RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System

Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this paper, we present RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in real world. We will release our implementation as an open-source package.Comment: Conditionally accpeted by TR

Decentralized Unknown Building Exploration by Frontier Incentivization and Voronoi Segmentation in a Communication Restricted Domain

Exploring unknown environments using multiple robots poses a complex challenge, particularly in situations where communication between robots is either impossible or limited. Existing exploration techniques exhibit research gaps due to unrealistic communication assumptions or the computational complexities associated with exploration strategies in unfamiliar domains. In our investigation of multi-robot exploration in unknown areas, we employed various exploration and coordination techniques, evaluating their performance in terms of robustness and efficiency across different levels of environmental complexity. Our research is centered on optimizing the exploration process through strategic agent distribution. We initially address the challenge of city roadway coverage, aiming to minimize the travel distance of each agent in a scenario involving multiple agents to enhance overall system efficiency. To achieve this, we partition the city into subregions. and utilize Voronoi relaxation to optimize the size of postman distances for these subregions. This technique highlights the essential elements of an efficient city exploration. Expanding our exploration techniques to unknown buildings, we develop strategies tailored to this specific domain. After a careful evaluation of various exploration techniques, we introduce another goal selection strategy, Unknown Closest. This strategy combines the advantages of a greedy approach with the improved dispersal of agents, achieved through the randomization effect of a larger goal set. We further assess the exploration techniques in environments with restricted communication, presenting upper coordination mechanisms such as frontier incentivization and area segmentation. These methods enhance exploration performance by promoting independence and implicit coordination among agents. Our simulations demonstrate the successful application of these techniques in various complexity of interiors. In summary, this dissertation offers solutions for multi-robot exploration in unknown domains, paving the way for more efficient, cost-effective, and adaptable exploration strategies. Our findings have significant implications for various fields, ranging from autonomous city-wide monitoring to the exploration of hazardous interiors, where time-efficient exploration is crucial

Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Sampling-Based Multi-Robot Exploration

International audienceThis paper presents a new approach for collaborative multi-robot planning issues. The main problem that arises from multi-robot exploration is waiting situations. We consider that such problem involves two or more autonomous robots in an unknown environment. The mission objective is to explore the entire map, while trying to minimize its executing time. Moreover if each robot uses the same topological graph, then it uses the same exploration path that makes waiting situations arising. To solve this problem, we propose a new approach in this paper based on sampling iteratively maps to allow interactive multi-robot exploration. Our approach has been implemented in simulation and the experiments demonstrate that the overall completion time of an exploration task can be significantly reduced by our sampling-based method

Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Spherical Decomposition

This work proposes an autonomous multi-robot exploration pipeline that coordinates the behaviors of robots in an indoor environment composed of multiple rooms. Contrary to simple frontier-based exploration approaches, we aim to enable robots to methodically explore and observe an unknown set of rooms in a structured building, keeping track of which rooms are already explored and sharing this information among robots to coordinate their behaviors in a distributed manner. To this end, we propose (1) a geometric cue extraction method that processes 3D map point cloud data and detects the locations of potential cues such as doors and rooms, (2) a spherical decomposition for open spaces used for target assignment. Using these two components, our pipeline effectively assigns tasks among robots, and enables a methodical exploration of rooms. We evaluate the performance of our pipeline using a team of up to 3 aerial robots, and show that our method outperforms the baseline by 36.6% in simulation and 26.4% in real-world experiments

Towards a Probabilistic Roadmap for Multi-robot Coordination

International audienceIn this paper, we discuss the problem of multi-robot coordination and propose an approach for coordinated multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, called ACS-PRM, is a sampling-based method and consists of three steps including C-space sampling, roadmap building and motion planning. In contrast to previous approaches, our approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the system efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be obviously reduced by our ACS-PRM approach compared with previous approaches