Bio-Inspired Obstacle Avoidance: from Animals to Intelligent Agents

A considerable amount of research in the field of modern robotics deals with mobile agents and their autonomous operation in unstructured, dynamic, and unpredictable environments. Designing robust controllers that map sensory input to action in order to avoid obstacles remains a challenging task. Several biological concepts are amenable to autonomous navigation and reactive obstacle avoidance. We present an overview of most noteworthy, elaborated, and interesting biologically-inspired approaches for solving the obstacle avoidance problem. We categorize these approaches into three groups: nature inspired optimization, reinforcement learning, and biorobotics. We emphasize the advantages and highlight potential drawbacks of each approach. We also identify the benefits of using biological principles in artificial intelligence in various research areas

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

Obstacle Avoidance Scheme Based Elite Opposition Bat Algorithm for Unmanned Ground Vehicles

Unmanned Ground Vehicles (UGVs) are intelligent vehicles that operate in an obstacle environment without an onboard human operator but can be controlled autonomously using an obstacle avoidance system or by a human operator from a remote location. In this research, an obstacle avoidance scheme-based elite opposition bat algorithm (EOBA) for UGVs was developed. The obstacle avoidance system comprises a simulation map, a perception system for obstacle detection, and the implementation of EOBA for generating an optimal collision-free path that led the UGV to the goal location. Three distance thresholds of 0.1 m, 0.2 m, and 0.3 m was used in the obstacle detection stage to determine the optimal distance threshold for obstacle avoidance. The performance of the obstacle avoidance scheme was compared with that of bat algorithm (BA) and particle swarm optimization (PSO) techniques. The simulation results show that the distance threshold of 0.3 m is the optimal threshold for obstacle avoidance provided that the size of the obstacle does not exceed the size of the UGV. The EOBA based scheme when compared with BA and PSO schemes obtained an average percentage reduction of 21.82% in terms of path length and 60% in terms of time taken to reach the target destination. The uniqueness of this approach is that the UGV avoid collision with an obstacle at a distance of 0.3 m from nearby obstacles as against taking three steps backward before avoiding obstacl

Simulation of identifying shortest path walkway in library by using ant colony optimization

A research is proposed based on Ant Colony Optimization for solving the shortest path problem in library.This is a research that the algorithm is aim to implement on a robot. The robot is used to walk around in the library to collect books from all the tables and put on book shelves.However, command prompt window will use to shows the result which is the shortest path.People nowadays are more concern about the efficiency of work,this may happen in library as well. Therefore,by determining the shortest path will help in reducing the time consume problem.This project is developed by starting with designing the workflow diagram as well as the design of the output interface.The work flow is the guide for the process of development.In between,Heuristic Approach is used to determine the entire possible paths at first,then Ant Colony Optimization algorithm will be implemented to search for the final and the shortest path. The system is used to be error free and the algorithm can effectively solve the shortest path problem

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

Study of Cooperative Control System for Multiple Mobile Robots Using Particle Swarm Optimization

The idea of using multiple mobile robots for tracking targets in an unknown environment can be realized with Particle Swarm Optimization proposed by Kennedy and Eberhart in 1995. The actual implementation of an efficient algorithm like Particle Swarm Optimization (PSO) is required when robots need to avoid the randomly placed obstacles in unknown environment and reach the target point. However, ordinary methods of obstacle avoidance have not proven good results in route planning. PSO is a self-adaptive population-based method in which behavior of the swarm is iteratively generated from the combination of social and cognitive behaviors and is an effective technique for collective robotic search problem. When PSO is used for exploration, this algorithm enables robots to travel on trajectories that lead to total swarm convergence on some target