Prescribed Performance Control of a Human-Following Surveillance Robot with Incomplete Observation

For people with lower limb muscle weakness, effective and timely rehabilitation intervention is essential for assisting in daily walking and facilitating recovery. Numerous studies have been conducted on rehabilitation robots; however, some critical issues in the field of human-following remain unaddressed. These include potential challenges related to the loss of sensory signals for intention recognition and the complexities associated with maintaining the relative pose of robots during the following process. A human-following surveillance robot is introduced as the basis of the research. To address potential interruptions in motion signals, such as data transmission blockages or body occlusion, we propose a human walking intention estimation algorithm based on set-membership filtering with incomplete observation. To ensure uninterrupted user walking and maintain an effective aid and detection range, we propose a human-following control algorithm based on prescribed performance. The experiment verifies the effectiveness of the proposed methods. The proposed intention estimation algorithm achieves continuous and accurate intention recognition under incomplete observation. The control algorithm presented in this paper achieves constrained robot following with respect to the relative pose.