Motion Planning and Reactive Control Algorithms for Object Manipulation in Uncertain Conditions

Abstract

This work proposes the application of several smart strategies for object manipulation tasks. A real-time flexible motion planning method was developed to be adapted to typical in-store logistics scenarios. The solution combines and optimizes some state-of-the-art techniques to solve object recognition and localization problems with a new hybrid pipeline. The algorithm guarantees good robustness and accuracy for object detection through depth images. A standard planner plans collision-free trajectories throughout the whole task while a proposed reactive motion control is active. Distributed proximity sensors were adopted to locally modify the planned trajectory when unexpected or misplaced obstacles intervene in the scene. To implement a robust grasping phase, a novel slipping control algorithm was used. It dynamically computes the grasp force by adapting it to the actual object physical properties so as to prevent slipping. Experimental results carried out in a typical supermarket scenario demonstrate the effectiveness of the presented methods.

Spectral Theory of Defective Matrices for Robust Joint Motion

“…Costanzo, Marco, et al performed a useful experiment using tactile perception data for implementing a reactive control algorithm [1]. NEO introduced by Jesse Haviland and Peter Corke approached the problem by using quadratic programming to implement the reactive control algorithm [2].…”

Spectral Theory of Defective Matrices for Robust Joint Motion

“…Costanzo, Marco, et al performed a useful experiment using tactile perception data for implementing a reactive control algorithm [1]. NEO introduced by Jesse Haviland and Peter Corke approached the problem by using quadratic programming to implement the reactive control algorithm [2].…”

A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective

“…Smart glasses can be used to pick parts from a shelf as the glass can read the barcode and the operator has free hands to do the picking activities [71]. Costanzo et al [73] developed a point cloud environment where a collaborative robot could perform pick and place activities for in-store logistics scenarios with application in warehouses. Liao et al [75] used a collaborative robot that received data from an IoT-based system equipped with a Raspberry Pi transmitting the destination place.…”