117 research outputs found
The advancement of an obstacle avoidance bayesian neural network for an intelligent wheelchair
In this paper, an advanced obstacle avoidance system is developed for an intelligent wheelchair designed to support people with mobility impairments who also have visual, upper limb, or cognitive impairment. To avoid obstacles, immediate environment information is continuously updated with range data sampled by an on-board laser range finder URG-04LX. Then, the data is transformed to find the relevant information to the navigating process before being presented to a trained obstacle avoidance neural network which is optimized under the supervision of a Bayesian framework to find its structure and weight values. The experiment results showed that this method allows the wheelchair to avoid collisions while simultaneously navigating through an unknown environment in real-time. More importantly, this new approach significantly enhances the performance of the system to pass narrow openings such as door passing. © 2013 IEEE
Intention prediction for interactive navigation in distributed robotic systems
Modern applications of mobile robots require them to have the ability to safely and
effectively navigate in human environments. New challenges arise when these
robots must plan their motion in a human-aware fashion. Current methods
addressing this problem have focused mainly on the activity forecasting aspect,
aiming at improving predictions without considering the active nature of the
interaction, i.e. the robot’s effect on the environment and consequent issues such as
reciprocity. Furthermore, many methods rely on computationally expensive offline
training of predictive models that may not be well suited to rapidly evolving
dynamic environments.
This thesis presents a novel approach for enabling autonomous robots to navigate
socially in environments with humans. Following formulations of the inverse
planning problem, agents reason about the intentions of other agents and make
predictions about their future interactive motion. A technique is proposed to
implement counterfactual reasoning over a parametrised set of light-weight
reciprocal motion models, thus making it more tractable to maintain beliefs over the
future trajectories of other agents towards plausible goals. The speed of inference
and the effectiveness of the algorithms is demonstrated via physical robot
experiments, where computationally constrained robots navigate amongst humans
in a distributed multi-sensor setup, able to infer other agents’ intentions as fast as
100ms after the first observation.
While intention inference is a key aspect of successful human-robot interaction,
executing any task requires planning that takes into account the predicted goals and
trajectories of other agents, e.g., pedestrians. It is well known that robots
demonstrate unwanted behaviours, such as freezing or becoming sluggishly
responsive, when placed in dynamic and cluttered environments, due to the way in
which safety margins according to simple heuristics end up covering the entire
feasible space of motion. The presented approach makes more refined predictions
about future movement, which enables robots to find collision-free paths quickly
and efficiently.
This thesis describes a novel technique for generating "interactive costmaps", a
representation of the planner’s costs and rewards across time and space, providing
an autonomous robot with the information required to navigate socially given the
estimate of other agents’ intentions. This multi-layered costmap deters the robot from
obstructing while encouraging social navigation respectful of other agents’ activity.
Results show that this approach minimises collisions and near-collisions, minimises
travel times for agents, and importantly offers the same computational cost as the
most common costmap alternatives for navigation.
A key part of the practical deployment of such technologies is their ease of
implementation and configuration. Since every use case and environment is
different and distinct, the presented methods use online adaptation to learn
parameters of the navigating agents during runtime. Furthermore, this thesis
includes a novel technique for allocating tasks in distributed robotics systems,
where a tool is provided to maximise the performance on any distributed setup by
automatic parameter tuning. All of these methods are implemented in ROS and
distributed as open-source. The ultimate aim is to provide an accessible and efficient
framework that may be seamlessly deployed on modern robots, enabling
widespread use of intention prediction for interactive navigation in distributed
robotic systems
Brain-Controlled Multi-Robot at Servo-Control Level Based on Nonlinear Model Predictive Control
Using a brain-computer interface (BCI) rather than limbs to control multiple robots (i.e., brain-controlled multi-robots) can better assist people with disabilities in daily life than a brain-controlled single robot. For example, one person with disabilities can move by a brain-controlled wheelchair (leader robot) and simultaneously transport objects by follower robots. In this paper, we explore how to control the direction, speed, and formation of a brain-controlled multi-robot system (consisting of leader and follower robots) for the first time and propose a novel multi-robot predictive control framework (MRPCF) that can track users' control intents and ensure the safety of multiple robots. The MRPCF consists of the leader controller, follower controller, and formation planner. We build a whole brain-controlled multi-robot physical system for the first time and test the proposed system through human-in-the-loop actual experiments. The experimental results indicate that the proposed system can track users' direction, speed, and formation control intents when guaranteeing multiple robots’ safety. This paper can promote the study of brain-controlled robots and multi-robot systems and provide some novel views into human-machine collaboration and integration
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