Decentralized Collision Avoidance Research Articles

Reactive Collision Avoidance Control for Nonholonomic Vehicles and Obstacles of Arbitrary Shape

Abstract Potential field-based collision avoidance algorithms for mobile robots frequently assume vehicles and obstacles to have circular or spherical shapes. This assumption not only simplifies the analysis but also limits the mobility of agents in confined spaces, particularly for vehicles with elongated or irregular shapes. To increase mobility, this letter presents a decentralized collision avoidance framework for nonholonomic systems of unicycle type that considers the non-circular shape and relative orientation of vehicles and obstacles. The framework builds on the concepts of potential field and avoidance functions. However, it proposes using a non-constant minimum safe distance radius that changes based on the shape, relative position, and relative orientation of agents. The control framework is proven to guarantee collision avoidance at all times and is shown, via simulation, to increase the ability of agents to navigate through narrow spaces safely.

Reciprocal Safety Velocity Cones for Decentralized Collision Avoidance in Multi-Agent Systems

In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n-dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace.

Dual-Horizon Reciprocal Collision Avoidance for Aircraft and Unmanned Aerial Systems

The aircraft conflict detection and resolution problem has been addressed with a wide range of centralised methods in the past few decades, e.g. constraint programming, mathematical programming or metaheuristics. In the context of autonomous, decentralized collision avoidance without explicit coordination, geometric methods provide an elegant, cost-effective approach to avoid collisions between mobile agents, provided they all share a same logic and a same view of the traffic. The Optimal Reciprocal Collision Avoidance (ORCA) algorithm is a state-of-the art geometric method for robot collision avoidance, which can be used as a Detect & Avoid logic on-board aircraft or Unmanned Aerial Vehicles. However, ORCA does not handle well some degenerate situations where agents operate at constant or near-constant speeds, which is a widespread feature of commercial aircraft or fixed-winged Unmanned Airborne Systems. In such degenerate situations, pairs of aircraft could end up flying parallel tracks without ever crossing paths to reach their respective destination. The Constant Speed ORCA (CS-ORCA) was proposed in 2018 to better handle these situations. In this paper, we discuss the limitations of both ORCA and CS-ORCA, and introduce the Dual-Horizon ORCA (DH-ORCA) algorithm, where two time horizons are used respectively for short-term collision avoidance and medium-term path-crossing. We show that this new approach mitigates the main issues of ORCA and CS-ORCA and yields better performances with dense traffic scenarios.

A Low-Cost Q-Learning-Based Approach to Handle Continuous Space Problems for Decentralized Multi-Agent Robot Navigation in Cluttered Environments

This paper addresses the problem of navigating decentralized multi-agent systems in partially cluttered environments and proposes a new machine-learning-based approach to solve it. On the basis of this approach, a new robust and flexible Q-learning-based model is proposed to handle a continuous space problem. As in reinforcement learning (RL) algorithms, Q-learning does not require a model of the environment. Additionally, Q-Learning (QL) has the advantages of being fast and easy to design. However, one disadvantage of QL is that it needs a massive amount of memory, and it grows exponentially with each extra feature introduced to the state space. In this research, we introduce an agent-level decentralized collision avoidance low-cost model for solving a continuous space problem in partially cluttered environments, followed by introducing a method to merge non-overlapping QL features in order to reduce its size significantly by about 70% and make it possible to solve more complicated scenarios with the same memory size. Additionally, another method is proposed for minimizing the sensory data that is used by the controller. A combination of these methods is able to handle swarm navigation low memory cost with at least18 number of robots. These methods can also be adapted for deep q-learning architectures so as to increase their approximation performance and also decrease their learning time process. Experiments reveal that the proposed method also achieves a high degree of accuracy for multi-agent systems in complex scenarios.

