An Intelligent Mobile Platform with Shared Autonomy

Date

2025-9-01

Author

Çelik, Cankan

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Substantial musculoskeletal risk persists in manual transport tasks across manufacturing and healthcare. Unlike lifting, pushing and pulling impose high shear forces on spinal discs, whose tolerance is significantly lower than their compression limits. Ergonomic research shows that pushing heavy hospital beds, carts or other wheeled equipment can easily exceed recommended shear-force limits, and experiments demonstrate that individuals typically push about 20% beyond recommended thresholds before discomfort causes them to stop. To reduce such risks, this thesis presents a force-guided mobile robot platform that augments a worker’s strength while keeping control intuitive. A differential-drive vehicle equipped with a cost-effective load-cell handlebar senses the operator’s push-pull and turning forces; these inputs can be represented as a forward/backward force vector and a torque about the centerline. By decomposing them into linear and rotational components and modeling them as spring-damper elements, the human–robot dynamics are integrated into the robot’s control laws. Novel virtual dynamics and anti-windup with dynamic clamping provide smooth propulsion and reject force spikes during high-inertia tasks. An elliptic repulsive field algorithm supplies obstacle avoidance aligned with human intuition, and a dual-adaptive velocity loop adjusts gains for payload and slope variation. User trials with different payloads, slopes, and guidance conditions show that the proposed platform reduces effort and work, improves comfort, eliminates collisions, and maintains consistent haptic feedback. Overall, combining biomechanical modelling with adaptive force-sensing control enables semi-autonomous assistive vehicles that protect workers from musculoskeletal injury while preserving their agency.

Subject Keywords

Adaptive Admittance Control of Mobile Robots, Physical Human-Robot Interaction, Haptics, Shared Autonomy, Elliptic Repulsive Field

URI

https://hdl.handle.net/11511/116191

Collections

Graduate School of Natural and Applied Sciences, Thesis