On obstacle avoidance path planning in unknown 3D environments: A fluid-based framework

Compared with preprocessed obstacle environments, unknown environments are more challenging for path planning. In unknown environments, an agent can make decisions only by relying on the obstacle information detected by its onboard sensors. However, when facing non-convex obstacles, this limited detection information can easily trap the agent in a local optimum. In this paper, a nature-inspired methodology called Interfered Fluid Dynamic System (IFDS) is extended to anti-local-optimum obstacle avoidance in unknown 3D environments for the first time and a novel fluid-based path planning framework is proposed. First, the detection region of the agent is discretized. Then, spherical virtual obstacles (SVOs) located at detected discrete points are generated and memorized. Thus, obstacle avoidance in unknown environments is transformed into the avoidance of known SVOs. Next, the currently generated and memorized SVOs are input to the core of the framework, the IFDS algorithm, to produce repulsive effects, and the corresponding 3D avoidance path is resolved. On this basis, to address local optimum in cases with non-convex obstacles, and considering compatibility with the IFDS, the direction coefficient and sink-heading angular rate adjustment strategies, which belong to the same system as the IFDS, are introduced to modify the IFDS in this framework. Finally, receding horizon control is introduced to improve the obstacle avoidance performance. Simulations show that the proposed framework can enable the agent to autonomously jump out of the 3D non-convex obstacle environments with typical features of the local optimum, including wall-like and cave-like obstacles, and safely reach the destination.