A Multi-Agent Deep Reinforcement Learning Approach for Practical Decentralized UAV Collision Avoidance

This letter proposes an improved deep reinforcement learning controller for the decentralized collision avoidance problem. Using a unique architecture incorporating ‘long-short term memory cells’ and a reward function inspired from gradient-based approaches, the controller outperforms existing techniques in environments with variable numbers of agents. The design of the reward function is also evaluated to critique existing deep reinforcement learning approaches and used to establish a better result. The proposed controller is subsequently tested in simulation and a real-world 3-dimensional drone environment: outperforming the predominant classical approach in the literature by a significant margin.

Reinforcement learning-based dynamic obstacle avoidance and integration of path planning.

Deep reinforcement learning has the advantage of being able to encode fairly complex behaviors by collecting and learning empirical information. In the current study, we have proposed a framework for reinforcement learning in decentralized collision avoidance where each agent independently makes its decision without communication with others. In an environment exposed to various kinds of dynamic obstacles with irregular movements, mobile robot agents could learn how to avoid obstacles and reach a target point efficiently. Moreover, a path planner was integrated with the reinforcement learning-based obstacle avoidance to solve the problem of not finding a path in a specific situation, thereby imposing path efficiency. The robots were trained about the policy of obstacle avoidance in environments where dynamic characteristics were considered with soft actor critic algorithm. The trained policy was implemented in the robot operating system (ROS), tested in virtual and real environments for the differential drive wheel robot to prove the effectiveness of the proposed method. Videos are available at https://youtu.be/xxzoh1XbAl0.

Hamiltonian coordination primitives for decentralized multiagent navigation

We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. Although this prediction problem can be shown to be NP-hard, humans are often capable of solving it efficiently by leveraging sophisticated mechanisms of implicit coordination. Inspired by the human paradigm, we propose a novel topological formalism that explicitly models multiagent coordination. Our formalism features both geometric and algebraic descriptions enabling the use of standard gradient-based optimization techniques for trajectory generation but also symbolic inference over coordination strategies. In this article, we contribute (a) HCP (Hamiltonian Coordination Primitives), a novel multiagent trajectory-generation pipeline that accommodates spatiotemporal constraints formulated as symbolic topological specifications corresponding to a desired coordination strategy; (b) HCPnav, an online planning framework for decentralized collision avoidance that generates motion by following multiagent trajectory primitives corresponding to high-likelihood, low-cost coordination strategies. Through a series of challenging trajectory-generation experiments, we show that HCP outperforms a trajectory-optimization baseline in generating trajectories of desired topological specifications in terms of success rate and computational efficiency. Finally, through a variety of navigation experiments, we illustrate the efficacy of HCPnav in handling challenging multiagent navigation scenarios under homogeneous or heterogeneous agents across a series of environments of different geometry.

SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms Under Uncertainty

We present decentralized collision avoidance algorithms for quadrotor swarms operating under uncertain state estimation. Our approach exploits the differential flatness property and feedforward linearization to approximate the quadrotor dynamics and performs reciprocal collision avoidance. We account for the uncertainty in position and velocity by formulating the collision constraints as chance constraints, which describe a set of velocities that avoid collisions with a specified confidence level. We present two different methods for formulating and solving the chance constraints: our first method assumes a Gaussian noise distribution. Our second method is its extension to the non-Gaussian case by using a Gaussian Mixture Model (GMM). We reformulate the linear chance constraints into equivalent deterministic constraints, which are used with an MPC framework to compute a local collision-free trajectory for each quadrotor. We evaluate the proposed algorithm in simulations on benchmark scenarios and highlight its benefits over prior methods. We observe that both the Gaussian and non-Gaussian methods provide improved collision avoidance performance over the deterministic method. On average, the Gaussian method requires ~ 5 ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 9 ms on average in scenarios with 4 agents.

