A Distributed Kinodynamic Collision Avoidance System under ROS

Collision avoidance is very important problem in the domain of multi-robot interaction. In this paper we propose a new approach of collision avoidance in the context of the optimal control system synthesis problem definition with minimal information available. It is assumed that robots have a certain scope within which they can interact with static and dynamic phase constraints. A group of robots is considered to be homogeneous, and control system unit for reaching terminal states already available to robots. The control system which is responsible for collision avoidance is only activated when the nearest neighbor is located in the scope of the considered robot. The first important feature of this work is the fact that the collision avoidance between two robots is reciprocal with joint control system, without assigning priorities. Another key feature of this work is the complete absence of information about the environment and the current state of other robots at given time. Robots only share information with nearest neighbors if they locate in the scope of each other. We also present a computational experiment with mobile robots as control objects. A multilayer perceptron was used to approximate the control function. Weights of the perceptron were optimized in unsupervised paradigm by an algorithm belonging to the evolutionary strategies class. At the beginning of each epoch we generate a sample of collision scenarios for optimization, while the quality criterion of the achieved weights at the end of epoch is evaluated on a fixed test sample. Experimental results demonstrate strong ability of the optimized multilayer perceptron to map the relative state of two mobile robots to controls in order to avoid collisions.

In the last decade, robotic systems have penetrated human life more than human can imagine. In particular, the multi-mobile robotic systems have faced a fast growing due to the fact that by deploying a large collection of mobile robots the overall system has a high redundancy and offers the capability of handling more complex tasks. A group of mobile robots increases robustness against failures and provide flexibility to system changes. In order to achieve a fully autonomous operation, the control algorithm to coordinate the robots becomes more important and decisive. The controller has to meet different requirements that sometimes are conflicting. The main goal of this thesis is to evaluate performance of coordination control algorithms for a group of mobile robots. In the first part of this thesis, a modular framework for simulation and experiments of coordination control of mobile robots is presented. All connections between the modules follow the subscriber/publisher paradigm on the exchanged data, i.e. a component publishes data and other components can subscribe on that data. The data is identified by a named magazine, and actual data packets are called issues of the magazine. By means of simulation and experiments, it is demonstrated how the modularity in the framework allows straightforward modification of system configurations, design parameters, and control algorithms. In the second part, hierarchical coordination control of mobile robots is presented. The hierarchy consists of three layers: a high-level control for motion planning of the robots, a low-level control for motion execution, and a flexible layer to accommodate shifting of responsibilities. One advantage of using a hierarchical approach is the isolation of control design in each layer. Changes in the control strategy of a layer do not necessarily require adaptation of other layers. The proposed control algorithms are used to coordinate a group of unicycle mobile robots that realize the transportation system of an automated warehouse. Using the framework, series of simulations with different design parameters are conducted, as well as real-time experiments. Subsequently, performance analysis of the results is carried out. It is shown that the proposed algorithms are flexible to system changes and scalable to variation in the warehouse demands. The algorithms are robust against failures in the transportation systems. It is found that an algorithm, denoted high-level control, with higher throughputs requires more information sharing between the robots. This algorithm is less robust against failures compared to the algorithm, denoted low-level control, that yields lower throughput. In addition, a cost analysis of the proposed control algorithms is given. It is studied that the cost of realizing the transport using a group of mobile robots, coordinated by the proposed control algorithm, has similar performance-to-cost ratio compared to conveyor systems. This conclusion is very promising having in minds that the proposed control algorithm is not necessarily the optimal one. In the third part, the problem of simultaneous tracking of individual references and formation keeping for a group of mobile robots is investigated. The control algorithm is developed using dynamic feedback linearization. The stability proof is analyzed using the theorem on interconnected systems. Using a root-mean-square-like indicator, the trade off between individual tracking and formation keeping, as well as the influences of communication topologies are analyzed. From the analysis of real-time experiment results, it is found that the best formation keeping is obtained when all robots communicate. As a trade off, this requires a larger communication bandwidth and yields large individual tracking errors. Furthermore, the analysis suggests that to achieve optimal individual tracking and formation keeping simultaneously, there is less need to share information between the robots. In the fourth part, as a complement to the hierarchical control approach, a coordination control algorithm based on Model Predictive Control (MPC) is presented. Focusing on the practical aspects, a sequentially decentralized MPC, i.e. a single MPC computes the control signals of all robots where priority rules are used to determine which robots handled earlier in optimization procedure, is implemented. Using the similar automated warehouse environment, the sequentially decentralized MPC is validated in simulations and real-time experiments. For comparison purposes, a centralized MPC, i.e. a single MPC computes the control signals of all robots, is also implemented. Using completion time as indicators, the influences of the MPC parameters are investigated. It is found that the centralized MPC is better than the sequentially decentralizwed MPC, but with the cost of high computation load and limited number of robots in real-time application. Relevance and performance comparison between the MPC and the hierarchical control approach are presented. It is found that regardless of the control algorithm choices, in terms of completion time, a better performance is obtained when the control algorithm uses information from all robots in computing the control signals.

