Clustered Multi-Target Search in Unknown Large Environments Using Modified Bee Swarm Optimization

Swarm robotics (SR) is an emerging field of research that utilizes a swarm of robots that work together as a team to solve complex problems. One such challenge in SR is the exploration of unknown environments to find multiple targets. This problem finds applications in various domains, such as archaeology, underground exploration, signal source...

Model-based Integrated Planning and Control of Autonomous Vehicles using Artificial Potential Fields

Navigation systems in an Autonomous Vehicles (AV) can be divided into two parts: a path planning block which takes in the environmental data and rules to design a collision-free obstacle and a vehicle control and tracking block which generates actuator inputs for the AV to follow the reference path generated by the path planning block. Each task...

Detect and Avoid for Autonomous Agents in Cluttered Environments

Autonomous agents are the future of many services and industries such as delivery systems, surveillance and monitoring, and search and rescue missions. An important aspect in an autonomous agent is the navigation system it uses to traverse the environment. Not much emphasis has been paid in the past on autonomous agent navigation in cluttered...

Integrating swarm intelligence with bayesian networks for continuous UAV-based surveillance in dynamic environments

The loss of wildlife due to illegal poaching activity poses threats on both the survival of iconic animal species and the livelihood of local communities. This research proposes a distributed surveillance model in which a UAV swarm autonomously coordinates continuous surveillance in a dynamic environment. The adaptive behaviour of poachers has...

A Cooperative Protocol for Vehicle Merging Using Bi-dimensional Artificial Potential Fields

In recent years, platooning solutions like cooperative adaptive cruise control (CACC) have been deeply studied. It is common in such platooning literature to assume that the vehicles drive on the same lane (longitudinal platooning). At the same time, lateral control during merging maneuvers is commonly addressed as a path planning problem, in...

Path planning for autonomous ships: A hybrid approach based on improved apf and modified vo methods

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified...

Artificial Potential Fields for Safe Reinforcement Learning: in Flight Control Systems

Reinforcement Learning (RL) methods have become a topic of interest for performing guidance and navigation tasks, due to potential adaptability and autonomy improvements within dynamic systems. Nevertheless, a core component of RL is an agent exploring the environment it finds itself in, resulting in an intrinsic violation of the agent's safety....

Model-based Path Planning and Control for Autonomous Vehicles using Artificial Potential Fields

This report presents the results of the graduation thesis from the TU Delft, performed at the Integrated Vehicle Safety department of TNO. The goal of this graduation project is to develop a unified path planning and tracking method for autonomous vehicles in highway driving scenarios, by making use of Artificial Potential Fields (APFs).<br/><br...

Potential Field Methods for Safe Reinforcement Learning: Exploring Q-Learning and Potential Fields

A Reinforcement Learning (RL) agent learns about its environment through exploration. For most physical applications such as search and rescue UAVs, this exploration must take place with safety in mind. Unregulated exploration, especially at the beginning of a run, will lead to fatal situations such as crashes. One approach to mitigating these...