The impact of catastrophic collisions and collision avoidance on a swarming behavior

Swarms of autonomous agents are useful in many applications due to their ability to accomplish tasks in a decentralized manner, making them more robust to failures. Due to the difficulty in running experiments with large numbers of hardware agents, researchers often make simplifying assumptions and remove constraints that might be present in a real swarm deployment. While simplifying away some constraints is tolerable, we feel that two in particular have been overlooked: one, that agents in a swarm take up physical space, and two, that agents might be damaged in collisions. Many existing works assume agents have negligible size or pass through each other with no added penalty. It seems possible to ignore these constraints using collision avoidance, but we show using an illustrative example that this is easier said than done. In particular, we show that collision avoidance can interfere with the intended swarming behavior and significant parameter tuning is necessary to ensure the behavior emerges as best as possible while collisions are avoided. We compare four different collision avoidance algorithms, two of which we consider to be the best decentralized collision avoidance algorithms available. Despite putting significant effort into tuning each algorithm to perform at its best, we believe our results show that further research is necessary to develop swarming behaviors that can achieve their goal while avoiding collisions with agents of non-negligible volume.

Directional optimal reciprocal collision avoidance

A great amount of effort has been devoted to the study on self-separation assurance approach for civil aviation in the airspace with increasing density. In this article, the Optimal Reciprocal Collision Avoidance (ORCA) algorithm is modified to make it work for autonomous and decentralized collision avoidance for civil aircraft. Without considering the direction selectivity of collision-free maneuver, aircraft may select the relative parallel trajectories by deploying the ORCA algorithm in both decentralized and centralized way. As a result, the collision tends to be postponed to the next time horizon because civil aircraft need to return to original trajectories. Simultaneously, the unified rules can hardly be integrated into the approach due to the lack of direction selectivity for collision-free navigation. The process of separation assurance will be disorderly when multiple aircraft are involved. To solve the problem mentioned above, a new algorithm called Directional Optimal Reciprocal Collision Avoidance (DORCA) is proposed. The DORCA algorithm employs a vector rotation mode to construct the forbidden Velocity Obstacle (VO) set in order to improve the computation efficiency. In addition, the direction selectivity of maneuver is achieved through constructing the direction-constrained VO set according to the direction of relative motion in velocity space. Direction selectivity of the algorithm enables the process of collision avoidance to comply with the unified rules. A number of encounter scenarios are conducted to confirm the validity and feasibility of the proposed DORCA algorithm. In all scenarios tested, the direction selectivity of collision-free maneuver can be successfully integrated into the DORCA algorithm, and the algorithm is more efficient than the ORCA algorithm for collision avoidance in decentralized way.

DCAD: Decentralized Collision Avoidance With Dynamics Constraints for Agile Quadrotor Swarms

We present DCAD, a novel, decentralized collision avoidance algorithm for navigating a swarm of quadrotors in dense environments populated with static and dynamic obstacles. Our algorithm relies on the concept of Optimal Reciprocal Collision Avoidance (ORCA) and utilizes a flatness-based Model Predictive Control (MPC) to generate local collision-free trajectories for each quadrotor. We feedforward linearize the non-linear dynamics of the quadrotor and subsequently use this linearized model in our MPC framework. Our approach tends to compute safe trajectories that avoid quadrotors from entering each other's downwash regions during close proximity maneuvers. In addition, we account for the uncertainty in the position and velocity sensor data using Kalman filter. We evaluate the performance of our algorithm with other state-of-the-art decentralized methods and demonstrate its superior performance in terms of smoothness of generated trajectories and lower probability of collision during high-velocity maneuvers.

Distributed Cooperative Avoidance Control for Multi-Unmanned Aerial Vehicles

It is well-known that collision-free control is a crucial issue in the path planning of unmanned aerial vehicles (UAVs). In this paper, we explore the collision avoidance scheme in a multi-UAV system. The research is based on the concept of multi-UAV cooperation combined with information fusion. Utilizing the fused information, the velocity obstacle method is adopted to design a decentralized collision avoidance algorithm. Four case studies are presented for the demonstration of the effectiveness of the proposed method. The first two case studies are to verify if UAVs can avoid a static circular or polygonal shape obstacle. The third case is to verify if a UAV can handle a temporary communication failure. The fourth case is to verify if UAVs can avoid other moving UAVs and static obstacles. Finally, hardware-in-the-loop test is given to further illustrate the effectiveness of the proposed method.