This paper shows the prospects and areas of application of groups of mobile robots. The implementation of many practical tasks by groups of robots requires the formation of a specific configuration. Different approaches to solving the formation task in groups of mobile robots are considered. It is shown that the complexity of group’s control increases with the number of robots. Moreover, in large groups of robots, the linear dimensions of the system may exceed the range of direct communication between robots using onboard communication devices. A formal statement of the formation task in a large group of mobile robots is given. The features of the periodic formation consisting of repeating geometric patterns are considered. A new method for the formation of a periodic system in a large group of mobile robots in conditions of limited communications has been proposed. The proposed method is based on the principles of decentralized control and swarm intelligence. The possibility of forming a complex periodic formations and multi-level formations in large groups of mobile robots is considered. The formation of such a system can be used in the tasks of monitoring, mapping, data collection and other applications.

This paper considers a rendezvous problem, where a group of wheeled mobile robots with sensing and power constraints is tasked to meet at a common destination. Each robot is modeled in the wheel kinematic level, which is governed by the wheel actuation power constraints. The group of robots is of a leader-follower network structure, where only the leader robot is aware of the desired destination and the follower robots are driven by the leader to the common destination via local sensing of neighboring robots. To ensure the availability of inter-robot communication, the motion of robots is constrained to preserve network connectivity, enabling leader-based guidance via connected paths. In addition, the motion of robots is further constrained to avoid collisions with other robots. To achieve these objectives, a guidance function based decentralized controller is developed to control the wheel velocities of each robot, which ensures global convergence to the destination while guaranteeing the network connectivity, collision avoidance, and not violating its sensing and actuator power capabilities. The simulation results demonstrate the effectiveness of the developed controller.

This paper presents a new design for formation control on a group of autonomous humanoid robots with obstacle and collision avoidance ability that inspired by human walking behavior in a pedestrian crowd. This human walking behavior model is known as Social Force Model (SFM) and has been widely used to predict individual movement in a crowd in many variety of situation. We combine consensus algorithm with virtual structure approach for formation control and SFM for obstacle and collision avoidance. In this research, we induced some factors in SFM into consensus equation and then implemented into a group of humanoid robots. The aim of the integration is to create a group of robots that are capable of carrying out its collective tasks while still able to maintain its safety. The use of perception effect, distinct obstacle and collision avoidance function on the algorithm will differentiate algorithm with the others. The attractive feature of this new design is the various behaviors of robots to go back to their initial formation after they avoid obstacles. In the case of robot is trapped in a crowded situation (singularity condition), robot will still trying to look a new position, until it find a condition that allow him to move forward. We verify the proposed algorithm by some simulations and experiments.

The method to organize a safe and optimal joint movement of a group of intelligent mobile robots that solve common tasks in a potentially dangerous territory is purpose. Such territories include: territory covered by fire, chemically contaminated territory, and another territory with the manifestation of an emergency in which robots will be of high utility. To solve this problem, a software platform for organizing group interactions of mobile objects has been developed and the principles of building its modular architecture are given. Using the platform, the developed technique for solving the problem was implemented and a series of experiments was conducted to evaluate its effectiveness. The technique is based on algorithms for efficiently representing the movement space in the form of a directed graph, finding the shortest path based on the a-star algorithm with an optimal heuristic function, and the logic of joint interaction of a group of mobile robots with the possibility of task distribution. Evaluation of the effectiveness of the developed method was carried out on the basis of a generalized method for calculating the stabilized value of the indication of the effectiveness criterion. A demonstration experiment is the task of simultaneously and safely collecting a group of robots in a target area in a potentially dangerous territory. The main parameter to be evaluated is simultaneity of the arrival of a group of robots depending on the number of robots in a group.

This paper presents an approach to coordinating a group of mobile robots that move along the designed paths. Subject to the constraints of velocity/acceleration bounds and collision avoidance, the robots are required to maintain the formation relationship while moving. The coordination is realized by posing the coordination problem as a velocity optimization problem in motion planning and then properly planning robots' velocities along the paths. The linear interactive and general optimizer (lingo) is utilized to achieve the velocity optimization goal. Simulations are performed on a group of mobile robots to demonstrate the effectiveness of the proposed approach in coordinated motion planning with the formation requirement.

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.

In this paper we study the problem of forming coalitions for dynamic tasks in multirobot systems. As robots, either individually or in groups, encounter new tasks for which individual or group resources do not suffice, robot coalitions that are collectively capable of meeting these requirements need to be formed. We propose a hybrid approach to this problem where coalitions proceed with the task if they have sufficient resources after liberating redundant members while they report it to a task coordinator in cases where their resources do not suffice. In turn, the task coordinator forms capable coalitions based on coalition formation games in which groups of robots are evaluated together in regards to each task’s required resources and cost of forming a coalition. The resulting coalitions are such that no group of robots has a viable alternative to staying within their assigned coalition. Thus, as new tasks are confronted, coalitions merge and split so that the resulting coalitions are capable of the newly encountered tasks. Simulations and experiments performed on groups of heterogeneous mobile robots demonstrate the effectiveness of the proposed approach.

