Collision Avoidance In Multi-robot Systems Research Articles

Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

AbstractCollision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

The Before, During, and After of Multi-robot Deadlock

Collision avoidance for multi-robot systems is a well-studied problem. Recently, control barrier functions (CBFs) have been proposed for synthesizing controllers that guarantee collision avoidance and goal stabilization for multiple robots. However, it has been noted that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that causes the robots to stall before reaching their goals. In this paper, we analyze the closed-loop dynamics of robots using CBFs, to characterize controller parameters, initial conditions, and goal locations that invariably lead the system to deadlock. Using tools from duality theory, we derive geometric properties of robot configurations of an N robot system once it is in deadlock and we justify them using the mechanics interpretation of KKT conditions. Our key deductions are that (1) system deadlock is characterized by a force equilibrium on robots and (2) deadlock occurs to ensure safety when safety is at the brink of being violated. These deductions allow us to interpret deadlock as a subset of the state space, and we show that this set is non-empty and located on the boundary of the safe set. By exploiting these properties, we analyze the number of admissible robot configurations in deadlock and develop a provably correct decentralized algorithm for deadlock resolution to safely deliver the robots to their goals. This algorithm is validated in simulations as well as experimentally on Khepera-IV robots For an interactive version of this paper, please visit https://arxiv.org/abs/2206.01781.

Fully Distributed Region-Reaching Control with Collision Avoidance for Multi-robot Systems

SUMMARYThis paper proposes a fully distributed continuous region-reaching controller for multi-robot systems which can effectively eliminate the chattering issues and the negative effects caused by discontinuities. The adaptive control gain technique is employed to solve the distributed region-reaching control problem. By performing Lyapunov function-based stability analysis, it is shown that all the robots can move cohesively within the desired region under the proposed distributed control algorithm. In addition, collision avoidance and velocity matching within the moving region can be guaranteed under properly designed control gains. Simulation examples are given to verify the capabilities of the proposed control method.

Multi-robot path planning based on improved artificial potential field and fuzzy inference system1

In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems.

Why Does Symmetry Cause Deadlocks?

Abstract Collision avoidance for multirobot systems has been studied thoroughly. Recently, control barrier functions (CBFs) have been proposed to mediate between collision avoidance and goal achievement for multiple robots. However, it has been noted that reactive controllers (such as CBFs) are prone to deadlock, an equilibrium that causes the robots to stall before reaching their goals. In this paper, we formally analyze two and three robot systems and discover circumstances under which CBFs cause deadlocks using duality theory. For the two robot system, we consider mutually heterogeneous robots (such as one more vigorous or closer to its goal than the other) and prove that this heterogeneity does not help in preventing deadlock. We then consider three robots, and conclude from these two scenarios that the geometric symmetry resulting from robots’ initial positions and goals constrains CBFs to generate velocities that render deadlock stable. Thus, conferring skewness to the system can help evade deadlock.

Evolutionary Dynamics of Multi-Agent Learning: A Survey

The interaction of multiple autonomous agents gives rise to highly dynamic and nondeterministic environments, contributing to the complexity in applications such as automated financial markets, smart grids, or robotics. Due to the sheer number of situations that may arise, it is not possible to foresee and program the optimal behaviour for all agents beforehand. Consequently, it becomes essential for the success of the system that the agents can learn their optimal behaviour and adapt to new situations or circumstances. The past two decades have seen the emergence of reinforcement learning, both in single and multi-agent settings, as a strong, robust and adaptive learning paradigm. Progress has been substantial, and a wide range of algorithms are now available. An important challenge in the domain of multi-agent learning is to gain qualitative insights into the resulting system dynamics. In the past decade, tools and methods from evolutionary game theory have been successfully employed to study multi-agent learning dynamics formally in strategic interactions. This article surveys the dynamical models that have been derived for various multi-agent reinforcement learning algorithms, making it possible to study and compare them qualitatively. Furthermore, new learning algorithms that have been introduced using these evolutionary game theoretic tools are reviewed. The evolutionary models can be used to study complex strategic interactions. Examples of such analysis are given for the domains of automated trading in stock markets and collision avoidance in multi-robot systems. The paper provides a roadmap on the progress that has been achieved in analysing the evolutionary dynamics of multi-agent learning by highlighting the main results and accomplishments.

Collision avoidance in multi-robot systems based on multi-layered reinforcement learning

It is important for a robot to acquire adaptive behaviors for avoiding surrounding robots and obstacles in complicated environments. Although the introduction of a learning scheme is expected to be one of the solutions for this purpose, a large size of memory and a large calculation cost are required to handle useful information such as motions of robots. In this paper, we introduce the multi-layered reinforcement learning method. By dividing a learning curriculum into multiple layers, the number of expected situations can be reduced. It is shown that real robots can adaptively avoid collision with each other and to obstacles in a complicated situation.