Detect and Avoid for Autonomous Agents in Cluttered Environments

Abstract

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 environments. Cluttered and unknown environments such as forests and subaquatic environments need to have autonomous navigation systems developed just for them due to their uncertain and changing nature. Path planning algorithms are used for the navigation of an autonomous agent in an environment. The agent needs to reach a target location while avoiding the obstacles it detects along the path. Such a system is called a Detect and Avoid (DAA) system and there are different implementations for it of which some are explored in this thesis. The Artificial Potential Fields method or APF for short is a method for mobile agent navigation which is based on generating an attractive force on the agent from the target and a repulsive force from the obstacles. This leads to the agent reaching the target while avoiding the obstacles along the way. The Classical APF (CAPF) method works for structured environments well but not for cluttered environments. The CAPF method can be replaced with a modified version where the agent is surrounded by a set of points (called bacteria points) around its current location and the agent moves by selecting a bacteria point as a future location. This method is named the Bacteria APF (BAPF) method. This selection happens through combinatorial optimization based on the potential value of each bacteria point. In this thesis, we propose two distinct contributions to the BAPF method. The first one being the use of an adaptive parameter in the repulsive cost function which is determined through a brute-force search. The second addition is a branching cost function that changes the value of the repulsive potential based on predefined perimeters around each obstacle. We show through simulations on densely and lightly cluttered environments that this Improved BAPF (IBAPF) method significantly improves the performance of the system in terms of the convergence to the target by almost 200% and reduced the time it takes to converge by around 25% as well as maintain the safety of the navigation route by keeping the average distance from obstacles around the same value.