In this paper, coordination and crash avoidance algorithm between a group of mobile robots is proposed based on the fuzzy control. Fuzzy control is developed to make the robots move in the predefined trajectories that must not collide. All the robots are coordinated so that they finish their trajectories in the same time, regardless of the length of the individual trajectories or the number of crash avoidance incidences between them. The simulation results obtained with three mobile robots are presented to show the performance of control, coordination and crash avoidance algorithm.

The article is devoted to the problem of long-term monitoring of vast territories and water areas by groups of mobile robots in order to timely prevent dangerous consequences of man-made or natural accidents. A group of robots starts from the base and begins collecting data from several work areas. For this, the group must be divided into subgroups according to the number of work areas. Some of the group’s robots remain in reserve at the base. When a robot runs out of energy, it can be replaced by another robot of the subgroup, or a robot from the reserve. In this paper, we propose a method and algorithms for the distribution of robots in a group into subgroups based on the multi-agent interaction of agents in working areas and agents of mobile robots. The differences between homogeneous and heterogeneous groups of robots are taken into account. The results of experimental studies are presented.KeywordsGroup of robotsDistributed computingFog roboticsMulti-agent technologies

In this article, the problem of performing different tasks with a group of mobile robots is addressed. To cope with issues like regulation to a point or trajectory tracking, a consensus scheme is considered. Three topologies were tested in simulation. The first goal was to make consensus in the group of robots, after the consensus point was relocated to achieve a regulation control. The last objective was to follow a desired trajectory moving the consensus point along the predefined path. The proposal was validated through experimental test with a group of three differential mobile robots.

One of the problems of group control, the distribution, of tasks in a group of mobile robots, is considered. The basic solution algorithm is proposed to use the “swarm intelligence” implemented on the basis of ant algorithms. This article is a development of the previous authors’ work, which reviewed the known approaches to the distribution of problems using swarm algorithms, described the ant algorithm in detail, and analyzed the resulting solution to the problem of single-criteria optimization. The problem statement, the working space model are presented, the robot function objectives and parameters characterizing their work are formalized. To solve the multi-criteria problem, the following quality criteria were selected: The amount of energy expended, the time it took to complete the tasks, and the number of robots involved. For multi-criteria optimization, the resulting vector optimality criterion is linearly converged by introducing additional parameters characterizing group control: The total efficiency of a group of robots, the specific amount of energy for the operation of the support group and the energy for moving one robot to the given coordinates. To implement the ant algorithm, the problem was presented in the form of a set of undirected weighted graphs on which the “ants” will build solutions. An example is given that shows a detailed transition from the initial problem setting to graphs. The experiments were carried out on the basis of a software-implemented algorithm for finding the optimal group behavior strategy for a multi-criteria objective function with weight coefficients. For a group of three robots, experimental data were obtained and the results analysed, and a solution was modeled for several groups of robots with different weights.KeywordsTask distributionGroup of robotsSwarm intelligenceAnt algorithm

Minimalist robots are functionally highly restricted but well suited for swarm robotic applications because of their low costs and small sizes. Connectivity maintenance and collision avoidance are challenging in minimalist swarm robotic systems due to a short communication range and the lack of positional and directional sensing. In this paper, we introduce a self-organizing control strategy for collective flocking of a swarm of minimalist robots with an aim to improve swarm connectivity and to reduce the chance of collision between robots. Based on the relative positional information built up via collaborations, each robot determines a collision-free operational polygon. This scheduling scheme coordinates the motion of the robots by dividing them into one group of immobile and one group of mobile robots, such that each mobile robot is surrounded by immobile robots serving as beacons. In addition, we introduce a cohesive force into motion planning, which has been shown to play an important role in maintaining a swarm during flocking. A new quantitative metric is introduced for measuring the connectivity of a swarm of agents with local communications, thereby, evaluating the performance of the proposed control scheme. We run extensive simulations using simulated Kilobots to examine the influence of different sources of noise and the size of swarms on the connectivity in the swarm and the speed of flocking. Finally, we implement the proposed algorithm on a swarm of real Kilobots to compare the flocking performance with and without the proposed control strategy for coordinated and collective motion.

This paper addresses the problem of flocking of mobile robots by bounded feedback. A control problem is formulated with the multiple goals: velocity consensus, collision avoidance, and cohesion maintenance. The flocking protocol is then obtained by means of control design. Particularly, a Lyapunov-like function is presented, the velocity control is designed to achieve coordination and linear speed consensus, and the steering control is designed for consensus on orientation. The convergence is verified via Barbalat's lemma. Simulation results with a group of mobile robots are presented to show the effectiveness of the proposed flocking control.