Publications

Despite the recent advancements in the field of wearable robotics, powered lower limb exoskeletons remain a technology strongly restricted to research or clinical settings. Their uptake as a device to assist mobility in the everyday life is still a challenge due to their high-cost and limited adaptability to unconstrained environments. To overcome these limitations, we propose an open-hardware and open-source platform specifically designed for boosting the research on intelligent exoskeletons (InteLLExo). The proposed prototype adopts design solutions based on low-cost materials, standardized components and simple construction aimed at simplifying the replicability of the device and increase its accessibility. In addition, the control architecture of the exoskeleton is developed within the Robot Operating System (ROS) to facilitate the integration of the device with artificial intelligence and neurorobotic techniques. Overall, InteLLExo aims at combining accessible technologies with robotic intelligence with the purpose of accelerating the research on intelligent exoskeletons and their adoption in everyday life.

Powered lower limb exoskeletons (LLEs) have demonstrated significant potential in augmenting mobility and providing rehabilitative support for individuals with gait impairments. However, most assistive exoskeletons rely on predetermined gait trajectories, limiting their effectiveness in unstructured environments. To address this limitation, Environment Adaptive Gait Planning (EAGP) strategies have emerged, focusing on real-time trajectory adaptation based on environmental perception. This work introduces a novel approach to EAGP using Deep Reinforcement Learning (DRL) for generating adaptive foot trajectories, specifically targeting obstacle avoidance during ground walking. The proposed method optimizes trajectory smoothness, environmental interaction, and compliance with exoskeleton kinematic constraints, as validated by simulations. This study advances the state-of-the-art of adaptive gait planning by leveraging the generalization capabilities of DRL, paving the way for enhanced mobility in real-world applications.