Navigating complex environments is a challenging task for robots, as they need to perceive, understand, and react to dynamic and uncertain situations. How can robots be programmed to achieve this goal? In this article, you will learn about some of the techniques and tools that computer engineers use to design and implement robot vision and perception systems.
The first step to program a robot to navigate complex environments is to equip it with sensors that can capture different types of data, such as images, depth, sound, motion, and location. Depending on the task and the environment, the robot may use cameras, lidars, radars, microphones, gyroscopes, GPS, or other sensors. The data collected by the sensors need to be processed and filtered to reduce noise and errors, and to extract relevant features and information.
Sensors: Lidar and Radar: Light Detection and Ranging (Lidar) and Radar sensors are commonly used to measure distances and create detailed maps of the surroundings. Cameras: Cameras provide visual information, allowing robots to recognize objects, obstacles, and landmarks. Inertial Measurement Units (IMU): IMUs measure accelerations and angular rates, helping robots maintain their orientation and estimate their position.
The second step is to use the sensor data to create a map of the environment and to determine the robot's position and orientation within it. This is known as simultaneous localization and mapping (SLAM), and it is a key problem in robot navigation. SLAM algorithms use techniques such as odometry, landmark detection, loop closure, and graph optimization to estimate the robot's pose and the map's structure. The map can be represented in different ways, such as occupancy grids, point clouds, or semantic labels.
Accurate mapping and localization are crucial for a robot to comprehend its position within an intricate environment. In a project involving an autonomous drone, I observed simultaneous localization and mapping (SLAM) algorithms. By combining data from sensors on board, the drone generated a comprehensive map of its surroundings and consistently updated its position within that map. This not only facilitated accurate navigation but also allowed the drone to adjust to variations in the environment, such as moving obstacles or modified terrain
Mapping: Robots create maps of their environment using sensor data. Simultaneous Localization and Mapping (SLAM) algorithms are often employed to build and update maps while the robot is in motion. Path Planning: Path planning algorithms determine the best route for the robot to reach its destination while avoiding obstacles. A* (A-star), Dijkstra's algorithm, and Rapidly-exploring Random Trees (RRT) are common path planning techniques. Localization: Localization algorithms estimate the robot's position within the environment. Particle filters and Extended Kalman Filters (EKF) are examples of techniques used for localization.
The third step is to use the map and the localization to plan a path and a trajectory for the robot to reach its destination or perform its task. This involves finding a feasible and optimal route that avoids obstacles and minimizes costs, such as time, energy, or distance. Planning algorithms use techniques such as graph search, sampling-based methods, or artificial intelligence to generate and evaluate possible paths. The robot then needs to follow the planned path by controlling its actuators, such as wheels, motors, or joints. Control algorithms use techniques such as feedback loops, PID controllers, or model predictive control to adjust the robot's speed, direction, and orientation.
Robots "behave" and it would be useful to have a well-defined behavior repertoire (systematic codes for behaviors) data base which includes not only the external behaviors but also their internal "behaviors". These internal behaviors could be "robot intentions", "situation interpretations", "problem situations", "plans", etc etc which emerge during robot behavior. In natural environments, especially collaborating with humans this state related and predictive knowledge is valuable to have available for other actors. Then it is a good question can we formulate such a behavior code set and what are the behavioral entities coded. I've named this Internet of Behaviors (IoB) for humans but I assume it can be applied to robots as well.
Obstacle Avoidance: Obstacle avoidance algorithms use sensor data to detect and navigate around obstacles in real-time. This can involve simple reactive methods or more complex decision-making approaches. Control Systems: Control systems manage the robot's motors and actuators to execute the planned actions. Proportional-Integral-Derivative (PID) controllers are often used for motor control. Machine Learning: Machine learning techniques, such as reinforcement learning, can be employed to enhance navigation capabilities by allowing robots to learn from experience and adapt to changing environments. Human-Robot Interaction: Robots can be programmed to interact with humans in shared spaces, considering social norms and safety protocols.
The fourth step is to enable the robot to learn from its experience and to adapt to changing conditions. This can improve the robot's performance, robustness, and flexibility in complex environments. Learning and adaptation algorithms use techniques such as machine learning, reinforcement learning, or neural networks to train the robot to recognize patterns, optimize parameters, or generate new behaviors. The robot can use different sources of data, such as sensor inputs, human feedback, or online resources, to update its models and policies.
In my experience, I have seen that having a Digital Twin of the Physical Robot and testing it within simulated environment before the actual roll out, helps to avoid the unplanned downtime and can make the Robot working more predictable in the actual environment. Digital twins are of crucial importance for industrial automation. Digital twins describe the use of holistic simulations to virtually mirror a physical system. The reinforcement learning/ Deep Q learning models can be trained to develop the most optimal policy in a given environment and state. Simulated environment can generate more adverse scenarios to test the virtualized version of the Robot and develop an optimal policy before it goes into the actual environment.
Simulations: Simulations are valuable for testing and refining navigation algorithms in a virtual environment before deploying them on physical robots. Redundancy and Fault Tolerance: Building redundancy and fault-tolerant features into the navigation system ensures that the robot can adapt to unexpected situations or equipment failures. The integration of these components enables robots to navigate through a wide range of environments, from structured indoor spaces to unstructured outdoor terrains. The specific programming techniques and algorithms chosen depend on the characteristics of the environment, the type of robot, and the desired level of autonomy.
The fifth step is to allow the robot to communicate and collaborate with other robots or humans in the environment. This can enhance the robot's capabilities, efficiency, and safety in complex environments. Communication and collaboration algorithms use techniques such as wireless protocols, data fusion, or game theory to exchange information, coordinate actions, or negotiate outcomes. The robot can use different modes of communication, such as speech, gestures, or signals, to interact with other agents.
The sixth step is to evaluate and test the robot's navigation system in different scenarios and conditions. This can help to identify and correct errors, bugs, or limitations in the system, and to verify and validate its functionality, reliability, and performance. Evaluation and testing methods use techniques such as simulation, benchmarking, or user studies to measure and compare the robot's behavior, results, or impact. The robot can use different metrics, such as accuracy, efficiency, or satisfaction, to assess its navigation system.