an evader by a group of quadrotors in an environment with no-fly zones. While the pursuers cannot enter the no-fly zones, the evader moves freely through the zones to avoid capture. Using tools for Voronoi-based coverage control, we provide an algorithm to distribute the pursuers around the zone's boundary and minimize capture time once the evader emerges. Finally, we present an algorithm for the guaranteed capture of multiple evaders by one or more pursuers in a bounded, convex environment. The pursuers utilize properties of the evader's Voronoi cell to choose a control strategy that minimizes the safe-reachable area of the evader, which in turn leads to the evader's capture
Collision Free Navigation of a Multi-Robot Team for Intruder Interception
In this report, we propose a decentralised motion control algorithm for the
mobile robots to intercept an intruder entering (k-intercepting) or escaping
(e-intercepting) a protected region. In continuation, we propose a
decentralized navigation strategy (dynamic-intercepting) for a multi-robot team
known as predators to intercept the intruders or in the other words, preys,
from escaping a siege ring which is created by the predators. A necessary and
sufficient condition for the existence of a solution of this problem is
obtained. Furthermore, we propose an intelligent game-based decision-making
algorithm (IGD) for a fleet of mobile robots to maximize the probability of
detection in a bounded region. We prove that the proposed decentralised
cooperative and non-cooperative game-based decision-making algorithm enables
each robot to make the best decision to choose the shortest path with minimum
local information. Then we propose a leader-follower based collision-free
navigation control method for a fleet of mobile robots to traverse an unknown
cluttered environment where is occupied by multiple obstacles to trap a target.
We prove that each individual team member is able to traverse safely in the
region, which is cluttered by many obstacles with any shapes to trap the target
while using the sensors in some indefinite switching points and not
continuously, which leads to saving energy consumption and increasing the
battery life of the robots consequently. And finally, we propose a novel
navigation strategy for a unicycle mobile robot in a cluttered area with moving
obstacles based on virtual field force algorithm. The mathematical proof of the
navigation laws and the computer simulations are provided to confirm the
validity, robustness, and reliability of the proposed methods
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