Distributed formation control with obstacle and collision avoidance for humanoid robot

Distributed formation control of multi humanoid robot system with obstacle avoidance and collision avoidance are presented. The architecture consists of several layers, namely consensus tracking, consensus based formation control, behaviour and physical robot layers. The formation control system was simulated in MATLAB software before it was implemented to humanoid NAO Aldebaran robots. In the experimental study the visual odometry was used to track the robots’ position and their moving direction. The result of the experiment showed that the distributed formation control design was succeed in directing the robots into their planned formation, avoiding obstacles, and avoiding collisions among themselves with certain communication topology, shape of formation, obstacles’ position and their number.

This paper presents a distributed consensus formation control with collision and obstacle avoidance using fuzzy wavelet neural networks (FWNNs) for a group of networked mobile Mecanum-wheeled omnidirectional robots (MWORs) with uncertainties. The dynamic behavior of each uncertain MWOR is modeled by a reduced three-input–three-output second-order state equation with uncertainties, and the multi-MWOR system is modeled by graph theory. Using the Lyapunov stability theory and online learning the system uncertainties via FWNNs, an adaptive and distributed consensus backstepping control approach is presented to carry out formation control in the presence of uncertainties. Collision and obstacle-avoidance methods are provided to avoid any collisions among MWORs and their working environments. Five simulations are conducted to show the effectiveness and merit of the proposed method .

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.

This study proposes a distributed formation control framework for multi-agent systems by integrating deep reinforcement learning (DRL) with interdisciplinary methodologies from control theory, robotics, and artificial intelligence. Aimed at addressing collaborative formation challenges in dynamic environments, the approach uses graph-theoretic modeling for formation-keeping and obstacle avoidance and dynamic Attention-based Advantage Actor-Critic (DA-A2C) algorithm to solve collaborative formation problems in dynamic environments like autonomous drone swarms and robotic fleets. To balance formation consistency and obstacle repulsion, this strategy optimizes neighbor perception dynamic weight allocation using the dynamic Ω-zone mechanism. Experimental validations in simulated environments with static/moving obstacles show superior performance over SA2C, MATD3, and DDPG baselines, covering formation control and obstacle avoidance in complex situations and outperforming other algorithms in adaptability and robustness. This research can improve cross-domain applications in intelligent transportation, industrial automation, and emergency response systems. The method works in multi-robot formation systems, and future study will focus on 3D environment generalization and efficient communication protocols.

Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms (formation control term, velocity matching term, and obstacle avoidance term) is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.

This paper investigates distributed formation control of multi-mobile robot systems with collision avoidance. A novel nested constraints based anti-disturbance formation control scheme is established, which contains two parts. In the first part, a formation signal generator is constructed on the communication network by using proper distributed cooperative control approaches and artificial potential field-based methods, such that it "completes" the desired formation control task in its outputs. In the second part, by combining proper disturbance estimations, some nonlinear tracking controllers are designed for the robots to track the corresponding outputs of the generator asymptotically. A "nested constraint mechanism" is put forward accordingly and applied to limit the generator's outputs and the robots' tracking errors. In this way, the studied multi-mobile robot systems realize the formation control goal with collision avoidance. Compared with existing results, the proposed method has a better plug-and-play feature to deal with the tight encapsulations of the practical robot products, and has better convergence performances with the existence of disturbances. Some experiments are given to validate the effects of the proposed methods.

Multi-agent formation control is an important part of distributed perception and cooperation, which is convenient to complete various complex tasks and would be a key research direction in the future. This paper reviews the corresponding problems of formation control and the existing centralized and distributed formation control strategies. In particular, we discuss four types of distributed formation control methods based on position and displacement in the global coordinate system and distance and bearing in the nonglobal coordinate system, respectively. Moreover, this paper analyzes affine formation which does not require the global coordinate system. Combined with the current practical applications of multi-agent systems, the latest research for the formation control of the unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), unmanned surface vehicle (USV) and autonomous underwater vehicle (AUV) is given. Finally, the challenges and opportunities in this burgeoning field are discussed.

