Maze-like Environment Research Articles

Quantifying Swarm Resilience with Simulated Exploration of Maze-Like Environments

Quantifying Swarm Resilience with Simulated Exploration of Maze-Like Environments

Quantifying Swarm Resilience with Simulated Exploration of Maze-Like Environments

Quantifying Swarm Resilience with Simulated Exploration of Maze-Like Environments

DLSC: Distributed Multi-Agent Trajectory Planning in Maze-Like Dynamic Environments Using Linear Safe Corridor

This article presents an online distributed trajectory planning algorithm for a quadrotor swarm in a maze-like dynamic environment. We utilize a dynamic linear safe corridor to construct the feasible collision constraints that can ensure interagent collision avoidance and consider the uncertainty of moving obstacles. We introduce mode-based subgoal planning to resolve deadlock faster in a complex environment using only previously shared information. For dynamic obstacle avoidance, we adopt heuristic methods such as collision alert propagation and escape point planning to deal with the situation where dynamic obstacles approach the agents clustered in a narrow corridor. We prove that the proposed algorithm guarantees the feasibility of the optimization problem for every replanning step. In an obstacle-free space, the proposed method can compute the trajectories for 60 agents on average 7.66 ms per agent with an Intel i7 laptop and shows the perfect success rate. Also, our method shows 64.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> shorter flight time than buffered Voronoi cell and 34.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> shorter than with our previous work. We conduct the simulation in a random forest and maze with four dynamic obstacles, and the proposed algorithm shows the highest success rate and shortest flight time compared to state-of-the-art baseline algorithms. In particular, the proposed algorithm shows over 97 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> success rate when the velocity of moving obstacles is below the agent's maximum speed. We validate the safety and robustness of the proposed algorithm through a hardware demonstration with ten quadrotors and two pedestrians in a maze-like environment.

Threat impairs flexible use of a cognitive map.

Goal-directed behavior requires adaptive systems that respond to environmental demands. In the absence of threat (or presence of reward), individuals can explore many behavioral trajectories, effectively interrogating the environment across multiple dimensions. This leads to flexible, relational memory encoding and retrieval. In the presence of danger, motivation shifts to an imperative state characterized by a narrow focus of attention on threatening information. This impairs flexible, relational memory. We test how these motivational shifts (Murty & Adcock, 2017) affect behavioral flexibility in an ecologically valid setting. Participants learned the structure of maze-like environments and navigated to the location of objects in both safe and threatening contexts. The latter contained a predator that could 'capture' participants, leading to electric shock. After learning, the path to some objects was unpredictably blocked. forcing a detour for which one route was significantly shorter. We predicted that threat would push participants toward an imperative state, leading to less efficient and less flexible navigation. Threat caused participants to take longer paths to goal objects and less efficient detours when obstacles were encountered. Threat-related impairments in detour navigation persisted after controlling for non-detour navigation performance. and non-detour navigation was not a reliable predictor of detour navigation, This suggests a specific impairment in flexible navigation during detours, an impairment unlikely to be explained by more general processes like predator avoidance or divided attention that may be present during non-detour navigation. These results provide ecologically valid evidence that dynamic, observable threats reduce flexible use of cognitive maps to guide behavior.

RLSS: real-time, decentralized, cooperative, networkless multi-robot trajectory planning using linear spatial separations

Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots’ dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm’s performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments.

A Hybrid Human-in-the-Loop Deep Reinforcement Learning Method for UAV Motion Planning for Long Trajectories with Unpredictable Obstacles

Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things (IoT) ecosystem due to their ability to collect and transmit data from remote and hard-to-reach areas. Ensuring collision-free navigation for these UAVs is crucial in achieving this goal. However, existing UAV collision-avoidance methods face two challenges: conventional path-planning methods are energy-intensive and computationally demanding, while deep reinforcement learning (DRL)-based motion-planning methods are prone to make UAVs trapped in complex environments—especially for long trajectories with unpredictable obstacles—due to UAVs’ limited sensing ability. To address these challenges, we propose a hybrid collision-avoidance method for the real-time navigation of UAVs in complex environments with unpredictable obstacles. We firstly develop a Human-in-the-Loop DRL (HL-DRL) training module for mapless obstacle avoidance and secondly establish a global-planning module that generates a few points as waypoint guidance. Moreover, a novel goal-updating algorithm is proposed to integrate the HL-DRL training module with the global-planning module by adaptively determining the to-be-reached waypoint. The proposed method is evaluated in different simulated environments. Results demonstrate that our approach can rapidly adapt to changes in environments with short replanning time and prevent the UAV from getting stuck in maze-like environments.

