Parallel control model for navigation tasks on service robots

Abstract

Autonomous mobility remains an open research problem in robotics. This is a complex problem that has its characteristics according to the type of task and environment intended for the robot’s activity. Service robotics has in this sense problems that have not been solved satisfactorily. These robots must interact with human beings in environments designed for human beings, which implies that one of the basic sensors for structuring motion control and navigation schemes are those that replicate the human optical sense. In their normal activity, robots are expected to interpret visual information in the environment while following a certain motion policy that allows them to move from one point to another in the environment, consistent with their tasks. A good optical sensing system can be structured around digital cameras, with which it can apply visual identification routines of both the trajectory and its environment. This research proposes a parallel control scheme (with two loops) for the definition of movements of a service robot from images. On the one hand, there is a control loop based on a visual memory strategy using a convolutional neural network. This system contemplates a deep learning model that is trained from images of the environment containing characteristic elements of the navigation environment (various types of obstacles and different cases of free trajectories with and without navigation path). To this first loop is connected in parallel a second loop in charge of defining the specific distances to the obstacles using a stereo vision system. The objective of this parallel loop is to quickly identify the obstacle points in front of the robot from the images using a bacterial interaction model. These two loops form an information feedback motion control framework that quickly analyzes the environment and defines motion strategies from digital images, achieving real-time control driven by visual information. Among the advantages of our scheme are the low processing and memory costs in the robot, and the no need to modify the environment to facilitate the navigation of the robot. The performance of the system is validated by simulation and laboratory experiments.

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

“…Moreover, Table 4 lists the multimodal navigation. The applications of the multimodal navigation consist of entertainment [ 143 ], security [ 133 ], transport [ 2 , 106 , 107 , 110 , 112 , 147 ], assistance [ 2 , 16 , 108 ], exploration [ 3 , 134 , 135 , 138 , 141 ], social applications [ 12 , 140 ], tracking [ 105 , 125 , 131 , 145 , 146 ], caring and monitoring [ 113 ], disaster monitoring or search and rescue [ 136 , 149 ], floor cleaning [ 137 ], wheeled robot [ 109 ], person following [ 129 ], and false-ceiling inspection [ 157 ]. The combination of virtual sensors and neural networks is most commonly used in multimodal navigation, which represents 77.19% of the cited research.…”

“…It used vestibular, visual, and kinesthetic inputs incorporated with the objective function. Montiel et al [ 113 ] designed a parallel control model based on an optical sensing system to define the movements and a convolutional neural network (CNN) to analyze the environment and motion strategies to achieve real-time control. It was proposed with two loops as a feedback motion control framework for service robotics related to monitoring and caring for people.…”

Implementation and Evaluation of Advantage Actor-Critic Algorithm on a Desktop Computer with a Multi-Core CPU

“…In this paper, the implementation and evaluation of the A2C algorithm are performed. To generate knowledge, multiple agents are run synchronously in parallel, each in its environment (all identical instances), letting each one finish its interaction process before starting a new epoch of the system adjustment [21]. At any instant of the tuning process, each agent experiences a different state, which allows the algorithm to decorrelate the data of each agent.…”