Multi-robot motion-formation distributed control with sensor self-calibration: experimental validation

In this paper, we present the design and implementation of a robust motion formation distributed control algorithm for a team of mobile robots. The primary task for the team is to form a geometric shape, which can be freely translated and rotated at the same time. This approach makes the robots to behave as a cohesive whole, which can be useful in tasks such as collaborative transportation. The robustness of the algorithm relies on the fact that each robot employs only local measurements from a laser sensor which does not need to be off-line calibrated. Furthermore, robots do not need to exchange any information with each other. Being free of sensor calibration and not requiring a communication channel helps the scaling of the overall system to a large number of robots. In addition, since the robots do not need any off-board localization system, but require only relative positions with respect to their neighbors, it can be aimed to have a full autonomous team that operates in environments where such localization systems are not available. The computational cost of the algorithm is inexpensive and the resources from a standard microcontroller will suffice. This fact makes the usage of our approach appealing as a support for other more demanding algorithms, e.g., processing images from onboard cameras. We validate the performance of the algorithm with a team of four mobile robots equipped with low-cost commercially available laser scanners.Comment: 6 pages. ICARCV 201

Cooperative distributed pick and place algorithm for mobile manipulators with camera feedback

The main goal of this research is to perform a pick and place task done by several mobile robots equipped with a robotic arm by using a distributed algorithm and a formation control law for keeping the shape. Nowadays, the industry sometimes needs the cooperation of different robots to achieve what cannot be reached by a single robot. Sometimes, the fact of using only a single robot can be either really expensive or not powerful enough and it is worth to implement a system with several agents. The studied case will be with four agents and it will be tested experimentally with the mobile nexus robots which are in the DTPA lab. The fact of being a task performed by several agents means that formation control theory will be taken into account, specifically the formation control Law designed by Garcia de Marina, Jayawardhana, and Cao [1]. Several studies and tests related to formation control have been performed in the PhD Nexus Group. Furthermore, some research about pick and place task has also been studied but only for a single robot. Because of this reason, the aim of this research is to extrapolate the results of the pick and place task done for one robot to a formation of several agents by using the formation control law previously specified. Moreover once the algorithm is tested in a four agent formation is relatively easy to change the number of agents by simply modifying some parameters. Challenges such as recognition of objects (Markers), tracking them (PI control) and keeping the formation of the agents (formation control theory) will be achieved by using the suitable sensors. For instance, a camera mounted on the robotic arm will be used for the recognition of objects, while a RPLidar Laser Scanner will be used for measuring the distances between robots and ensure that they keep the formationOutgoin

Localization Algorithms for GNSS-denied and Challenging Environments

In this dissertation, the problem about localization in GNSS-denied and challenging environments is addressed. Specifically, the challenging environments discussed in this dissertation include two different types, environments including only low-resolution features and environments containing moving objects. To achieve accurate pose estimates, the errors are always bounded through matching observations from sensors with surrounding environments. These challenging environments, unfortunately, would bring troubles into matching related methods, such as fingerprint matching, and ICP. For instance, in environments with low-resolution features, the on-board sensor measurements could match to multiple positions on a map, which creates ambiguity; in environments with moving objects included, the accuracy of the estimated localization is affected by the moving objects when performing matching. In this dissertation, two sensor fusion based strategies are proposed to solve localization problems with respect to these two types of challenging environments, respectively. For environments with only low-resolution features, such as flying over sea or desert, a multi-agent localization algorithm using pairwise communication with ranging and magnetic anomaly measurements is proposed in this dissertation. A scalable framework is then presented to extend the multi-agent localization algorithm to be suitable for a large group of agents (e.g., 128 agents) through applying CI algorithm. The simulation results show that the proposed algorithm is able to deal with large group sizes, achieve 10 meters level localization performance with 180 km traveling distance, while under restrictive communication constraints. For environments including moving objects, lidar-inertial-based solutions are proposed and tested in this dissertation. Inspired by the CI algorithm presented above, a potential solution using multiple features motions estimate and tracking is analyzed. In order to improve the performance and effectiveness of the potential solution, a lidar-inertial based SLAM algorithm is then proposed. In this method, an efficient tightly-coupled iterated Kalman filter with a build-in dynamic object filter is designed as the front-end of the SLAM algorithm, and the factor graph strategy using a scan context technology as the loop closure detection is utilized as the back-end. The performance of the proposed lidar-inertial based SLAM algorithm is evaluated with several data sets collected in environments including moving objects, and compared with the state-of-the-art lidar-inertial based SLAM algorithms

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area

An Auto-Adaptive Multi-Objective Strategy for Multi-Robot Exploration of Constrained-Communication Environments

The exploration problem is a fundamental subject in autonomous mobile robotics that deals with achieving the complete coverage of a previously unknown environment. There are several scenarios where completing exploration of a zone is a main part of the mission. Due to the efficiency and robustness brought by the multi-robot systems, exploration is usually done cooperatively. Wireless communication plays an important role in collaborative multi-robot strategies. Unfortunately, the assumption of stable communication and end-to-end connectivity may be easily compromised in real scenarios. In this paper, a novel auto-adaptive multi-objective strategy is followed to support the selection of tasks regarding both exploration performance and connectivity level. Compared with others, the proposed approach shows effectiveness and flexibility to tackle the multi-robot exploration problem, being capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time