Ethical Consideration in AI &amp; Machine Learning

This innovative investigation on the ethics of AI and machine learning sets out on a multifaceted voyage through a maze-like environment. It seeks to offer a new viewpoint on negotiating the changing issues posed by AI and machine learning by exposing the interwoven layers of ethical considerations. This work encourages a progressive mindset in the face of a constantly evolving technology environment by supporting flexibility, inclusion, and ethical awareness. The paper starts off by emphasizing the moral complexity of artificial intelligence (AI), which includes issues with bias and justice as well as problems with responsibility, autonomous decision-making, and unanticipated outcomes of intelligent systems. It illustrates the various ways in which different ethical factors interact and weave together to create new levels of ethical complexity. Beyond basic algorithms and automated procedures, the rapidly developing fields of artificial intelligence (AI) and machine learning pose a dynamic and complex ethical problem. This research article takes a singular turn as it explores the complex ethical maze that artificial intelligence and machine learning have placed before us. It seeks to offer a new viewpoint on negotiating the changing issues posed by AI and machine learning by exposing the interwoven layers of ethical considerations. This work encourages a progressive mindset in the face of a constantly evolving technology environment by supporting flexibility, inclusion, and ethical awareness. The article highlights the mutually beneficial relationship that exists between those who develop technology and society in the context of AI development and application. It explores the moral responsibilities of AI designers as well as users, showing how their choices and interactions can influence the moral environment.

Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms

With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.

A Novel Collision-Free Homotopy Path Planning for Planar Robotic Arms.

Achieving the smart motion of any autonomous or semi-autonomous robot requires an efficient algorithm to determine a feasible collision-free path. In this paper, a novel collision-free path homotopy-based path-planning algorithm applied to planar robotic arms is presented. The algorithm utilizes homotopy continuation methods (HCMs) to solve the non-linear algebraic equations system (NAES) that models the robot’s workspace. The method was validated with three case studies with robotic arms in different configurations. For the first case, a robot arm with three links must enter a narrow corridor with two obstacles. For the second case, a six-link robot arm with a gripper is required to take an object inside a narrow corridor with two obstacles. For the third case, a twenty-link arm must take an object inside a maze-like environment. These case studies validated, by simulation, the versatility and capacity of the proposed path-planning algorithm. The results show that the CPU time is dozens of milliseconds with a memory consumption less than 4.5 kB for the first two cases. For the third case, the CPU time is around 2.7 s and the memory consumption around 18 kB. Finally, the method’s performance was further validated using the industrial robot arm CRS CataLyst-5 by Thermo Electron.

Online Distributed Trajectory Planning for Quadrotor Swarm With Feasibility Guarantee Using Linear Safe Corridor

This letter presents a new online multi-agent trajectory planning algorithm that guarantees to generate safe, dynamically feasible trajectories in a cluttered environment. The proposed algorithm utilizes a linear safe corridor (LSC) to formulate the distributed trajectory optimization problem with only feasible constraints, so it does not resort to slack variables or soft constraints to avoid optimization failure. We adopt a priority-based goal planning method to prevent the deadlock without an additional procedure to decide which robot to yield. The proposed algorithm can compute the trajectories for 60 agents on average 15.5 ms per agent with an Intel i7 laptop and shows a similar flight distance and distance compared to the baselines based on soft constraints. We verified that the proposed method can reach the goal without deadlock in both the random forest and the indoor space, and we validated the safety and operability of the proposed algorithm through a real flight test with ten quadrotors in a maze-like environment.

Cooperative Pathfinding

Cooperative Pathfinding is a multi-agent path planning problem where agents must find non-colliding routes to separate destinations, given full information about the routes of other agents. This paper presents three new algorithms for efficiently solving this problem, suitable for use in Real-Time Strategy games and other real-time environments. The algorithms are decoupled approaches that break down the problem into a series of single-agent searches. Cooperative A* (CA*) searches space-time for a non-colliding route. Hierarchical Cooperative A* (HCA*) uses an abstract heuristic to boost performance. Finally, Windowed Hierarchical Cooperative A* (WHCA*) limits the space-time search depth to a dynamic window, spreading computation over the duration of the route. The algorithms are applied to a series of challenging, maze-like environments, and compared to A* with Local Repair (the current video-games industry standard). The results show that the new algorithms, especially WHCA*, are robust and efficient solutions to the Cooperative Pathfinding problem, finding more successful routes and following better paths than Local Repair A*

