Aerial robots are widely deployed, but highly cluttered environments such as dense forests remain inaccessible to drones and even more so to swarms of drones. In these scenarios, previously unknown surroundings and narrow corridors combined with requirements of swarm coordination can create challenges.

To overcome this challenge and enable swarm navigation in the wild, a team of researchers develop miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors.

Source: Research Article

The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, inter-robot collision avoidance, dynamical feasibility, swarm coordination, and so on, thus realizing an extensible planner.

Furthermore, the proposed planner deforms trajectory shapes and adjusts time allocation synchronously based on spatial-temporal joint optimisation. A high-quality trajectory thus can be obtained after exhaustively exploiting the solution space within only a few milliseconds, even in the most constrained environment.

The planner is finally integrated into the developed palm-sized swarm platform with onboard perception, localization, and control. Benchmark comparisons validate the superior performance of the planner in trajectory quality and computing time. Various real-world field experiments demonstrate the extensibility of our system.

The approach evolves aerial robotics in three aspects:

• the capability of cluttered environment navigation,
• extensibility to diverse task requirements, and
• coordination as a swarm without external facilities

This experiment is designed to demonstrate swarm navigation with full autonomy in a highly dense wild, i.e., a bamboo forest, without harming themselves or the plants.