Minimally-intrusive Navigation in Dense Crowds with Integrated Macro and Micro-level Dynamics

In mobile robot navigation, despite advancements, the generation of optimal paths often disrupts pedestrian areas. To tackle this, we propose three key contributions to improve human-robot coexistence in shared spaces. Firstly, we have established a comprehensive framework to understand disturbances at individual and flow levels. Our framework provides specialized computational strategies for in-depth studies of human-robot interactions from both micro and macro perspectives. By employing novel penalty terms, namely Flow Disturbance Penalty (FDP) and Individual Disturbance Penalty (IDP), our framework facilitates a more nuanced assessment and analysis of the robot navigation's impact on pedestrians. Secondly, we introduce an innovative sampling-based navigation system that adeptly integrates a suite of safety measures with the predictability of robotic movements. This system not only accounts for traditional factors such as trajectory length and travel time but also actively incorporates pedestrian awareness. Our navigation system aims to minimize disturbances and promote harmonious coexistence by considering safety protocols, trajectory clarity, and pedestrian engagement. Lastly, we validate our algorithm's effectiveness and real-time performance through simulations and real-world tests, demonstrating its ability to navigate with minimal pedestrian disturbance in various environments.

Towards High Efficient Long-horizon Planning with Expert-guided Motion-encoding Tree Search

Autonomous driving holds promise for increased safety, optimized traffic management, and a new level of convenience in transportation. While model-based reinforcement learning approaches such as MuZero enables long-term planning, the exponentially increase of the number of search nodes as the tree goes deeper significantly effect the searching efficiency. To deal with this problem, in this paper we proposed the expert-guided motion-encoding tree search (EMTS) algorithm. EMTS extends the MuZero algorithm by representing possible motions with a comprehensive motion primitives latent space and incorporating expert policies toimprove the searching efficiency. The comprehensive motion primitives latent space enables EMTS to sample arbitrary trajectories instead of raw action to reduce the depth of the search tree. And the incorporation of expert policies guided the search and training phases the EMTS algorithm to enable early convergence. In the experiment section, the EMTS algorithm is compared with other four algorithms in three challenging scenarios. The experiment result verifies the effectiveness and the searching efficiency of the proposed EMTS algorithm.