UUV two-phase formation and priority avoidance control considering steering amplitude limitation

This paper presents distributed formation control with guaranteed prescribed performances for multiple parametric uncertain marine surface vehicles (MSVs) subject to external time-varying disturbances, under an undirected graph. To provide the transient and steady-state performances of neighborhood errors, the prescribed performance constraints are imposed on the neighborhood errors and these prescribed performance constraints are guaranteed by the prescribed performance control methodology. The uncertain parameters and the upper bounds of the external disturbances are estimated by a distributed parameter estimate method, in which the additive terms consisting of parameter estimates from the neighbors are added to adaptive laws to improve the consensus speed of adaptive parameters. Based on dynamic surface control (DSC) technique, prescribed performance control methodology, and Lyapunov synthesis, distributed adaptive formation controllers are designed to force the following vehicles to perform desired formation pattern with guaranteed prescribed performances. The proposed controllers are distributed in the sense that each vehicle calculates its control signals based on local information from its neighborhood set, and the utilization of DSC technique could avoid using the neighbors' accelerations in the distributed formation controllers. An illustrated example is given to show the performances of the distributed formation controllers.

In this paper, a multi-agent formation control strategy with collision avoidance and obstacle avoidance in complex environments is proposed. Under a virtual leader structure, the multi-agent formation is realized through path planning and formation control. In path planning, a transitional region based on prediction horizon is used to replan reference trajectories and avoid obstacles in advance. The virtual-leader alternation strategy and feasible state searching strategy are adopted to avoid one side obstacles and crowded obstacles, respectively. Then the path planning optimization problem is formulated by mixed-integer quadratic programming (MIQP) to calculate trajectories collision-free with obstacles. In formation control, a distributed model predictive control (DMPC) algorithm is developed to form and maintain formation. In DMPC optimization problem, the non-convex obstacle avoidance constraints are replaced with deviation constraints and linear constraints to design terminal invariant set and reduce online computation burden. Under the whole formation control strategy, the tasks of obstacle avoidance and collision avoidance are both achieved and the agents maintain the formation as much as possible. Finally, the example with different obstacle situations is discussed to illustrate the effectiveness of the proposed strategy.

Vector Field Based Formation Control of Multi-Robot System

Cooperative control for multiple vehicles is a promising technology with the capability to improve traffic efficiency and fuel savings. Given its potential for both commercial and military applications, multiple unmanned vehicle formation has attracted considerable attention recently. In this paper, the use of formation control for connected and autonomous vehicles was explored and a novel distributed formation control approach was proposed. To begin, the evolution mechanism of multi-lane formation was investigated and a formation transition model based on a finite state machine was constructed. A bi-level formation control scheme was then proposed; this framework’s upper and lower levels were used to perform trajectory planning and MPC-based control, respectively. A novel trajectory planning approach was constructed by combining the distributed consensus algorithm and the potential field method. Moreover, additional acceleration constraints were imposed on the trajectory planning algorithm. Finally, three scenarios were designed to validate the proposed formation control algorithm using Webots. The results illustrate that a formation deployed with the proposed formation control algorithm can handle abnormal situations and realize consensus within 12s.

This paper investigates the formation control problem of heterogeneous discrete-time nonlinear multi-agent systems with structural uncertainties. The control objective is to make the outputs of agents reach a desired formation while tracking a leader. The formation control problem of heterogeneous discrete-time multi-agent systems is first converted into a robust output regulation problem, and then, a distributed formation control scheme based on output regulation framework is proposed. A $p$ -copy internal model is synthesized into the distributed formation control law such that the overall closed-loop system can be robust to structural uncertainties. It is shown that the heterogeneous discrete-time nonlinear multi-agent system can reach the desired formation regardless of small parameter permutations of agents under the proposed control law. Finally, simulation and experiment studies are provided to demonstrate the effectiveness of the proposed control law.

In view of the marine surface vehicles are subject to unknown time-varying external disturbance and its velocities are not available for feedback, a robust finite-time output feedback control scheme is presented for multiple marine surface vehicles to achieve precise formation control in this paper. Without neglecting the first time derivative of disturbance, a novel finite-time extended state observer (FTESO) is proposed to precisely estimate the external disturbance and velocity information in finite time. Based on the outputs of FTESO, the distributed formation controller is designed to guarantee that a group of marine surface vehicles can track the time-varying virtual leader in finite time with precise tracking performance. The errors of the proposed FTESO and formation controller can be guaranteed to converge to zero in finite time by using the homogeneous method and the Lyapunov theory. Numerical simulations are presented to illustrate the effectiveness of the proposed finite-time output feedback formation control scheme.