Probabilistic model-checking of collaborative robots: A human injury assessment in agricultural applications

Guevara, Leonardo; Khalid, Muhammad; Hanheide, Marc; Parsons, Simon

doi:10.1016/j.compag.2024.108987

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2024

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Cited by 2 publications

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Runtime Anomaly Monitoring of Human Perception Models for Robotic Systems

Arunachalam,

Huang,

Hanheide

et al. 2024

2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)

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Runtime Anomaly Monitoring of Human Perception Models for Robotic Systems

Arunachalam,

Huang,

Hanheide

et al. 2024

2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)

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An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

Urvina,

Guevara,

Vásconez

et al. 2024

Agriculture

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This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

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Probabilistic model-checking of collaborative robots: A human injury assessment in agricultural applications

Cited by 2 publications

References 39 publications

Runtime Anomaly Monitoring of Human Perception Models for Robotic Systems

Runtime Anomaly Monitoring of Human Perception Models for Robotic Systems

An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

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