706 research outputs found
Distributed motion misbehavior detection in teams of heterogeneous aerial robots
This paper addresses the problem of detecting possible misbehavior in a group of autonomous mobile robots, which coexist in a shared environment and interact with each other and coordinate according to a set of common interaction rules. Such rules specify what actions each robot is allowed to perform in order to interact with the other members of the group. The rules are distributed, i.e., they can be evaluated only starting from the knowledge of the individual robot and the information the robot gathers from neighboring robots. We consider misbehaving those robots which, because of either spontaneous failures or malicious tampering, do not follow the rules and whose behavior thus deviates from the nominal assigned one. The main contribution of the paper is to provide a methodology to detect such misbehavior by observing the congruence of actual behavior with the assigned rules as applied to the actual state of the system. The presented methodology is based on a consensus protocol on the events observed by robots. The methodology is fully distributed in the sense that it can be performed by individual robots based only on the local available information, it has been theoretically proven and validated with experiments involving real aerial heterogeneous robots
Circular formation control of fixed-wing UAVs with constant speeds
In this paper we propose an algorithm for stabilizing circular formations of
fixed-wing UAVs with constant speeds. The algorithm is based on the idea of
tracking circles with different radii in order to control the inter-vehicle
phases with respect to a target circumference. We prove that the desired
equilibrium is exponentially stable and thanks to the guidance vector field
that guides the vehicles, the algorithm can be extended to other closed
trajectories. One of the main advantages of this approach is that the algorithm
guarantees the confinement of the team in a specific area, even when
communications or sensing among vehicles are lost. We show the effectiveness of
the algorithm with an actual formation flight of three aircraft. The algorithm
is ready to use for the general public in the open-source Paparazzi autopilot.Comment: 6 pages, submitted to IROS 201
Flight Test Results for UAVs Using Boid Guidance Algorithms
A critical technology for operating groups of Uninhabited Aerial Vehicles (UAVs) is distributed guidance. Distributed guidance allows an operator to command several vehicles at the same time, reduces operator workload, and adds redundancy to the system. Some of the leading software candidates for achieving distributed guidance are known as Boid Guidance Algorithms (BGAs), which are agent-based techniques relying on the interactions of simple behaviors. Flight tests are crucial to the advancement of flight technologies such as BGAs, and this was identified as an important area for development. This paper presents the results from the 2005 flight tests of BGAs at NASA Dryden Flight Research Center with two RnR Products’ APV-3 UAVs employing CloudCap Technology\u27s Piccolo autopilot system. Major challenges in these flight tests include the use of a waypoint-following system, limited computation resources, and management of safety procedures. The conclusions of this work include the need for using a path-following platform and completion of a full system optimization. This work is an important step in the development of a deployable distributed guidance system
Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field
Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent
ergodic motion control guided by a gradient of a potential field. A finite
element method is hereby implemented to obtain a solution of Helmholtz partial
differential equation, which models the potential field for surveying motion
control. This allows us to survey arbitrarily shaped domains and to include
obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental
idea. For a simple kinematic motion, the obstacles and boundary avoidance
constraints are successfully handled by directing the agent motion with the
gradient of the potential. However, including additional constraints, such as
the minimal clearance dsitance from stationary and moving obstacles and the
minimal path curvature radius, requires further alternations of the control
algorithm. We introduce a relatively simple yet robust approach for handling
these constraints by formulating a straightforward optimization problem based
on collision-free escapes route maneuvers. This approach provides a guaranteed
collision avoidance mechanism, while being computationally inexpensive as a
result of the optimization problem partitioning. The proposed motion control is
evaluated in three realistic surveying scenarios simulations, showing the
effectiveness of the surveying and the robustness of the control algorithm.
Furthermore, potential maneuvering difficulties due to improperly defined
surveying scenarios are highlighted and we provide guidelines on how to
overpass them. The results are promising and indiacate real-world applicability
of proposed constrained multi-agent motion control for autonomous surveying and
potentially other HEDAC utilizations.Comment: Revised manuscrip
Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding
Multi-agent pathfinding (MAPF) is a critical field in many large-scale
robotic applications, often being the fundamental step in multi-agent systems.
The increasing complexity of MAPF in complex and crowded environments, however,
critically diminishes the effectiveness of existing solutions. In contrast to
other studies that have either presented a general overview of the recent
advancements in MAPF or extensively reviewed Deep Reinforcement Learning (DRL)
within multi-agent system settings independently, our work presented in this
review paper focuses on highlighting the integration of DRL-based approaches in
MAPF. Moreover, we aim to bridge the current gap in evaluating MAPF solutions
by addressing the lack of unified evaluation metrics and providing
comprehensive clarification on these metrics. Finally, our paper discusses the
potential of model-based DRL as a promising future direction and provides its
required foundational understanding to address current challenges in MAPF. Our
objective is to assist readers in gaining insight into the current research
direction, providing unified metrics for comparing different MAPF algorithms
and expanding their knowledge of model-based DRL to address the existing
challenges in MAPF.Comment: 36 pages, 10 figures, published in Artif Intell Rev 57, 41 (2024
Long-range collision avoidance for shared space simulation based on social forces
Shared space is an innovative approach to improve environments where both pedestrians and vehicles are present, with integrated layouts to balance priority. The Social Force Model (SFM) was used to visualise pedestrian and car trajectories so that peaks of density and pressure at critical locations are avoided. This paper extends the SFM to consider a long-range collision detection and collision resolution strategy. The determination of potential conflicts is enhanced using principle component analysis for a set of agent's prior speeds and directions. This long-range collision avoidance strategy results in more realistic SFM-based trajectories for pedestrians and cars in shared spaces
Adaptive and learning-based formation control of swarm robots
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation
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