Daniel Herrera

Optimization methodology to fruit grove mapping in precision agriculture

A method capable of efficiently mapping a semi-structured environment is presented.Grove mapping based on LiDAR and the GPS locations of the corner trees is given.An optimization tool that adjusts measurements acquired by a mobile robot is used.The technique was tested in an olive grove located in San Juan - Argentina.It is incorporated a novel filtering technique of unlikely data. The mapping of partially structured agricultural environments is a valuable resource for precision agriculture. In this paper, a technique for the mapping of a fruit grove by a mobile robot is proposed, which uses only front laser information of the environment and the exact position of the grove corners. This method is based on solving an optimization problem with nonlinear constraints, which reduces errors inherent to the measurement process, ensuring an efficient and precise map construction. The resulting algorithm was tested in a real orchard environment. For this, it is also developed a data filtering method capable to comply efficiently the observation-feature matching. The maximum average error obtained by the methodology in simulations was about 13cm, and in real experimentation was about 36cm.

Human-robot interaction in precision agriculture: Sharing the workspace with service units

Currently, Chile and Argentina experience serious challenges that affect their agricultural productivity. For example, in Chile, the loss of farmable field due to recent earthquakes, and volcanic eruptions as well as the loss of water reserves due to climate changes are affecting the agriculture. Additionally, both countries are facing a same problem: the loss of human labor force. Field workers are migrating from the farm to other fields in the industry which offer them more stable and more profitable jobs (like the mining industry in Chile, or car assembling lines in Argentina). In this adverse scenario, it becomes necessary to introduce and develop agricultural automation and sensing technologies for both primary (harvesting, seeding, fertilizing, spraying) and secondary tasks (grove supervision, weed detection, hauling, mowing). However, fully robotized farms are not yet a possibility since the transition from human labor force dependent farming to autonomous farming needs to be smooth and requires legal regulation not yet in discussion. In this paper, we summarize the state of the art in human-robot interaction in farmable fields, with emphasis in the current constrains associated with flexible automatization of farms in Argentina and Chile. In particular, we introduce the guidelines for designing a humanrobot interaction strategy for harvesting tasks, that could be used for other agricultural tasks.

Dynamic Modeling and Identification of an Agriculture Autonomous Vehicle

In the present article, it is presented the modeling and identification of an autonomous vehicle that has been designed for agricultural tasks. With the purpose of defining the best model structure, different models have been presented. Particularly, it is assumed that the lateral and longitudinal dynamics are decoupled dynamics, and based on this assumption these are modeled and identified in an isolated way. Particular emphasis was made in lateral and rotational dynamics. The vehicle under study is a quadricycle (ATV) that has been modified and adapted to work in an autonomous way. It has been presented simulation proofs and experimentation with the real vehicle that allows guaranteeing the performance of the developed models.

Controllers design for differential drive mobile robots based on extended kinematic modeling

This paper presents the simulation results of the controllers design for differential-drive mobile robot (DDMR) using a novel modeling method, which is based on the inclusion of the sideway velocity into the kinematic modeling, in order to obtain a holonomic-like model. Next, non-holonomic constraint is introduced assuming that the sideway slipping is measurable. The controller design considers a variable position for the point of interest and takes into account also the robot constrained inputs. The obtained inverse kinematics controller is differentiable, time invariant and naturally incorporates the sideway slipping which is considered measurable. Moreover, the proposed controller can be used for both: trajectory tracking and path following by setting appropriate desired values at the planning stage. The Lyapunov theory is used to prove the stability of the control system. Simulator includes a robot dynamics module that supports physics engines. Obtained simulations results show a high performance for both tasks.

Human interaction dynamics for its use in mobile robotics: Impedance control for leader-follower formation

A complete characterization of the behavior in human-robot interactions U+0028 HRI U+0029 includes both: the behavioral dynamics and the control laws that characterize how the behavior is regulated with the perception data. In this way, this work proposes a leader-follower coordinate control based on an impedance control that allows to establish a dynamic relation between social forces and motion error. For this, a scheme is presented to identify the impedance based on fictitious social forces, which are described by distance-based potential fields. As part of the validation procedure, we present an experimental comparison to select the better of two different fictitious force structures. The criteria are determined by two qualities: least impedance errors during the validation procedure and least parameter variance during the recursive estimation procedure. Finally, with the best fictitious force and its identified impedance, an impedance control is designed for a mobile robot Pioneer 3AT, which is programmed to follow a human in a structured scenario. According to results, and under the hypothesis that moving like humans will be acceptable by humans, it is believed that the proposed control improves the social acceptance of the robot for this kind of interaction.

Dynamic emulation of human locomotion through mobile robots

Representations of human actions allow not only to infer the combining structure of complex movements, but also by creating a socially acceptable behavior. Take the case of a pedestrian following another one; he uses a mental kinematic control that allows to keep a relative distance and orientation between them. Additionally, his gait is also limited by its non holonomic and dynamic nature. Under the hypothesis that moving like humans will be acceptable for humans, this paper proposes a mechanism to indirectly identify human locomotion dynamics by using a kinematic leader-follower control as indirect inputs. The parameters of both of them are identified together by using human-human interaction data. Additionally, a control scheme based on feedback linearization is proposed to emulate the identified human dynamics. Finally, validation procedures over the measures of an experimental mobile robot are used to show the emulation of human locomotion dynamics. The results show that the robot is capable to emulate human locomotion dynamics, and, it could be useful to improve the social acceptance during human-robot interactions.

Behavioral dynamics of the human locomotion for improving the social acceptance of mobile robots

A complete characterization of the behavior in human-robot interactions (HRI) include both: the behavioral dynamic and the control laws that characterizes the way as the behavior is regulated with the perception data. In this way, when a pedestrian follows another one, it uses a mental kinematic control that allows to keep a relative distance and orientation between them, however it is also limited by its non holonomic and dynamic nature. Therefore, this paper proposes a mechanism to indirectly identify a first-order dynamic model of the human-locomotion by using a kinematic leader-follower control as a linkage of velocity references. Thus, experimental human-human interaction data is used to identify and validate the proposed model. This model is expected to be used to predict or to emulate human dynamic behavior by improving the social acceptance of robots.

Human-Robot Interaction: Legible behavior rules in passing and crossing events

In human-robot interaction, the incorporation of social rules results to be crucial to guarantee the comfort of the human. It must be obtained by means of improving the legibility of the robot motions, because developing soft and smooth motion does not guarantee entirely a social acceptable motion. Hence, in this paper a novel fuzzy logic approach is proposed to incorporate social rules in walking events, where the relative positions, orientations, distances and velocities between the robots and the humans are considered. Additionally to verify its performance, its incorporation in a path-following control through social forces is proposed. The simulation results demonstrate that the system is able to overcome many usual interferer situations, and to adapt its behavior to different interference events over time.

Port-Hamiltonian modelling of a car-like robot

This paper proposes a dynamical modelling of a car-like robot based on the port-Hamiltonian approach. It consists in projecting the dynamics of a free body into the possible velocity space determined by the non-holonomic constraints of the vehicle. For this approach, it is considered a simplified bicycle-like representation of the Ackermann mechanism with rear traction and steering control on the front wheel. Simulations are given to illustrate the effectiveness of the approach.