Sensors-based mobile robot for harsh environments: functionalities, energy consumption analysis and characterization

Abstract

Mobile robots and rovers play an important role in many industrial applications. Under certain constraints, they are suitable in harsh environments and conditions in which protracted human activity is not safe or permitted. In many circumstances, mechanical aspects and electrical consumption need to be optimized for autonomous and wheeled mobile robots. The paper illustrates the design of a semi-custom wheeled mobile robot with high-efficiency mono- or polycrystalline photovoltaic panel on the roof that supports the lithium ion batteries during particular tasks (e.g. navigating rough terrain, obstacles or steep paths) to extend the robot’s autonomy. An electronic controller was designed, and data acquisition related to power consumption performed using a specific experimental setup. The robot can detect parameters such as temperature, humidity, concentrations of toxic gas species and the presence of flames, making it particularly suitable for contaminated environments or industrial plants. For this aim, the mobile robot was equipped with a wide range of commercial sensors and a Global Positioning System receiver to track its position. In addition, using an HC-06 Bluetooth transceiver, the robot receives commands and instructions, and sends the acquired data to the developed IoTool smartphone application, where they are displayed to be analysed by user.

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

“…Furthermore, with constrained spaces and highly-contaminated facilities, fully autonomous solutions are unlikely to be considered safe or cost-effective in the near future, although there is a significant interest in the development of such capabilities ( Bandala et al, 2019 ). In this respect, the advent of more efficient sensors, electronics and algorithms enables under certain constraints the ability of robotic platforms to recognize dangerous scenarios in harsh environments ( Alon et al, 2021 ; De Fazio et al, 2021 ). Within our BT framework, this recognition is enabled through context data acquisition and interpretation across two channels 1) through the multimodal health state classification relying upon the output of the embedded sensors (e.g., evaluate human’s posture, gesture, facial expression, current emotional state; sense frustration, distress, work overload or possible injury situation) and 2) through direct communication with the human (e.g., ask what type of activities needs to be completed, what type of support is needed, check human availability to perform specific tasks).…”

Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments

“…2. Predicting the relation between two states of motion: The next state of a robot is also dependent on its current state, as shown in Equation (2). In this equation, x i y i is the initial state, and ẋ ẏ is the next state of the motion of a robot.…”

“…Estimating the next move of a robot in an unknown environment is a key concern [1][2][3][4]. Simultaneous Localization and Mapping (SLAM) technology is employed to estimate the current position of a robot on a known map.…”