This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests.

Consider the practically relevant situation in which a robotic system is assigned a task to be executed in an environment that contains moving obstacles. Generating collision-free motions that allow the robot to execute the task while complying with its control input limitations is a challenging problem, whose solution must be sought in the robot state space extended with time. We describe a general planning framework which can be tailored to robots described by either kinematic or dynamic models. The main component is a control-based scheme for producing configuration space subtrajectories along which the task constraint is continuously satisfied. The geometric motion and time history along each subtrajectory are generated separately in order to guarantee feasibility of the latter and at the same time make the scheme intrinsically more flexible. A randomized algorithm then explores the search space by repeatedly invoking the motion generation scheme and checking the produced subtrajectories for collisions. The proposed framework is shown to provide a probabilistically complete planner both in the kinematic and the dynamic case. Modified versions of the planners based on the exploration–exploitation approach are also devised to improve search efficiency or optimize a performance criterion along the solution. We present results in various scenarios involving non-holonomic mobile robots and fixed-based manipulators to show the performance of the planner.

A non-holonomic robot with a bounded control input travels in a dynamic unknown 3D environment with moving obstacles. We propose a 3D navigation strategy to reach a given final destination point while avoiding collisions with obstacles. A formal analysis of the proposed 3D robot navigation algorithm is given. Computer simulation results and experiments with a real flying autonomous vehicle confirm the applicability and performance of the proposed guidance approach.

We present a novel framework for collision free assisted navigation of a semi-autonomous vehicle in complex unknown environments with moving and steady obstacles. In the proposed system, a semi-autonomous vehicle is guided by a human operator and an automatic reactive navigator. The autonomous reactive navigation block takes control from the human operator in situations where there is the danger of collision with obstacle. A mathematically rigorous analysis of the proposed approach is provided. The performance of the proposed assisted navigation system is demonstrated via experiments with a real semi-autonomous hospital bed and extensive computer simulations.

We present a simple biologically inspired strategy for the navigation of a unicycle-like robot towards a target while avoiding collisions with moving obstacles. A mathematically rigorous analysis of the proposed approach is provided. The performance of the algorithm is demonstrated via experiments with a real robot and extensive computer simulations.

A new real-time obstacle avoidance method for mobile robots has been developed. This method, namely the vector-distance function method, permits the detection of obstacles (both moving and stationary) and generates a path that can avoid collisions. The proposed approach expresses the distance information in a vector form. Then the notion of weighting is introduced to describe relationship between sensors of mobile robots and the target to be reached. Furthermore, R-mode, L-mode and T-mode are introduced to generate a safe path for the mobile robot in a dynamic environment filled with both stationary and moving obstacles. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. Simulation results are included to demonstrate the applicability and effectiveness of the developed algorithm.