Swarm intelligence based robotic search in unknown maze-like environments

This paper proposes a novel decentralize and asynchronous robotic search algorithm based on particle swarm optimization (PSO), which has focused on solving mazes and finding targets in unknown environments with minimal inter-swarm communication and without any synchronization or communication center. In the proposed method, robots are advanced particles of the PSO algorithm, enriched with a toolkit, including an angle of rotation to change the course when confronted with obstacles to avoid them (AoR tool), and a memory to remember and reuse their best personal experiences to turn back from dead-ends (Mem tool). This toolkit enables the swarm to avoid obstacles and solve mazes while moving toward the target. The performance of the proposed algorithm is tested in a specially designed framework. As a validation, the proposed algorithm is compared with some recently published methods, including Adaptive Robotic PSO (A-RPSO), Robotic Bat Algorithm (RBA), and Adaptive Robotic Bat Algorithm (ARBA), in simple search environments that they can solve. The results of this comparison show that the introduced search method has the highest success rate (100%) in environments of different sizes and reflects the nature of swarm intelligence better. The proposed method is also tested in various maze-like search environments. The results depict the algorithm’s high efficiency to solve mazes in varying complexity levels and locate the target in a reliable time. It is also shown that the performance of the proposed algorithm does not decrease and remains constant as the complexity of search environments increases.

Autonomous Navigation of Magnetic Microrobots in a Large Workspace Using Mobile-Coil System

Untethered magnetic miniature robots have attracted much attention in recent years, due to their minimally invasive features in biomedicine and highly flexible traits in micromanipulation. Autonomous control is one of the essential issues for magnetic microrobots, however, which remains challenging, especially for complex and large-workspace environments. To this end, this article proposes a novel navigation framework, using a home-designed eye-in-hand mobile-coil system. A three-step motion planner is designed to know entire surroundings and generate optimal paths, which contains environment reconstruction, position registration, and modified A* path searching. Moreover, a double-loop motion controller is designed to make the microrobot accurately follow the computed path and intelligently avoid collisions: The inner loop performs mechanism following; the outer loop decides appropriate magnetic fields for actuation, which includes a disturbance observer (DOB), a fuzzy logic modifier (FLM), and a model predictive controller (MPC). Experiments demonstrate the effectiveness of the proposed strategy: Feasible trajectories in a maze-like environment of diameter 230 mm can be planned, and autonomous navigation of microrobot is realized with both global accessibility and local adaptability.

End-to-End Autonomous Exploration with Deep Reinforcement Learning and Intrinsic Motivation.

Developing artificial intelligence (AI) agents is challenging for efficient exploration in visually rich and complex environments. In this study, we formulate the exploration question as a reinforcement learning problem and rely on intrinsic motivation to guide exploration behavior. Such intrinsic motivation is driven by curiosity and is calculated based on episode memory. To distribute the intrinsic motivation, we use a count-based method and temporal distance to generate it synchronously. We tested our approach in 3D maze-like environments and validated its performance in exploration tasks through extensive experiments. The experimental results show that our agent can learn exploration ability from raw sensory input and accomplish autonomous exploration across different mazes. In addition, the learned policy is not biased by stochastic objects. We also analyze the effects of different training methods and driving forces on exploration policy.

Hybrid control for tracking environmental level sets by nonholonomic robots in maze-like environments

A non-holonomic constant-speed robot travels in an unknown maze-like environment cluttered with complex obstacles. Through the obstacle-free part of the plane, the robot should autonomously arrive at the isoline where an unknown scalar field assumes a given value. Afterwards, it should track the obstacle-free part of the isoline. The robot has access only to the field value at the current location and the distance from this location to the obstacles. We present a hybrid nonlinear navigation law that solves this mission. The law does not use estimation of the field gradient and is non-demanding with respect to both computation and motion. The non-local convergence of the proposed algorithm is rigorously justified and confirmed by computer simulation tests.

