This paper proposes a method for detecting and recovering from immobilized states in mobile robots operating in outdoor environments. In recent years, the demand for autonomous mobile robots capable of navigating outdoor environments has been increasing. In particular, the delivery industry anticipates replacing human-driven transport with robots to cover the section between pickup and delivery points. However, achieving autonomous navigation in outdoor environments is significantly more challenging than in indoor settings as robots must coexist with pedestrians and vehicles while complying with traffic regulations, such as obeying pedestrian signals. In this study, we aimed to develop a mobile robot capable of autonomous navigation from a starting point to a target point in a real outdoor environment. We implemented a self-position estimation method using scan matching between the real-time point-cloud data and a loop-closure-corrected three-dimensional point-cloud map. This was integrated with a path-planning system that combines detection and recovery of a robot from immobilized states, prioritized velocity control, and event-based waypoints, enabling safe autonomous navigation that complies with traffic rules in outdoor environments. In addition, we implemented a control architecture that prioritizes asynchronous velocity commands sent in parallel. To enhance the autonomy of mobile robots, a method was proposed in this study in which the robot detects obstacles and recovers from an immobilized state when it becomes stuck after colliding with these obstacles that cannot be captured by obstacle sensors during navigation. To evaluate the effectiveness of the proposed method, we conducted experiments using an autonomous mobile robot developed in this study to recover it from being stuck on a low step that the obstacle sensor could not detect. In the final run in the Tsukuba Challenge 2024, the system successfully performed four tasks of signal recognition and crossings and completed visits to both pickup and delivery destinations. The mobile robot also completed a 2 km course and was awarded the Tsukuba Mayor’s Award.

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