Thermal Image Sensing Model for Robotic Planning and Search

Abstract

This work presents a search planning system for a rolling robot to find a source of infra-red (IR) radiation at an unknown location. Heat emissions are observed by a low-cost home-made IR passive visual sensor. The sensor capability for detection of radiation spectra was experimentally characterized. The sensor data were modeled by an exponential model to estimate the distance as a function of the IR image’s intensity, and, a polynomial model to estimate temperature as a function of IR intensities. Both theoretical models are combined to deduce a subtle nonlinear exact solution via distance-temperature. A planning system obtains feed back from the IR camera (position, intensity, and temperature) to lead the robot to find the heat source. The planner is a system of nonlinear equations recursively solved by a Newton-based approach to estimate the IR-source in global coordinates. The planning system assists an autonomous navigation control in order to reach the goal and avoid collisions. Trigonometric partial differential equations were established to control the robot’s course towards the heat emission. A sine function produces attractive accelerations toward the IR source. A cosine function produces repulsive accelerations against the obstacles observed by an RGB-D sensor. Simulations and real experiments of complex indoor are presented to illustrate the convenience and efficacy of the proposed approach.

Neural control and coordination of decentralized transportation robots

“…Prior work on wheeled robot path planning used partial derivatives to search and find a global goal, 40 and a system of nonlinear partial trigonometric derivatives for planning and searching 41 was reported. Other work presented partial exponential derivatives for local navigation in highly dynamic environments.…”

Rolling Biped Polynomial Motion Planning

“…Alternatively, when there is only robot's local sensor data and the whole environment is unknown, it uses feedback from local observations. Planning methods can be generalized into four types: deterministic (based on mathematical numeric/analytic functions and models) [1][2][3], stochastic (recursive numerical methods based on probabilistic uncertainties) [4][5][6], heuristic (algorithms based on logical control and humanheuristic decision-making) [7][8][9][10], and mixed planning methods [11][12][13].…”

Aspects Regarding of a UGV Fire Fighting Thermal Shield

“…Proc. 2021, 6, 83 2 of 9 artificial obstacles [37][38][39][40][41][42][43]; determining limitations due to the low level of predictability of the evolution of the fire [44][45][46][47]; determining limitations due to the level of thermal gradients of the heat sources [48][49][50][51][52][53]; and identifying the material characteristics specific to the structure of the robot and to the components destined to the protection of the robot and the equipment (sensors, batteries, controllers, etc.) against high temperatures [54][55][56].…”

“…The purpose of this article is to analyze and validate a numerical-analytical model for the behaviour of an item thermal protection equipment, namely a thermal shield (Figure 1). payload [32][33][34][35][36]; identifying the dynamical characteristics depending on the terrain and artificial obstacles [37][38][39][40][41][42][43]; determining limitations due to the low level of predictability of the evolution of the fire [44][45][46][47]; determining limitations due to the level of thermal gradients of the heat sources [48][49][50][51][52][53]; and identifying the material characteristics specific to the structure of the robot and to the components destined to the protection of the robot and the equipment (sensors, batteries, controllers, etc.) against high temperatures [54][55][56].…”