9,196 research outputs found
Potential field based navigation for planetary rovers using internal states
The work in this paper aims to introduce analysis and applications for the internal state model which is a new model for a swarm of rovers interacting via pair-wise attractive and repulsive potentials. The internal state model updates the state of the art in overcoming the local minima problem through solving the problem with comparatively lower computation cost than other methods. The simulations results show that using the internal state model, a swarm of planetary rovers, rather than moving in a static potential field, are able to manipulate the potential according to their estimation of whether they are moving towards or away from the goal, which allows them to escape from and maneuver around a local minimum in the potential field to reach a goal. An application of a swarm of rovers to solve the problem for different shaped obstacles is introduced to show the efficiency of the model. The model proves stable convergence to a goal and provides similarities with the behaviour of real groups of animals
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
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
Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
Localization in challenging, natural environments such as forests or
woodlands is an important capability for many applications from guiding a robot
navigating along a forest trail to monitoring vegetation growth with handheld
sensors. In this work we explore laser-based localization in both urban and
natural environments, which is suitable for online applications. We propose a
deep learning approach capable of learning meaningful descriptors directly from
3D point clouds by comparing triplets (anchor, positive and negative examples).
The approach learns a feature space representation for a set of segmented point
clouds that are matched between a current and previous observations. Our
learning method is tailored towards loop closure detection resulting in a small
model which can be deployed using only a CPU. The proposed learning method
would allow the full pipeline to run on robots with limited computational
payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info:
https://ori.ox.ac.uk/esm-localizatio
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