1,830 research outputs found
Solving the potential field local minimum problem using internal agent states
We propose a new, extended artificial potential field method, which uses dynamic internal agent states. The internal states are modelled as a dynamical system of coupled first order differential equations that manipulate the potential field in which the agent is situated. The internal state dynamics are forced by the interaction of the agent with the external environment. Local equilibria in the potential field are then manipulated by the internal states and transformed from stable equilibria to unstable equilibria, allowiong escape from local minima in the potential field. This new methodology successfully solves reactive path planning problems, such as a complex maze with multiple local minima, which cannot be solved using conventional static potential fields
Model-Based Decentralized Policy Optimization
Decentralized policy optimization has been commonly used in cooperative
multi-agent tasks. However, since all agents are updating their policies
simultaneously, from the perspective of individual agents, the environment is
non-stationary, resulting in it being hard to guarantee monotonic policy
improvement. To help the policy improvement be stable and monotonic, we propose
model-based decentralized policy optimization (MDPO), which incorporates a
latent variable function to help construct the transition and reward function
from an individual perspective. We theoretically analyze that the policy
optimization of MDPO is more stable than model-free decentralized policy
optimization. Moreover, due to non-stationarity, the latent variable function
is varying and hard to be modeled. We further propose a latent variable
prediction method to reduce the error of the latent variable function, which
theoretically contributes to the monotonic policy improvement. Empirically,
MDPO can indeed obtain superior performance than model-free decentralized
policy optimization in a variety of cooperative multi-agent tasks.Comment: 24 page
Self-organized Polygon Formation Control based on Distributed Estimation
This paper studies the problem of controlling a multi-robot system to achieve
a polygon formation in a self-organized manner. Different from the typical
formation control strategies where robots are steered to satisfy the predefined
control variables, such as pairwise distances, relative positions and bearings,
the foremost idea of this paper is to achieve polygon formations by injecting
control inputs randomly to a few robots (say, vertex robots) of the group, and
the rest follow the simple principles of moving towards the midpoint of their
two nearest neighbors in the ring graph without any external inputs. In our
problem, a fleet of robots is initially distributed in the plane. The socalled
vertex robots take the responsibility of determining the geometric shape of the
entire formation and its overall size, while the others move so as to minimize
the differences with two direct neighbors. In the first step, each vertex robot
estimates the number of robots in its associated chain. Two types of control
inputs that serve for the estimation are designed using the measurements from
the latest and the last two time instants respectively. In the second step, the
self-organized formation control law is proposed where only vertex robots
receive external information. Comparisons between the two estimation strategies
are carried out in terms of the convergence speed and robustness. The
effectiveness of the whole control framework is further validated in both
simulation and physical experiments
FC Portugal 3D Simulation Team: Team Description Paper 2020
The FC Portugal 3D team is developed upon the structure of our previous
Simulation league 2D/3D teams and our standard platform league team. Our
research concerning the robot low-level skills is focused on developing
behaviors that may be applied on real robots with minimal adaptation using
model-based approaches. Our research on high-level soccer coordination
methodologies and team playing is mainly focused on the adaptation of
previously developed methodologies from our 2D soccer teams to the 3D humanoid
environment and on creating new coordination methodologies based on the
previously developed ones. The research-oriented development of our team has
been pushing it to be one of the most competitive over the years (World
champion in 2000 and Coach Champion in 2002, European champion in 2000 and
2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation
and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and
European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes
some of the main innovations of our 3D simulation league team during the last
years. A new generic framework for reinforcement learning tasks has also been
developed. The current research is focused on improving the above-mentioned
framework by developing new learning algorithms to optimize low-level skills,
such as running and sprinting. We are also trying to increase student contact
by providing reinforcement learning assignments to be completed using our new
framework, which exposes a simple interface without sharing low-level
implementation details
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