add publications and update author linking

Author: pluto

content/publication/journal-article/Swarm-of-micro-flying-robots-in-the-wild/index.md

@@ -1,5 +1,5 @@
 ---
-title: 'Swarm of micro flying robots in the wild'
+title: "Swarm of micro flying robots in the wild"
 
 # Authors
 authors:
@@ -15,20 +15,20 @@ authors:
   - Chao Xu
   - Fei Gao
 
-date: '2022-05-04'
-doi: '10.1126/scirobotics.abm5954'
+date: "2022-05-04"
+doi: "10.1126/scirobotics.abm5954"
 
 # Schedule page publish date (NOT publication's date).
-publishDate: '2022-05-04'
+publishDate: "2022-05-04"
 
 # Publication type.
-publication_types: ['article-journal']
+publication_types: ["article-journal"]
 
 # Publication name.
-publication: 'Science Robotics, Vol 7, Issue 66'
-publication_short: 'Science Robotics'
+publication: "Science Robotics"
+publication_short: ""
 
-abstract: 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 enable swarm navigation in the wild, we 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. The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, and 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 optimization. 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. Our approach evolves aerial robotics in three aspects: capability of cluttered environment navigation, extensibility to diverse task requirements, and coordination as a swarm without external facilities.
+abstract: "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 enable swarm navigation in the wild, we 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. The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, and 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 optimization. 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. Our approach evolves aerial robotics in three aspects: capability of cluttered environment navigation, extensibility to diverse task requirements, and coordination as a swarm without external facilities."
 
 # Summary.
 # summary: We 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 to enable swarm navigation in the wild.