Over the last decade, there has been a substantial surge in the demand for Autonomous Mobile Robots (AMRs) owing to their proficiency in navigating complex environments without the use of physical or electromechanical guidance devices. In particular, the exploration of obstacle avoidance holds paramount importance in AMR research, particularly as these robots are intended to function in settings with the presence of humans, vehicles, or other robots, necessitating the safe evasion of both stationary and moving obstacles. Within this context, this paper presents a solution for navigating in dynamic environments that can be easily integrated into the current ROS2 Navigation Stack (Nav2). The proposed solution is based on a Laser Imaging Detection and Ranging (LiDAR) sensor-based system, which allows for the detection of dynamic obstacles and estimating their velocities using a linear Kalman filter. The obtained information was integrated into the navigation system and the proposed solution was then validated through a campaign of simulation tests on an AMR prototype developed by HSG Engineering Srl. Simulations showed that this architecture guarantees safer navigation and represents a solid starting point for developing the robot’s navigation for industrial use.

Local Path-Planning Optimisation for an Industrial Autonomous Mobile Robot via Dynamic Obstacle Detection / Cecchi, Lorenzo; Bucci, Alessandro; Topini, Alberto; Ridolfi, Alessandro; Bono Bonacchi, Luigi. - STAMPA. - 1125 LNNS:(2024), pp. 126-135. (Intervento presentato al convegno 3rd International Symposium on Industrial Engineering and Automation - ISIEA 2024 tenutosi a Bolzano, Italy nel 19-21 June 2024) [10.1007/978-3-031-70465-9_14].

Local Path-Planning Optimisation for an Industrial Autonomous Mobile Robot via Dynamic Obstacle Detection

Cecchi, Lorenzo
;
Bucci, Alessandro;Topini, Alberto;Ridolfi, Alessandro;
2024

Abstract

Over the last decade, there has been a substantial surge in the demand for Autonomous Mobile Robots (AMRs) owing to their proficiency in navigating complex environments without the use of physical or electromechanical guidance devices. In particular, the exploration of obstacle avoidance holds paramount importance in AMR research, particularly as these robots are intended to function in settings with the presence of humans, vehicles, or other robots, necessitating the safe evasion of both stationary and moving obstacles. Within this context, this paper presents a solution for navigating in dynamic environments that can be easily integrated into the current ROS2 Navigation Stack (Nav2). The proposed solution is based on a Laser Imaging Detection and Ranging (LiDAR) sensor-based system, which allows for the detection of dynamic obstacles and estimating their velocities using a linear Kalman filter. The obtained information was integrated into the navigation system and the proposed solution was then validated through a campaign of simulation tests on an AMR prototype developed by HSG Engineering Srl. Simulations showed that this architecture guarantees safer navigation and represents a solid starting point for developing the robot’s navigation for industrial use.
2024
Latest Advancements in Mechanical Engineering (ISIEA 2024)
3rd International Symposium on Industrial Engineering and Automation - ISIEA 2024
Bolzano, Italy
19-21 June 2024
Goal 9: Industry, Innovation, and Infrastructure
Cecchi, Lorenzo; Bucci, Alessandro; Topini, Alberto; Ridolfi, Alessandro; Bono Bonacchi, Luigi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1401112
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