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

Stag hunt game-based approach for cooperative UAVs

Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology hasfound applicationsin numerous construction project phases, ranging from site mapping, progress monitoring, building inspection, damage assessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and efficiency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant e quilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algorithm in generating feasible and efficient flight paths for UAV formation during the inspection task

Motion Planning of Autonomous Vehicles on a Dual Carriageway without Speed Lanes

The problem of motion planning of an autonomous vehicle amidst other vehicles on a straight road is considered. Traffic in a number of countries is unorganized, where the vehicles do not move within predefined speed lanes. In this paper, we formulate a mechanism wherein an autonomous vehicle may travel on the “wrong” side in order to overtake a vehicle. Challenges include assessing a possible overtaking opportunity, cooperating with other vehicles, partial driving on the “wrong” side of the road and safely going to and returning from the “wrong” side. The experimental results presented show vehicles cooperating to accomplish overtaking manoeuvres

A novel path planning approach for smart cargo ships based on anisotropic fast marching

Path planning is an essential tool for smart cargo ships that navigate in coastal waters, inland waters or other crowded waters. These ships require expert and intelligent systems to plan safe paths in order to avoid collision with both static and dynamic obstacles. This research proposes a novel path planning approach based on the anisotropic fast marching (FM) method to specifically assist with safe operations in complex marine navigation environments. A repulsive force field is specially produced to describe the safe area distribution surrounding obstacles based on the knowledge of human. In addition, a joint potential field is created to evaluate the travel cost and a gradient descent method is used to search for appropriate paths from the start point to the end point. Meanwhile, the approach can be used to constantly optimize the paths with the help of the expert knowledge in collision avoidance. Particularly, the approach is validated and evaluated through simulations. The obtained results show that it is capable of providing a reasonable and smooth path in a crowded waters. Moreover, the ability of this approach exhibits a significant contribution to the development of expert and intelligent systems in autonomous collision avoidance

QoS multicast routing protocol oriented to cognitive network using competitive coevolutionary algorithm

The human intervention in the network management and maintenance should be reduced to alleviate the ever-increasing spatial and temporal complexity. By mimicking the cognitive behaviors of human being, the cognitive network improves the scalability, self-adaptation, self-organization, and self-protection in the network. To implement the cognitive network, the cognitive behaviors for the network nodes need to be carefully designed. Quality of service (QoS) multicast is an important network problem. Therefore, it is appealing to develop an effective QoS multicast routing protocol oriented to cognitive network. In this paper, we design the cognitive behaviors summarized in the cognitive science for the network nodes. Based on the cognitive behaviors, we propose a QoS multicast routing protocol oriented to cognitive network, named as CogMRT. It is a distributed protocol where each node only maintains local information. The routing search is in a hop by hop way. Inspired by the small-world phenomenon, the cognitive behaviors help to accumulate the experiential route information. Since the QoS multicast routing is a typical combinatorial optimization problem and it is proved to be NP-Complete, we have applied the competitive coevolutionary algorithm (CCA) for the multicast tree construction. The CCA adopts novel encoding method and genetic operations which leverage the characteristics of the problem. We implement and evaluate CogMRT and other two promising alternative protocols in NS2 platform. The results show that CogMRT has remarkable advantages over the counterpart traditional protocols by exploiting the cognitive favors

A systematic literature review of multi-agent pathfinding for maze research

Multi-agent Pathfinding, also known as MAPF, is an Artificial Intelligence problem-solving. The aim is to direct each agent to find its path to reach its target, both individually and in groups. Of course, this path allows agents to move without colliding with each other. This MAPF application is implemented in many areas that require the movement of various agents, such as warehouse robots, autonomous cars, video games, traffic control, Unmanned Aerial Vehicles (UAV), Search and Rescue (SAR), many others. The use of multi-agent in exploring often assumes all areas to be explored are free of obstructions. However, the use of MAPF to achieve their goals often faces static barriers, and even other agents can also be considered dynamic barriers. Because it requires some constraints in the program, such as agents cannot collide with each other. The use of single-agent can find the shortest path through exploration. Still, multi-agent cooperation should shorten the time to find a target location, especially if there is more than one target. This paper explains the Systematic Literature Review (SLR) method to review research on various multi-agent pathfinding. The contribution of this paper is the analysis of multi-agent pathfinding and its potential application in solving maze problems based on an SLR

A novel path planning approach for smart cargo ships based on anisotropic fast marching

Path planning is an essential tool for smart cargo ships that navigate in coastal waters, inland waters or other crowded waters. These ships require expert and intelligent systems to plan safe paths in order to avoid collision with both static and dynamic obstacles. This research proposes a novel path planning approach based on the anisotropic fast marching (FM) method to specifically assist with safe operations in complex marine navigation environments. A repulsive force field is specially produced to describe the safe area distribution surrounding obstacles based on the knowledge of human. In addition, a joint potential field is created to evaluate the travel cost and a gradient descent method is used to search for appropriate paths from the start point to the end point. Meanwhile, the approach can be used to constantly optimize the paths with the help of the expert knowledge in collision avoidance. Particularly, the approach is validated and evaluated through simulations. The obtained results show that it is capable of providing a reasonable and smooth path in a crowded waters. Moreover, the ability of this approach exhibits a significant contribution to the development of expert and intelligent systems in autonomous collision avoidance