Robot Learning by Demonstration with Dynamic Parameterization of the Orientation: An Application to Agricultural Activities

Abstract

This work proposes a Learning by Demonstration framework based on Dynamic Movement Primitives (DMPs) that could be effectively adopted to plan complex activities in robotics such as the ones to be performed in agricultural domains and avoid orientation discontinuity during motion learning. The approach resorts to Lie theory and integrates into the DMP equations the exponential and logarithmic map, which converts any element of the Lie group SO(3) into an element of the tangent space so(3) and vice versa. Moreover, it includes a dynamic parameterization for the tangent space elements to manage the discontinuity of the logarithmic map. The proposed approach was tested on the Tiago robot during the fulfillment of four agricultural activities, such as digging, seeding, irrigation and harvesting. The obtained results were compared to the one achieved by using the original formulation of the DMPs and demonstrated the high capability of the proposed method to manage orientation discontinuity (the success rate was 100 % for all the tested poses).

Integrated Route-Planning System for Agricultural Robots

“…However, until now, the development of agricultural robotics has been focused on task-specific implementation, without considering robots (either as sensing platforms or actuating platforms) as an integrated part of the entire farming system [26,27]. Although there are various works on the interaction of robots with other operational entities (e.g., cooperation between ground vehicles and aerial vehicles, [28] or cooperation between human and robots, [29][30][31][32][33][34][35][36]), this interaction cannot be considered as a connection between the robot and the entire farming system. In this sense, the decision-making in robotic operation is localized and does not come from a farm management system that considers all the interacting tasks and conditions of the farm.…”

Robotics and AI for Precision Agriculture

“…The controllers have demonstrated their usefulness in different real work environments at different nominal speeds, validated on a tracked mobile platform, in complex and varying field conditions, including loose soil, stones, and humidity. In [5], a learning-by-demonstration framework based on Dynamic Movement Primitives (DMPs) is proposed, which could be effectively adopted to plan complex activities in agricultural robotics avoiding orientation discontinuity during motion learning. The proposed approach is tested on the Tiago robot during the fulfillment of four agricultural activities, such as digging, seeding, irrigation and harvesting.…”