4,313 research outputs found
Realization of reactive control for multi purpose mobile agents
Mobile robots are built for different purposes, have different physical size, shape, mechanics and electronics. They are required to work in real-time, realize more than one goal simultaneously, hence to communicate and cooperate with other agents. The approach proposed in this paper for mobile robot control is reactive and has layered structure that supports multi sensor perception. Potential field method is implemented for both obstacle avoidance and goal tracking. However imaginary forces of the obstacles and of the goal point are separately treated, and then resulting behaviors are fused with the help of the geometry. Proposed control is tested on simulations where
different scenarios are studied. Results have confirmed the high performance of the method
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method
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
Study of Cooperative Control System for Multiple Mobile Robots Using Particle Swarm Optimization
The idea of using multiple mobile robots for tracking targets in an unknown environment can be realized with Particle Swarm Optimization proposed by Kennedy and Eberhart in 1995. The actual implementation of an efficient algorithm like Particle Swarm Optimization (PSO) is required when robots need to avoid the randomly placed obstacles in unknown environment and reach the target point. However, ordinary methods of obstacle avoidance have not proven good results in route planning. PSO is a self-adaptive population-based method in which behavior of the swarm is iteratively generated from the combination of social and cognitive behaviors and is an effective technique for collective robotic search problem. When PSO is used for exploration, this algorithm enables robots to travel on trajectories that lead to total
swarm convergence on some target
Spatial context-aware person-following for a domestic robot
Domestic robots are in the focus of research in
terms of service providers in households and even as robotic
companion that share the living space with humans. A major
capability of mobile domestic robots that is joint exploration
of space. One challenge to deal with this task is how could we
let the robots move in space in reasonable, socially acceptable
ways so that it will support interaction and communication
as a part of the joint exploration. As a step towards this
challenge, we have developed a context-aware following behav-
ior considering these social aspects and applied these together
with a multi-modal person-tracking method to switch between
three basic following approaches, namely direction-following,
path-following and parallel-following. These are derived from
the observation of human-human following schemes and are
activated depending on the current spatial context (e.g. free
space) and the relative position of the interacting human.
A combination of the elementary behaviors is performed in
real time with our mobile robot in different environments.
First experimental results are provided to demonstrate the
practicability of the proposed approach
An Approach for Multi-Robot Opportunistic Coexistence in Shared Space
This thesis considers a situation in which multiple robots operate in the
same environment towards the achievement of different tasks. In this situation,
please consider that not only the tasks, but also the robots themselves
are likely be heterogeneous, i.e., different from each other in their
morphology, dynamics, sensors, capabilities, etc. As an example, think
about a "smart hotel": small wheeled robots are likely to be devoted to
cleaning floors, whereas a humanoid robot may be devoted to social interaction,
e.g., welcoming guests and providing relevant information to
them upon request.
Under these conditions, robots are required not only to co-exist, but also
to coordinate their activity if we want them to exhibit a coherent and
effective behavior: this may range from mutual avoidance to avoid collisions,
to a more explicit coordinated behavior, e.g., task assignment or
cooperative localization.
The issues above have been deeply investigated in the Literature. Among
the topics that may play a crucial role to design a successful system, this
thesis focuses on the following ones:
(i) An integrated approach for path following and obstacle avoidance is
applied to unicycle type robots, by extending an existing algorithm [1]
initially developed for the single robot case to the multi-robot domain.
The approach is based on the definition of the path to be followed as a
curve f (x;y) in space, while obstacles are modeled as Gaussian functions
that modify the original function, generating a resulting safe path. The
attractiveness of this methodology which makes it look very simple, is
that it neither requires the computation of a projection of the robot position
on the path, nor does it need to consider a moving virtual target
to be tracked. The performance of the proposed approach is analyzed
by means of a series of experiments performed in dynamic environments
with unicycle-type robots by integrating and determining the position of
robot using odometry and in Motion capturing environment.
(ii) We investigate the problem of multi-robot cooperative localization
in dynamic environments. Specifically, we propose an approach where
wheeled robots are localized using the monocular camera embedded in
the head of a Pepper humanoid robot, to the end of minimizing deviations
from their paths and avoiding each other during navigation tasks.
Indeed, position estimation requires obtaining a linear relationship between
points in the image and points in the world frame: to this end, an
Inverse Perspective mapping (IPM) approach has been adopted to transform
the acquired image into a bird eye view of the environment. The
scenario is made more complex by the fact that Pepper\u2019s head is moving
dynamically while tracking the wheeled robots, which requires to consider
a different IPM transformation matrix whenever the attitude (Pitch
and Yaw) of the camera changes. Finally, the IPM position estimate returned
by Pepper is merged with the estimate returned by the odometry
of the wheeled robots through an Extened Kalman Filter. Experiments
are shown with multiple robots moving along different paths in a shared
space, by avoiding each other without onboard sensors, i.e., by relying
only on mutual positioning information.
Software for implementing the theoretical models described above have
been developed in ROS, and validated by performing real experiments
with two types of robots, namely: (i) a unicycle wheeled Roomba robot(commercially available all over the world), (ii) Pepper Humanoid robot
(commercially available in Japan and B2B model in Europe)
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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