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.

With the advancement in technology, Unmanned Aerial Vehicles (UAVs) have been able to safely maneuver in risky environments. During landing, the UAV should be able to slow down while not affecting its physical design. Currently, multiple sensors are being used to increase the accuracy while landing which might weigh down some of the smaller UAVs. The usage of a single sensor to guarantee safe landing is yet on its early stages of development. It was observed that insects use Optical Flow to maneuver, and this has been used as a method to design UAV's journey and land safely. By dividing each frame, an Optical Flow Difference is calculated which is used as a metric for the UAV to move towards the landing platform.

Once the UAV has positioned itself on top of the elevated landing platform, image dilation is used to safely land the UAV. One of the methods tested used Image Dilation Method using IMU. Using the calculated image dilation, the control input to the UAV is generated and the UAV lands. This method failed in providing safe landing and it also did not take the vision of the UAV into consideration. Then, Image Dilation Method using Features from Accelerated Segment Test (IDMF-AST) was tested. This method tracks features observed by the UAV and calculated an estimate of the image dilation. The estimate of image landing is used to control the UAV. This showed dependency on the landing design platform and the hyperparameters that are used for implementing IDMF for the type of landing.

Different landing designs were tested for different elevated landing platforms. It was observed that concentric circles on a textured landing marker provided the highest probability of safe landing. To tackle the dependency of the hyperparameters, a Classification Model was proposed to find an optimal set of hyperparameters for each possible height of the platform. This trained model was incorporated with the IDMF, thereby designing the Adaptive IDMF algorithm. The Adaptive IDMF was tested against the original IDMF on the metrics of safe landing probability, time taken to safely land and simulation time. Adaptive IDMF performed better compared to IDMF providing an 190% increase in probability of safe landing, faster safe landing and lesser simulation and compilation time.