Almost global finite-time stabilization of spacecraft formation flying with decentralized collision avoidance

This paper presents finite-time stabilization of spacecraft formation flying with a decentralized collision avoidance in the framework of geometric mechanics. A finite-time control scheme is developed such that each spacecraft achieves desired relative configuration and velocities with respect to the virtual leader autonomously. The finite-time control scheme is combined with a decentralized collision avoidance scheme. A Lyapunov analysis guarantees that the spacecraft motions converge to the desired state trajectories with the combined control scheme in finite time. Numerical simulation results verify the successful application of the proposed combined control scheme for spacecraft formation flying in the presence of unknown external disturbances while avoiding collisions via the decentralized collision avoidance.

Human-Comfortable Collision-Free Navigation for Personal Aerial Vehicles

Semi- or fully autonomous personal aerial vehicles (PAVs) are currently studied and developed by public and private organizations as a solution for traffic congestion. While optimal collision-free navigation algorithms have been proposed for autonomous robots, trajectories and accelerations for PAVs should also take into account human comfort. In this letter, we propose a reactive decentralized collision avoidance strategy that incorporates passenger physiological comfort based on the optimal reciprocal collision avoidance strategy. We study in simulation the effects of increasing PAV densities on the level of comfort, on the relative flight time and on the number of collisions per flight hour and demonstrate that our strategy reduces collision risk for platforms with limited dynamic range. Finally, we validate our strategy with a swarm of ten quadcopters flying outdoors.

Asymptotic Tracking Control for Spacecraft Formation Flying with Decentralized Collision Avoidance

This paper presents a tracking control scheme for spacecraft formation flying with a decentralized collision-avoidance scheme, using a virtual leader state trajectory. The configuration space for a spacecraft is the Lie group , which is the set of positions and orientations in three-dimensional Euclidean space. A virtual leader trajectory, in the form of attitude and orbital motion of a virtual satellite, is generated offline. Each spacecraft tracks a desired relative configuration with respect to the virtual leader in an autonomous manner, to achieve the desired formation. The relative configuration between a spacecraft and the virtual leader is described in terms of exponential coordinates on . A continuous-time feedback tracking control scheme is designed using these exponential coordinates and the relative velocities. A Lyapunov analysis guarantees that the spacecraft asymptotically converge to their desired state trajectories. This tracking control scheme is combined with a decentralized collision-avoidance control scheme generated from artificial potentials for each spacecraft, which includes information of relative positions of other spacecraft within communications range. Asymptotic convergence to the desired trajectory with this combined control law is demonstrated using a Lyapunov analysis. Numerical simulation results verify the successful application of this tracking control scheme to a formation maneuver with decentralized collision avoidance.

Applications of hybrid reachability analysis to robotic aerial vehicles

The control of complex non-linear systems can be aided by modeling each system as a collection of simplified hybrid modes, with each mode representing a particular operating regime defined by the system dynamics or by a region of the state space in which the system operates. Guarantees on the safety and performance of such hybrid systems can still be challenging to generate, however. Reachability analysis using a dynamic game formulation with Hamilton—Jacobi methods provides a useful way to generate these types of guarantees, and the technique is flexible enough to analyze a wide variety of systems. This paper presents two applications of reachable sets, both focused on guaranteeing the safety and performance of robotic aerial vehicles. In the first example, reachable sets are used to design and implement a backflip maneuver for a quadrotor helicopter. In the second, reachability analysis is used to design a decentralized collision avoidance algorithm for multiple quadrotors. The theory for both examples is explained, and successful experimental results are presented from flight tests on the STARMAC quadrotor helicopter platform.