RUN: rational ubiquitous navigation, a model for automated navigation and searching in virtual environments

By now, the realm of virtual reality is abuzz with high-quality visuals, enough to simulate a real-world scene. The use of intelligence in virtual reality systems, however, is a milestone yet to be achieved to make possible seamless realism in a virtual environment. This paper presents a model, rational ubiquitous navigation to improve believability of a virtual environment. The model intends to augment maturity of a virtual agent by inculcating in it the human-like learning capability. A novel approach for automated navigation and searching is proposed by incorporating machine learning in virtual reality. An intelligent virtual agent learns objects of interest along with the paths followed for navigation. A mental map is molded dynamically as a user navigates in the environment. The map is followed by the agent during self-directed navigation to access any known object. After reaching at a location where an object of interest resides, the required object is selected on the basis of front-facet feature. The model is implemented in a case-study project learn objects on path (LOOP). Twelve users evaluated the model in the immersive maze-like environment of LOOP. Results of the evaluation assure applicability of the model in various cross-modality applications.

Deep Reinforcement Learning for Instruction Following Visual Navigation in 3D Maze-Like Environments

In this work, we address the problem of visual navigation by following instructions. In this task, the robot must interpret a natural language instruction in order to follow a predefined path in a possibly unknown environment. Despite different approaches have been proposed in the last years, they are all based on the assumption that the environment contains objects or other elements that can be used to formulate instructions, such as houses or offices. On the contrary, we focus on situations where the environment objects cannot be used to specify a navigation path. In particular, we consider 3D maze-like environments as our test bench because they can be very large and offer very intricate structures. We show that without reference points, visual navigation and instruction following can be rather challenging, and that standard approaches can not be applied successfully. For this reason, we propose a new architecture that explicitly learns both visual navigation and instruction understanding. We demonstrate with simulated experiments that our method can effectively follow instructions and navigate in previously unseen mazes of various sizes.

Multi-behaviors coordination controller design with enzymatic numerical P systems for robots

Membrane computing models are parallel and distributed natural computing models. These models are often referred to as P systems. This paper proposes a novel multi-behaviors co-ordination controller model using enzymatic numerical P systems for autonomous mobile robots navigation in unknown environments. An environment classifier is constructed to identify different environment patterns in the maze-like environment and the multi-behavior co-ordination controller is constructed to coordinate the behaviors of the robots in different environments. Eleven sensory prototypes of local environments are presented to design the environment classifier, which needs to memorize only rough information, for solving the problems of poor obstacle clearance and sensor noise. A switching control strategy and multi-behaviors coordinator are developed without detailed environmental knowledge and heavy computation burden, for avoiding the local minimum traps or oscillation problems and adapt to the unknown environments. Also, a serial behaviors control law is constructed on the basis of Lyapunov stability theory aiming at the specialized environment, for realizing stable navigation and avoiding actuator saturation. Moreover, both environment classifier and multi-behavior coordination controller are amenable to the addition of new environment models or new behaviors due to the modularity of the hierarchical architecture of P systems. The simulation of wheeled mobile robots shows the effectiveness of this approach.

Integrated robot planning, path following, and obstacle avoidance in two and three dimensions: Wheeled robots, underwater vehicles, and multicopters

We propose an innovative, integrated solution to path planning, path following, and obstacle avoidance that is suitable both for 2D and 3D navigation. The proposed method takes as input a generic curve connecting a start and a goal position, and is able to find a corresponding path from start to goal in a maze-like environment even in the absence of global information, it guarantees convergence to the path with kinematic control, and finally avoids locally sensed obstacles without becoming trapped in deadlocks. This is achieved by computing a closed-form expression in which the control variables are a continuous function of the input curve, the robot’s state, and the distance of all the locally sensed obstacles. Specifically, we introduce a novel formalism for describing the path in two and three dimensions, as well as a computationally efficient method for path deformation (based only on local sensor readings) that is able to find a path to the goal even when such path cannot be produced through continuous deformations of the original. The article provides formal proofs of all the properties above, as well as simulated results in a simulated environment with a wheeled robot, an underwater vehicle, and a multicopter.

A novel highly-extensible 2-DOF pneumatic actuator for soft robotic applications

A novel highly-extensible pneumatic actuator and the underlying drive principle is presented in this paper. First, a simplified physical model is proposed for the drive principle. Second, mechanical design and control system of the prototype are explained. Third, generated force and speed are investigated, and validity of the model is shown through experiments, both for one and two degree of freedom systems. Finally, a potential application of the actuator is demonstrated in the form of a soft robot that can achieve navigation in maze-like environments. Experimental results indicate that open-loop speed control of the actuator can be achieved with speeds higher than the ones found in literature. The extension ratio of the proposed actuator is only limited by length of the tubing used, and can be increased depending on the application field.